Abstract

Abstract
This paper addresses two challenges that are intertwined and are key in informing signal processing methods restoring natural (voiced) speech from whispered speech. The first challenge involves characterizing and modeling the evolution of the harmonic phase/magnitude structure of a sequence of individual pitch periods in a voiced region of natural speech comprising sustained or co-articulated vowels. A novel algorithm segmenting individual pitch pulses is proposed, which is then used to obtain illustrative results highlighting important differences between sustained and co-articulated vowels, and suggesting practical synthetic voicing approaches. The second challenge involves model-based synthetic voicing restoration in real-time and on-the-fly. Three implementation alternatives are described that differ in their signal reconstruction approaches: frequency-domain, combined frequency- and time-domain, and physiologically inspired filtering of glottal excitation pulses individually generated. The three alternatives are compared objectively using illustrative examples, and subjectively using the results of listening tests involving synthetic voicing of sustained and co-articulated vowels in word context. © 2025 Elsevier B.V., All rights reserved.

Abstract

In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being.

Abstract
Integrating physical robots in an educational context often entails acquiring expensive equipment that often operates using proprietary software. Both conditions restrict the students from exploring and fully understanding the internal operation of robots. In response to these limitations, a three-degree-of-freedom robotic manipulator, based on the “EEZYbotARM MK2” open-source design by Carlo Franciscone, is being repurposed and integrated within the SimTwo simulation environment to operate within a hardware-in-the-loop architecture. To accomplish this objective, first, an open-source Arduino-based library was developed aiming at the robot’s online and offline programming akin to industrial robots. The firmware is able to communicate with the SimTwo software in which the digital twin’s robot is living. The dynamic behavior of the robot’s digital twin must be properly parametrized and aligned with the physical robot’s dynamics. This article describes the modeling of the robot joint’s actuator and its closed-loop controller formulation. The obtained results show that the dynamic behavior of the robot joint digital twin closely matches both open and closed-loop, the one of its physical counterpart.

Abstract
Optical sensing exploiting plasmonics and other types of surface waves provides exceptional performance for chemical and biological detection due to its high sensitivity and real-time capabilities. This study explores the integration of thin films with plasmonic, specifically leveraging metallic and dielectric nano structures, fabricated through sputtering and colloidal synthesis techniques. Advanced surface wave excitations such as localized surface plasmon resonances (SPR), Tamm Plasmon Polaritons (TPP), Bloch surface waves, and surface plasmon polaritons (SPP) are used to amplify sensor performance. Simulations and experimental data show that these nanostructured coatings significantly enhance electromagnetic field confinement, leading to improved detection limits and sensor robustness, showcasing promising applications in environmental monitoring, gas detection, and biomedical diagnostics.

Abstract
Battery degradation remains a major challenge in electric vehicle (EV) adoption, directly affecting long-term performance, cost, and user satisfaction. This paper proposes a data-driven charging strategy that reduces battery wear while meeting the user's daily range needs. By integrating manufacturer guidelines, battery aging models, and thermal dynamics, the proposed optimization algorithm dynamically adjusts the charging current and timing to minimize stressors, such as high temperatures and prolonged high state of charge (SoC). The methodology is responsive to user inputs such as departure time and required driving range, enabling personalized charging behavior. Simulation results show that this approach can reduce battery degradation by up to 2.7% over a 30-day period compared to conventional charging habits, without compromising usability. The framework is designed for integration into Battery Management Systems (BMS), with applications for both private EV users and fleet operators. We address EV battery aging driven by high core temperature and prolonged high state of charge (SoC) during overnight/home charging. Given a user-specified departure time and required driving range, we schedule charging power over time to minimize predicted degradation exposure while still meeting the range requirement. The scheduler optimizes charging timing/current under SoC dynamics, thermal constraints, and charger/ BMS limits.