Affective computing has recently gained popularity, especially in the field of human-computer interaction systems, where effectively evoking and detecting emotions is of paramount importance to enhance users’ experience. However, several issues are hindering progress in the field. In fact, the complexity of emotions makes it difficult to understand their triggers and control their elicitation. Additionally, effective emotion recognition requires analyzing multiple sensor data, such as facial expressions and physiological signals. These factors combined make it hard to collect high-quality datasets that can be used for research purposes (e.g., development of emotion recognition algorithms). Despite these challenges, Virtual Reality (VR) holds promise as a solution. By providing a controlled and immersive environment, VR enables the replication of real-world emotional experiences and facilitates the tracking of signals indicative of emotional states. However, controlling emotion elicitation remains a challenging task also within VR. This research paper introduces the Magic Xroom, a VR platform designed to enhance control over emotion elicitation by leveraging the theory of flow. This theory establishes a mapping between an individual’s skill levels, task difficulty, and perceived emotions. In the Magic Xroom, the user’s skill level is continuously assessed, and task difficulty is adjusted accordingly to evoke specific emotions. Furthermore, user signals are collected using sensors, and virtual panels are utilized to determine the ground truth emotional states, making the Magic Xroom an ideal platform for collecting extensive datasets. The paper provides detailed implementation information, highlights the main properties of the Magic Xroom, and presents examples of virtual scenarios to illustrate its abilities and capabilities.

The V-Cockpit platform aims to transform the design, testing, and validation of car infotainment systems from the physical realm to virtual reality. It uniquely integrates various aspects of the creative phases—from conceptualization to evaluation—streamlining the process and reducing time and costs compared to traditional methods that focus on individual activities and rely heavily on physical prototyping. This technical note provides a comprehensive overview of the main platform’s aspects, highlighting the integration of hardware and behavioral analysis algorithms to improve user experience and detect potential design flaws early on. The V-Cockpit platform, composed of six key components, leverages virtual reality and digital twins, promising significant cost savings and enhanced design efficiency. This work details the system architecture, implementation, and first benefits of this innovative approach through the analysis of three use cases.

Affective Computing aims to build systems that can sense, interpret, and influence human emotions. In parallel, virtual reality (VR) has matured into a powerful solution for immersion, presence, and interaction. When combined, these two technologies enable a new generation of emotionally adaptive systems that can recognize users’ inner states and react in real time. In this article, we explore the opportunities and challenges of emotion recognition and emotion elicitation in VR, focusing on the Virtual Emotion, Elicitation and Recognition Loop, a closed-loop framework where virtual environments adapt based on recognized and targeted emotions. We discuss applications in manufacturing, product design, and education, and highlight key ethical and privacy concerns, especially in light of recent regulations such as the European Commission released the proposed Regulation on Artificial Intelligence (EU AI Act). Finally, we outline open research directions toward responsible, privacy-aware, and scalable affective VR systems, suggesting that the convergence between VR and Affective Computing can become a crucial foundation for the next generation of human-centric interactive technologies.

This article explores the potential of the emerging image compression standard AV1 Image File Format (AVIF) as a format for storing 2D texture data in 3D computer graphics, aiming to assess its suitability for graphics applications. It presents a comparative performance evaluation, focusing on image quality, compression efficiency, and processing times, by comparing AVIF with the traditional format JPEG and the texture compression schemes BPTC and S3TC. To conduct the evaluation, a selected set of test images is compressed into the specified formats, loaded as textures, and assessed in a mockup 3D application to evaluate their visual performance in a realistic rendering context. The results show that AVIF delivers better fidelity to the original image compared to JPEG, BPTC, and S3TC, while also yielding a smaller file size. It outperforms JPEG by 9.2 dB in visual quality and by 174.4% in compression ratio, on average. However, this comes at the cost of longer processing times, with AVIF taking 126 times longer than JPEG and 185 times longer than S3TC to encode an image. AVIF also showed a 536% increase in decoding time compared to JPEG. BPTC produced high-fidelity images, second only to AVIF, but it required longer encoding times, depending on the quality settings. However, unlike AVIF, it offers GPU optimization benefits.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.