Gianluca Setti

Soumia Siyoucef, Najmeddine Dhieb, Hakim Ghazzai et al.

Rehabilitation aims to help patients with limited mobility regain their physical abilities through targeted movements, exercises, stimulation, and other therapeutic methods. Recent advances in technology have introduced sensor-based systems into rehabilitation and clinical practices, enabling real-time monitoring and providing accurate feedback on movement accuracy. Among these sensors, LiDAR has demonstrated strong potential, offering key advantages over conventional techniques such as camera-based systems, which raise privacy concerns, and wearable sensors, which can be uncomfortable and prone to errors. In this work, we review the applications of LiDAR in rehabilitation, post-injury care, and hospital environments, focusing on studies published between 2019 and 2025. Studies across several areas have been explored: 3D body scanning and gait analysis with standalone LiDAR, LiDAR mounted on robotic systems for rehabilitation, real-time monitoring and environment scanning for safe navigation, and activity and position recognition. We also analyze processing techniques, particularly learning-based approaches, and support the discussion with statistical analysis, highlighting trends, gaps, and future research opportunities. To the best of our knowledge, this is the first comprehensive survey dedicated to LiDAR for rehabilitation applications, providing an overview of current methods, AI-based processing techniques, and open challenges.

Abdullah Khanfor, Chaima Zaghouani, Hakim Ghazzai et al.

Light Detection and Ranging (LiDAR) technology offers significant advantages for effective crowd management. This article presents LiDAR technology and highlights its primary advantages over other monitoring technologies, including enhanced privacy, performance in various weather conditions, and precise 3D mapping. We present a general taxonomy of four key tasks in crowd management: crowd detection, counting, tracking, and behavior classification, with illustrative examples of LiDAR applications for each task. We identify challenges and open research directions, including the scarcity of dedicated datasets, sensor fusion requirements, artificial intelligence integration, and processing needs for LiDAR point clouds. This article offers actionable insights for developing crowd management solutions tailored to public safety applications.

Lorenzo Capelli, Leandro de Souza Rosa, Gianluca Setti et al.

The recent explosive growth in Deep Neural Networks applications raises concerns about the black-box usage of such models, with limited trasparency and trustworthiness in high-stakes domains, which have been crystallized as regulatory requirements such as the European Union Artificial Intelligence Act. While models with embedded confidence metrics have been proposed, such approaches cannot be applied to already existing models without retraining, limiting their broad application. On the other hand, post-hoc methods, which evaluate pre-trained models, focus on solving problems related to improving the confidence in the model's predictions, and detecting Out-Of-Distribution or Adversarial Attacks samples as independent applications. To tackle the limited applicability of already existing methods, we introduce Multi-Layer Analysis for Confidence Scoring (MACS), a unified post-hoc framework that analyzes intermediate activations to produce classification-maps. From the classification-maps, we derive a score applicable for confidence estimation, detecting distributional shifts and adversarial attacks, unifying the three problems in a common framework, and achieving performances that surpass the state-of-the-art approaches in our experiments with the VGG16 and ViTb16 models with a fraction of their computational overhead.

Yuetan Chu, Gongning Luo, Longxi Zhou et al.

Pulmonary artery-vein segmentation is crucial for disease diagnosis and surgical planning and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However, concerns regarding adverse health effects from contrast agents used in CTPA have constrained its clinical utility. In contrast, identifying arteries and veins using non-contrast CT, a conventional and low-cost clinical examination routine, has long been considered impossible. Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions. HiPaS first performs spatial normalization on raw CT volumes via a super-resolution module, and then iteratively achieves segmentation results at different branch levels by utilizing the lower-level vessel segmentation as a prior for higher-level vessel segmentation. We trained and validated HiPaS on our established multi-centric dataset comprising 1,073 CT volumes with meticulous manual annotations. Both quantitative experiments and clinical evaluation demonstrated the superior performance of HiPaS, achieving an average dice score of 91.8% and a sensitivity of 98.0%. Further experiments showed the non-inferiority of HiPaS segmentation on non-contrast CT compared to segmentation on CTPA. Employing HiPaS, we have conducted an anatomical study of pulmonary vasculature on 11,784 participants in China (six sites), discovering a new association of pulmonary vessel anatomy with sex, age, and disease states: vessel abundance suggests a significantly higher association with females than males with slightly decreasing with age, and is also influenced by certain diseases, under the controlling of lung volumes.

Lorenzo Nikiforos, Charalampos Antoniadis, Luciano Prono et al.

The increasing scale of deep neural networks has led to a growing need for compression techniques such as pruning, quantization, and low-rank decomposition. While these methods are very effective in reducing memory, computation and energy consumption, they often introduce severe accuracy degradation when applied directly. We introduce Vanishing Contributions (VCON), a general approach for smoothly transitioning neural models into compressed form. Rather than replacing the original network directly with its compressed version, VCON executes the two in parallel during fine-tuning. The contribution of the original (uncompressed) model is progressively reduced, while that of the compressed model is gradually increased. This smooth transition allows the network to adapt over time, improving stability and mitigating accuracy degradation. We evaluate VCON across computer vision and natural language processing benchmarks, in combination with multiple compression strategies. Across all scenarios, VCON leads to consistent improvements: typical gains exceed 3%, while some configuration exhibits accuracy boosts of 20%. VCON thus provides a generalizable method that can be applied to the existing compression techniques, with evidence of consistent gains across multiple benchmarks.

Andriy Enttsel, Alex Marchioni, Andrea Zanellini et al.

Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly detection. However, compression can potentially impair the detector's ability to distinguish between regular and irregular patterns due to information loss. Here we extend the information-theoretic framework introduced in [1] to simultaneously address the trade-off between the three features on which the effectiveness of the system depends: the effectiveness of compression, the amount of distortion it introduces, and the distinguishability between compressed normal signals and compressed anomalous signals. We leverage a Gaussian assumption to draw curves showing how moving on a Pareto surface helps administer such a trade-off better than simply relying on optimal rate-distortion compression and hoping that compressed signals can be distinguished from each other.

Spyros Barbakos, Charalampos Antoniadis, Gerasimos Potamianos et al.

Video consumption is a key part of daily life, but watching entire videos can be tedious. To address this, researchers have explored video summarization and highlight detection to identify key video segments. While some works combine video frames and transcripts, and others tackle video summarization and highlight detection using Reinforcement Learning (RL), no existing work, to the best of our knowledge, integrates both modalities within an RL framework. In this paper, we propose a multimodal pipeline that leverages video frames and their corresponding transcripts to generate a more condensed version of the video and detect highlights using a modality fusion mechanism. The pipeline is trained within an RL framework, which rewards the model for generating diverse and representative summaries while ensuring the inclusion of video segments with meaningful transcript content. The unsupervised nature of the training allows for learning from large-scale unannotated datasets, overcoming the challenge posed by the limited size of existing annotated datasets. Our experiments show that using the transcript in video summarization and highlight detection achieves superior results compared to relying solely on the visual content of the video.

Yong Wang, Zhuo Liu, Leo Yu Zhang et al.

Applying chaos theory for secure digital communications is promising and it is well acknowledged that in such applications the underlying chaotic systems should be carefully chosen. However, the requirements imposed on the chaotic systems are usually heuristic, without theoretic guarantee for the resultant communication scheme. Among all the primitives for secure communications, it is well-accepted that (pseudo) random numbers are most essential. Taking the well-studied two-dimensional coupled map lattice (2D CML) as an example, this paper performs a theoretical study towards pseudo-random number generation with the 2D CML. In so doing, an analytical expression of the Lyapunov exponent (LE) spectrum of the 2D CML is first derived. Using the LEs, one can configure system parameters to ensure the 2D CML only exhibits complex dynamic behavior, and then collect pseudo-random numbers from the system orbits. Moreover, based on the observation that least significant bit distributes more evenly in the (pseudo) random distribution, an extraction algorithm E is developed with the property that, when applied to the orbits of the 2D CML, it can squeeze uniform bits. In implementation, if fixed-point arithmetic is used in binary format with a precision of $z$ bits after the radix point, E can ensure that the deviation of the squeezed bits is bounded by $2^{-z}$ . Further simulation results demonstrate that the new method not only guide the 2D CML model to exhibit complex dynamic behavior, but also generate uniformly distributed independent bits. In particular, the squeezed pseudo random bits can pass both NIST 800-22 and TestU01 test suites in various settings. This study thereby provides a theoretical basis for effectively applying the 2D CML to secure communications.

Leo Yu Zhang, Yuansheng Liu, Kwok-Wo Wong et al.

The need for fast and strong image cryptosystems motivates researchers to develop new techniques to apply traditional cryptographic primitives in order to exploit the intrinsic features of digital images. One of the most popular and mature technique is the use of complex ynamic phenomena, including chaotic orbits and quantum walks, to generate the required key stream. In this paper, under the assumption of plaintext attacks we investigate the security of a classic diffusion mechanism (and of its variants) used as the core cryptographic rimitive in some image cryptosystems based on the aforementioned complex dynamic phenomena. We have theoretically found that regardless of the key schedule process, the data complexity for recovering each element of the equivalent secret key from these diffusion mechanisms is only O(1). The proposed analysis is validated by means of numerical examples. Some additional cryptographic applications of our work are also discussed.

Mauro Mangia, Riccardo Rovatti, Gianluca Setti

Design of Random Modulation Pre-Integration systems based on the restricted-isometry property may be suboptimal when the energy of the signals to be acquired is not evenly distributed, i.e. when they are both sparse and localized. To counter this, we introduce an additional design criterion, that we call rakeness, accounting for the amount of energy that the measurements capture from the signal to be acquired. Hence, for localized signals a proper system tuning increases the rakeness as well as the average SNR of the samples used in its reconstruction. Yet, maximizing average SNR may go against the need of capturing all the components that are potentially non-zero in a sparse signal, i.e., against the restricted isometry requirement ensuring reconstructability. What we propose is to administer the trade-off between rakeness and restricted isometry in a statistical way by laying down an optimization problem. The solution of such an optimization problem is the statistic of the process generating the random waveforms onto which the signal is projected to obtain the measurements. The formal definition of such a problems is given as well as its solution for signals that are either localized in frequency or in more generic domain. Sample applications, to ECG signals and small images of printed letters and numbers, show that rakeness-based design leads to non-negligible improvements in both cases.