Le Van Tan | Searcharxiv

arXiv2025

Effect of linear and quadratic coupling on dynamical parameters of an optomechanical oscillator

Dynamics of icrocantilevers are of important interest in micro-mechanical systems for enhancing the functionality and applicable range of the cantilevers in vibration transducing and highly sensitive measurement. In this study, using the semi-classical Hamiltonian formalism, we study in detail the modification of the mechanical frequency and damping rate taking into account both the linear and quadratic coupling between the mechanical oscillator and the laser field in an opto-mechanical system. It has been seen that, the linear coupling greatly enhances the modification of the effective mechanical frequency and the effective damping rate while the quadratic coupling reduces these quantities. For a MHz-frequency oscillator, the damping rate could be 10^5 times increased and the frequency is several times modified. These results help clarifying the origin of the modification of the susceptibility function for cooling of the mechanical mode

quant-ph↗

arXiv2019

Severity Detection Tool for Patients with Infectious Disease

Hand, foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low and middle income countries. Tetanus in particular has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. In this paper, we aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram (ECG) and photoplethysmogram (PPG) waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. A support vector machine is employed to classify the ANSD levels. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved in classifying ANSD levels. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability (HRV) analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low and middle income countries, and thereby improve overall patient care.

eess.SP↗