Abstract
This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10m where two of the walls are equipped with RIS tiles. © 2023 IEEE.

Abstract
Semantic segmentation consists of classifying each pixel according to a set of classes. This process is particularly slow for high-resolution images, which are present in many applications, ranging from biomedicine to the automotive industry. In this work, we propose an algorithm targeted to segment high-resolution images based on two stages. During stage 1, a lower-resolution interpolation of the image is the input of a first neural network, whose low-resolution output is resized to the original resolution. Then, in stage 2, the probabilities resulting from stage 1 are divided into contiguous patches, with less confident ones being collected and refined by a second neural network. The main novelty of this algorithm is the aggregation of the low-resolution result from stage 1 with the high-resolution patches from stage 2. We propose the U-Net architecture segmentation, evaluated in six databases. Our method shows similar results to the baseline regarding the Dice coefficient, with fewer arithmetic operations.

Abstract

Abstract
Stress-ribbon footbridges are often prone to excessive vibrations induced by environmental phenomena (e.g., wind) and human actions (e.g., walking). This paper studies a stress-ribbon footbridge at the Faculty of Engineering of the University of Porto (FEUP) in Portugal, where different degrees of vertical vibrations are perceptible in response to human actions. We adopt sonification techniques to create a sonic manifestation that shows the footbridge’s dynamic response to human interaction. Two distinct sonification techniques – audification and parameter mapping – are adopted to provide intuitive access to the footbridge dynamics from low-level acceleration data and higher-level spectral analysis. In order to evaluate the proposed sonification techniques in exposing relevant information about human actions on the footbridge, an online perceptual test was conducted to assess the understanding of the three following dimensions: 1) the number of people interacting with the footbridge, 2) their walking speed, and 3) the steadiness of their pace. The online perceptual test was conducted with and without a short training phase. Results of n= 23 participants show that parameter mapping sonification is more effective in promoting an intuitive understating of the footbridge dynamics compared to audification. Furthermore, when exposed to a short training phase, the participants’ perception improved in identifying the correct dimensions. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Language is closely related to how we perceive ourselves and signify our reality. In this scope, we created Desiring Machines, an interactive media art project that allows the experience of affective virtual environments adopting speech emotion recognition as the leading input source. Participants can share their emotions by speaking, singing, reciting poetry, or making any vocal sounds to generate virtual environments on the run. Our contribution combines two machine learning models. We propose a long-short term memory and a convolutional neural network to predict four main emotional categories from high-level semantic and low-level paralinguistic acoustic features. Predicted emotions are mapped to audiovisual representations by an end-to-end process encoding emotion in virtual environments. We use a generative model of chord progressions to transfer speech emotion into music based on the tonal interval space. Also, we implement a generative adversarial network to synthesize an image from the transcribed speech-to-text. The generated visuals are used as the style image in the style-transfer process onto an equirectangular projection of a spherical panorama selected for each emotional category. The result is an immersive virtual space encapsulating emotions in spheres disposed into a 3D environment. Users can create new affective representations or interact with other previously encoded instances (This ArtsIT publication is an extended version of the earlier abstract presented at the ACM MM22 [1]). © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
This paper presents a clinical case study investigating the pattern of a saxophonist's embouchure as a possible origin of orofacial pain. The rehabilitation addressed the dental occlusion and a fracture in a metal ceramic bridge. To evaluate the undesirable loads on the upper teeth, two piezoresistive sensors were placed between the central incisors and the mouthpiece during the embouchure. A newly fixed metal ceramic prosthesis was placed from teeth 13 to 25, and two implants were placed in the premolar zone corresponding to teeth 14 and 15. After the oral rehabilitation, the embouchure force measurements showed that higher stability was promoted by the newly fixed metal-ceramic prosthesis. The musician executed a more symmetric loading of the central incisors (teeth 11 and 21). The functional demands of the saxophone player and consequent application of excessive pressure can significantly influence and modify the metal-ceramic position on the anterior zone teeth 21/22. The contribution of engineering (i.e., monitoring the applied forces on the musician's dental structures) was therefore crucial for the correct assessment and design of the treatment plan.