Abstract

Abstract
Whispers are common and essential for secondary communication. Nonetheless, individuals with aphonia, including laryngectomees, rely on whispers as their primary means of communication. Due to the distinct features between whispered and regular speech, debates have emerged in the field of speech recognition, highlighting the challenge of effectively converting between them. This study investigates the characteristics of whispered speech and proposes a system for converting whispered vowels into normal ones. The system is developed using multilayer perceptron networks and two types of generative adversarial networks. Three metrics are analyzed to evaluate the performance of the system: mel-cepstral distortion, root mean square error of the fundamental frequency, and accuracy with f1-score of a vowel classifier. Overall, the perceptron networks demonstrated better results, with no significant differences observed between male and female voices or the presence/absence of speech silence, except for improved accuracy in estimating the fundamental frequency during the conversion process. © 2025, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Integrated Access and Backhaul (IAB) in cellular networks combines access and backhaul within a wireless infrastructure reducing reliance on fibre-based backhaul. This enables flexible and more cost-effective network expansion, especially in hard-to-reach areas. Positioning a mobile IAB node (MIAB) in a seaport environment, in order to ensure on-demand, resilient wireless connectivity, presents unique challenges due to the high density of User Equipments (UEs) and potential shadowing effects caused by obstacles. This paper addresses the problem of positioning MIABs within areas containing UEs, fixed IAB donors (FIABs), and obstacles. Our approach considers user associations and different types of scheduling, ensuring MIABs and FIABs meet the capacity requirements of a special team of served UEs, while not exceeding backhaul capacity. With a Genetic Algorithm solver, we achieve capacity improvement gains, by up to 200% for the 90th percentile, particularly during emergency capacity demands.

Abstract
High-frequency radio networks, including those operating in the millimeter-wave bands, are sensible to Line-of-Sight (LoS) obstructions. Computer Vision (CV) algorithms can be leveraged to improve network performance by processing and interpreting visual data, enabling obstacle avoidance and ensuring LoS signal propagation. We propose a vision-aided Radio Access Network (RAN) based on the O-RAN architecture and capable of perceiving the surrounding environment. The vision-aided RAN consists of a gNodeB (gNB) equipped with a video camera that employs CV techniques to extract critical environmental information. An xApp is used to collect and process metrics from the RAN and receive data from a Vision Module (VM). This enhances the RAN's ability to perceive its surroundings, leading to better connectivity in challenging environments. © 2025 IFIP.

Abstract

Abstract