• The role of each imaging modality is evaluated in the identification and segmentation of the lesions. • Changes in the importance of imaging modalities are evaluated for different segmentation tasks. • The impact and challenges of using AI-generated data instead of real counterpart scans are evaluated to check how the network can explain potential differences. Brain studies require the use of several complementary imaging modalities. When some modality is unavailable, Artificial Intelligence (AI) has recently provided ways to estimate them. Radiologists modulate the use of the available modalities depending on the task they have to perform. We aim to trace artificially the radiological process through a multibranch neural network architecture, the StarNet. The goal is to explain how and where different imaging modalities, either really collected or artificially reconstructed, are used in different radiological tasks by reading inside the structure of the network. To do that, StarNet includes several satellite networks, one per source modality, connected at each layer by a central unit. This design enables us to assess the contribution of each imaging modality, identifying where the contribution occurs, and to quantify the variations if certain modalities are substituted with AI-generated counterparts. The ultimate goal is to enable data-related and task-related ablation studies through the complete explainability of StarNet, thus offering radiologists clear guidance on which imaging sequences contribute to the task, to what extent, and at which stages of the process. As an example, we applied the proposed architecture to the 2D slices extracted from 3D volumes acquired with multimodal magnetic resonance imaging (MRI), to assess: 1. The role of the used imaging modalities; 2. The change in role when the radiological task changes; 3. The effects of synthetic data on the process. The results are presented and discussed.

Despite several automated strategies for identification/segmentation of Multiple Sclerosis (MS) lesions in Magnetic Resonance Imaging (MRI) being developed, they consistently fall short when compared to the performance of human experts. This emphasizes the unique skills and expertise of human professionals in dealing with the uncertainty resulting from the vagueness and variability of MS, the lack of specificity of MRI concerning MS, and the inherent instabilities of MRI. Physicians manage this uncertainty in part by relying on their radiological, clinical, and anatomical experience. We have developed an automated framework for identifying and segmenting MS lesions in MRI scans by introducing a novel approach to replicating human diagnosis, a significant advancement in the field. This framework has the potential to revolutionize the way MS lesions are identified and segmented, being based on three main concepts: (1) Modeling the uncertainty; (2) Use of separately trained Convolutional Neural Networks (CNNs) optimized for detecting lesions, also considering their context in the brain, and to ensure spatial continuity; (3) Implementing an ensemble classifier to combine information from these CNNs. The proposed framework has been trained, validated, and tested on a single MRI modality, the FLuid-Attenuated Inversion Recovery (FLAIR) of the MSSEG benchmark public data set containing annotated data from seven expert radiologists and one ground truth. The comparison with the ground truth and each of the seven human raters demonstrates that it operates similarly to human raters. At the same time, the proposed model demonstrates more stability, effectiveness and robustness to biases than any other state-of-the-art model though using just the FLAIR modality.

Autonomous Driving (AD) entails vehicles that can perceive their surroundings and navigate without human intervention. This involves utilising a combination of sensors and algorithms to recognise obstacles, interpret traffic signals, and make driving decisions. While AD holds promise for transforming transportation by enhancing safety, reducing congestion, minimising pollution, and optimising efficiency, it poses technical challenges also. This work extends a novel approach to building an autonomous vehicle agent using Deep Reinforcement Learning (DRL) with Proximal Policy Optimisation (PPO) to navigate urban environments simulated by the CAR Learning to Act (CARLA) Simulator. The agent aims to maintain lane integrity and avoid collisions, even in adverse weather conditions. The proposed architecture integrates a 180-degree environmental view and various multimodal data inputs (RGB, segmentation, and depth camera inputs), extensively tested through experimentation. Notably, the integration of segmentation and depth data results in a 13% reduction in the collision rate, with the proposed agent achieving a total reward of 2510. This approach demonstrates significant progress over the previous framework, showcasing improved obstacle detection and collision avoidance accuracy. Moreover, these findings contribute to ongoing autonomous vehicle research, offering insights into effective strategies for developing robust and dependable driving agents capable of navigating urban environments and interacting with road infrastructure, contributing to advancements in Augmented Intelligence of Things (AIoT)-enabled autonomous driving.

In the contemporary era, the global proliferation of Internet of Things (IoT) devices exceeds 15 billion, serving functions from wearables to smart grid monitoring. These devices frequently manage sensitive data, underscoring the need for secure and reliable IoT networks leveraging blockchain technology. A key innovation of this study is an approach to mitigate vulnerabilities that quantum computing poses to blockchain-based IoT systems, which existing cryptographic methods cannot effectively address. Quantum computers could exploit these weaknesses to compromise key-pair generation and extract private keys from transaction signatures. To overcome this, the research introduces an optimized implementation of the post-quantum digital signature algorithm Dilithium-5, ensuring blockchain security and quantum readiness. These transaction signatures are designed for low-power, cost-effective microcontrollers, such as the ESP32, making the solution accessible for a wide range of IoT devices. In addition, the study includes a case study involving a post-quantum safe portable device for measuring blood oxygen levels and heart rate, illustrating the practical benefits and effectiveness of the proposed solution in enhancing IoT security against quantum threats. The results demonstrate that the proposed approach ensures quantum-resistant security while maintaining performance efficiency, making it suitable for real-world IoT applications.

Abstract High-performance computing (HPC) is crucial for artificial intelligence (AI) and deep learning (DL) but faces challenges related to scalability, data transfer costs, and security risks. Federated Learning (FL) enables collaborative model training without centralized data aggregation. However, FL introduces vulnerabilities, as exchanged models can be intercepted and manipulated, necessitating robust cryptographic protection. With the advent of quantum computing, traditional security mechanisms are at risk, requiring the adoption of Post-Quantum Cryptographic (PQC) algorithms. This study benchmarks three PQC digital signature algorithms: Falcon, SPHINCS+, and ML-DSA. Their execution time, memory usage, and computational efficiency are evaluated in a simulated FL setting. To extend the analysis, different cryptographic hash functions (SHA3-256, SHA3-512, and BLAKE3) are analyzed to assess hashing efficiency under varying computational loads. Both centralized and decentralized FL scenarios are simulated, incorporating PQC-based digital signatures at each phase of the communication pipeline to ensure model integrity and authenticity. The results provide insights into the trade-offs between security and computational overhead, guiding the selection of scalable cryptographic solutions for FL. Falcon and ML-DSA demonstrate minimal impact on computational performance, making them strong candidates for securing FL environments. Future research directions include the direct signing of DL models to enhance security and the integration of widely used FL libraries for more realistic evaluations. These advancements could improve the practical deployment of post-quantum security solutions in FL, ensuring resilience against emerging quantum threats.

One of the studies of the active brain-computer interface (BCI) focuses on identifying movements from human neurophysiological signals to control external devices such as robotic arms. In the literature, EEG-based BCI is utilized to decode user’s information to perform action or fill the gap from the brain to the arms in the case of illness. The purpose of this work is to understand, among the scientific literature, what the best Deep Learning (DL) architecture is for motor execution (ME) classification. Data from 105 people from the Physionet dataset and 15 subjects from the Upper Limb dataset were used. EEGnetv4, Deep4Net, and EEGITnet were used to classify EEG signals under ME for real-time BCI. The best results were achieved from the EEGNET trained without Common Spatial Pattern transformation, for both datasets.

Digital Twin technology in healthcare offers personalized care through advanced analytics, real-time data, and virtual models. In this paper we propose the adoption of the Digital Twin approach as a means for modeling hands, both injured and healthy, in a Double Digital Twin (DDT) using the Virtual Glove. The VG acts as a supportive rehabilitation device, capable of collecting data and reconstructing models for both the impaired and healthy hands. This study emphasizes the calibration process, which adjusts the model of the healthy hand to match the injured hand. Additionally, the transformed healthy hand model is used to guide the task with the injured system, ensuring synchronization and repeatability of exercises. Furthermore, the framework associated with DDT facilitates the analysis and quantification of the mobility of the impaired hand in comparison to the healthy one. Finally, a preliminary experiment is presented. Index Terms—Virtual Glove, Tele-Medicine, Double Digital Twin, Hand Rehabiliation, Virtual Reality

Magnetic Resonance Imaging (MRI) stands as a noninvasive tool for diagnosing and monitoring various diseases. The flexibility of MRI configuration parameters allows for adaptable imaging sequences, and at the same time poses challenges in terms of reproducibility, as variability in imaging sequences leads to significant differences in image contrast. This is one of the major causes that compromise the reliability of deep learning methods. Since the majority of the literature is focused on documenting the effects of this issue rather than delving into its underlying causes, this work follows a different approach. A Siamese Neural Network (SNN) has been trained to identify the scanner that acquired the input image. Experimental results include the use of Euclidean Distance (ED) and machine learning algorithms trained and tested using the feature vectors generated with the SNN. The results have shown that the proposed method is capable of distinguishing the scanner used for the acquisition with high accuracy. For a comprehensive interpretation of the results, the feature vectors have been dimensionality reduced and visualized with a 3D plot. Finally, the proposed method is sensitive to MR image contrast variability and could be used to detect data-related inconsistencies and provide a mechanism to make users aware of potential issues.

We propose a new approach for Alzheimer's disease (AD) detection using diffusion tensor imaging, specifically fractional anisotropy (FA) images, based on a combination of unsupervised and supervised deep learning techniques. Our method involves training a 3D convolutional autoencoder to learn low-dimensional representations of FA images in an unsupervised manner and using the learned representations to pre-train a supervised 3D convolutional classifier to predict the presence or absence of AD. Unsupervised pre-training can improve the classifier's performance, especially when difficult-to-collect labeled data are limited. We evaluate our approach on the OASIS-3 dataset and demonstrate promising performance.

Emotion recognition is useful in several fields, starting from medical diagnosis to driving a Brain-Computer Interface (BCI) or helping people with disabilities. During the last decades, many researchers applied automatic strategies to identify emotional states based on data acquired by electroen-cephalography (EEG). However, the task is very hard and results have been often ambiguous. This work aims to perform brain connectivity studies of EEG data of four self-stimulated emotional classes (“relax”, “anger”, “happiness”, “sadness”) using a graph model of the Phase Lag Index (PLI), being PLI a measurement of connection insensitive to volume conduction effect. Qualitative results show that, for the analyzed emotions, connectivity analysis indicates some relevant differences both in the active brain regions and in the bandwidths involved in the activation. This method for connectome generation and analysis shows that useful information can be derived and used for contributing to disambiguating the problem of automatic emotion recognition.

Magnetic resonance imaging (MRI) is an effective imaging tool that, due to its non-invasiveness and multiple-parameter nature, is frequently used in medicine. In particular, the MRI's inherent flexibility deriving from the usage of multiple parameters allows to obtain images of variable contrast and quality. However, intrinsic MRI contrast variability often comes with drawbacks in terms of differences in different scanners, thus resulting in the impossibility of standardizing the image contrast. In particular, this variability could negatively affect the automatic analysis of Deep Learning (DL) methods, both in the training phase and in the test phase. In this work, we present several results on how images collected from different MRI scanners are handled by DL methods. To this end, we trained a Siamese network (SNN), based on the EfficientNet-B0 Convolutional Neural Network (EN-CNN), to learn how to recognize the scanner that has generated a given image. The output encoding features of the SNN have been projected into a 2D space with Uniform Manifold Approximation and Projection (UMAP) and have been discussed. Regarding the training phase, the UMAP projects show that the network is capable of separating MR images encoded features from different MRI scanners. Moreover, even if the MR images of different subjects are acquired with the same scanner, the results suggest that there are considerable differences in how the SNN encoded those features. The test phase confirmed that the SNN architecture is capable of recognizing images from different MRI scanners.

Telerehabilitation has many proven advantages to present for hand mobility restoration. To produce a way for the patient to benefit from it without losing the continuous support of the therapist that traditional rehabilitation offers, we built a complete system of controlled rehabilitation based on touchless hand tracking and developed the framework for its use from therapists and patients. In this paper, we present the devices it uses together with user-friendly applications developed keeping in mind the patients and therapists. Furthermore, we present the methods that can be used for analyzing hand tracking data in order to acquire information for joint-wise mobility improvement.

Hand tracking and gesture recognition are fundamental in a multitude of applications. Various sensors have been used for this purpose, however, all monocular vision systems face limitations caused by occlusions. Wearable equipment overcome said limitations, although deemed impractical in some cases. Using more than one sensor provides a way to overcome this problem, but necessitates more complicated designs. In this work, we aim to highlight contemporary methods used for hand tracking and gesture recognition by collecting publications of systems developed in the last decade, that employ contactless devices as RGB cameras, IR, and depth sensors, along with some preceding pillar works. Additionally, we briefly present common steps, techniques, and basic algorithms used during the process of developing modern hand tracking and gesture recognition systems and, finally, we derive the trend for the next future.

In medical imaging, images represent the quantification of the interaction between electromagnetic waves and our body and are represented in grey-scale. In addition, medical imaging often produces multimodal images. However, the analysis and interpretation of these images mostly occur in sequence or, as in the case of automatic tools, they are simply concatenated as independent sources of information. In both cases, color perception and color contrast are not exploited. Color perception and color contrast play a crucial role in human vision to recognize objects effectively and efficiently, and this can in principle extend to automatic systems. In this paper we show how color coding, particularly using color opponent models, can become an effective tool for preliminary color-based segmentation. Tests have been conducted on multimodal Magnetic Resonance Imaging (MRI) of the brain collected in a public database and the results obtained show the importance of color coding in medical imaging analysis.

Abstract Virtual Glove (VG) is a low-cost computer vision system that utilizes two orthogonal LEAP motion sensors to provide detailed 4D hand tracking in real–time. VG can find many applications in the field of human-system interaction, such as remote control of machines or tele-rehabilitation. An innovative and efficient data-integration strategy, based on the velocity calculation, for selecting data from one of the LEAPs at each time, is proposed for VG. The position of each joint of the hand model, when obscured to a LEAP, is guessed and tends to flicker. Since VG uses two LEAP sensors, two spatial representations are available each moment for each joint: the method consists of the selection of the one with the lower velocity at each time instant. Choosing the smoother trajectory leads to VG stabilization and precision optimization, reduces occlusions (parts of the hand or handling objects obscuring other hand parts) and/or, when both sensors are seeing the same joint, reduces the number of outliers produced by hardware instabilities. The strategy is experimentally evaluated, in terms of reduction of outliers with respect to a previously used data selection strategy on VG, and results are reported and discussed. In the future, an objective test set has to be imagined, designed, and realized, also with the help of an external precise positioning equipment, to allow also quantitative and objective evaluation of the gain in precision and, maybe, of the intrinsic limitations of the proposed strategy. Moreover, advanced Artificial Intelligence-based (AI-based) real-time data integration strategies, specific for VG, will be designed and tested on the resulting dataset.

Hand rehabilitation therapy is fundamental in the recovery process for patients suffering from post-stroke or post-surgery impairments.Traditional approaches require the presence of therapist during the sessions, involving high costs and subjective measurements of the patients' abilities and progresses.Recently, several alternative approaches have been proposed.Mechanical devices are often expensive, cumbersome and patient specific, while virtual devices are not subject to this limitations, but, especially if based on a single sensor, could suffer from occlusions.In this paper a novel multi-sensor approach, based on the simultaneous use of two LEAP motion controllers, is proposed.The hardware and software design is illustrated and the measurements error induced by the mutual infrared interference is discussed.Finally, a calibration procedure, a tracking model prototype based on the sensors turnover and preliminary experimental results are presented.