Abstract
Pedestrian safety is a pressing subject in urban areas. The disorderly sharing of streets and roads between pedestrians and vehicles leads to potentially serious accidents for pedestrians. This student project aims to tackle the issue by placing an interactive gaming device at traffic lights. SMASHY by Stempe Safety offers pedestrians an amusing and active way to discourage jaywalking. The multipurpose solution features a smashing game with buttons on one side and a screen displaying useful information on the other side. While the traffic light remains red for pedestrians, the module buttons light up and the players can start smashing the buttons as fast as possible, until the light turns green and consequently, the game ends. Ultimately, the modules are connected to an app where, if desired by the player, scores can be tracked and difficulty can vary based on user performance. Multiple modules can be placed around the city and the app will track player scores by location. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
Most human time is spent indoors, and due to the pandemic, monitoring indoor air quality (IAQ) has become more crucial. In this study, an IoT (Internet of Things) architecture is implemented to monitor IAQ parameters, including CO2 and particulate matter (PM). An ESP32-C6-based device is developed to measure sensor data and send them, using the MQTT protocol, to a remote InfluxDBv2 database instance, where the data are stored and visualized. The Python 3.11 scripting programming language is used to automate Flux queries to the database, allowing a more in-depth data interpretation. The implemented system allows to analyze two measured scenarios during sleep: one with the door slightly open and one with the door closed. Results indicate that sleeping with the door slightly open causes CO2 levels to ascend slowly and maintain lower concentrations compared to sleeping with the door closed, where CO2 levels ascend faster and the maximum recommended values are exceeded. This demonstrates the benefits of ventilation in maintaining IAQ. The developed system can be used for sensing in different environments, such as schools or offices, so an IAQ assessment can be made. Based on the generated data, predictive models can be designed to support decisions on intelligent natural ventilation systems, achieving an optimized, efficient, and ubiquitous solution to moderate the IAQ.

Abstract
In recent years, code-reuse attacks have been used to exploit software vulnerabilities and gain control of numerous software programs and embedded devices. Several measures have been put in place to prevent this type of attack, such as Control-Flow Integrity (CFI) systems, and some of these systems have already been integrated into hardware. Nevertheless, Function-Oriented Programming (FOP) attacks, a form of code-reuse that chains functions to carry out malicious actions, continue to persist. In this work, we present the first analysis of the implications and feasibility of FOP attacks on microcontrollers, focusing on ARM Cortex-M processors that support PACBTI, that is, a hardware feature designed for CFI system implementation. During this process, we identified multiple dispatch gadgets in two common Real-time Operating System (RTOS). Since these gadgets reside within core OS functionalities, they are inherently included in a broad range of embedded operating systems. Furthermore, we also present CortexMFopper - a tool specially built to identify FOP gadgets in embedded devices and to raise awareness of this technique.

Abstract
CoffeeMush is an innovative and sustainable project developed as part of the European Project Semester (EPS) at ISEP in 2024. This student project aims to tackle waste management environmental problems by turning coffee waste into mushrooms, a valuable food source. CoffeeMush consists of a smart device providing optimal conditions for mushroom cultivation, complemented by a user-friendly Android application for remote monitoring and control. The design was guided by ethical, sustainability, market and technical considerations. The paper describes the theoretical background of the project, the technical design, and the prototype development and testing. The results show the feasibility of CoffeeMush as a practical and environmentally friendly solution for urban mushroom cultivation, and its impact on sustainable food production and waste reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
The number of electric vehicles has been increasing significantly due to various factors, such as the higher prices of fossil fuels, concerns about the increasing pollution, and the resulting incentive to use energy from renewable sources. There are currently a few charging facilities, which are still quite scattered, and several are still experimental, requiring appropriate planning of this infrastructure in order to support the growing number of electric vehicles adequately. Thus, optimising the location of charging stations becomes a critical issue, which can be achieved through the application of mathematical models and data analysis tools. An example is genetic algorithms, which have demonstrated their versatility in solving complex optimisation problems, especially those involving multiple variables. This work presents a proposal for a more comprehensive genetic algorithm model that encompasses all variables from the perspectives of all entities involved. Its experimentation was conducted using real data, with the aim of finding the best combination of locations, minimising the total number of stations and maximising the coverage of the area under study. Thus, it is essential to carefully consider user preferences, accessibility, energy demand, and existing electrical infrastructure to ensure an effective and sustainable installation. The findings highlight the crucial role of these computing tools in addressing complex problems from various viewpoints, leading to solutions that cater to the needs of all parties involved. While not necessarily perfect, these solutions represent a balanced compromise across multiple dimensions of the problem.

Abstract
Autonomous Vehicles aim to understand their surrounding environment by detecting relevant objects in the scene, which can be performed using a combination of sensors. The accurate prediction of pedestrians is a particularly challenging task, since the existing algorithms have more difficulty detecting small objects. This work studies and addresses this often overlooked problem by proposing Multimodal PointPillars (M-PP), a fast and effective novel fusion architecture for 3D object detection. Inspired by both MVX-Net and PointPillars, image features from a 2D CNN-based feature map are fused with the 3D point cloud in an early fusion architecture. By changing the heavy 3D convolutions of MVX-Net to a set of convolutional layers in 2D space, along with combining LiDAR and image information at an early stage, M-PP considerably improves inference time over the baseline, running at 28.49 Hz. It achieves inference speeds suitable for real-world applications while keeping the high performance of multimodal approaches. Extensive experiments show that our proposed architecture outperforms both MVX-Net and PointPillars for the pedestrian class in the KITTI 3D object detection dataset, with 62.78% in $AP_{BEV}$ (moderate difficulty), while also outperforming MVX-Net in the nuScenes dataset. Moreover, experiments were conducted to measure the detection performance based on object distance. The performance of M-PP surpassed other methods in pedestrian detection at any distance, particularly for faraway objects (more than 30 meters). Qualitative analysis shows that M-PP visibly outperformed MVX-Net for pedestrians and cyclists, while simultaneously making accurate predictions of cars.