3 research outputs found
COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers
Monitoring of prevalent airborne diseases such as COVID-19 characteristically
involves respiratory assessments. While auscultation is a mainstream method for
preliminary screening of disease symptoms, its utility is hampered by the need
for dedicated hospital visits. Remote monitoring based on recordings of
respiratory sounds on portable devices is a promising alternative, which can
assist in early assessment of COVID-19 that primarily affects the lower
respiratory tract. In this study, we introduce a novel deep learning approach
to distinguish patients with COVID-19 from healthy controls given audio
recordings of cough or breathing sounds. The proposed approach leverages a
novel hierarchical spectrogram transformer (HST) on spectrogram representations
of respiratory sounds. HST embodies self-attention mechanisms over local
windows in spectrograms, and window size is progressively grown over model
stages to capture local to global context. HST is compared against
state-of-the-art conventional and deep-learning baselines. Demonstrations on
crowd-sourced multi-national datasets indicate that HST outperforms competing
methods, achieving over 83% area under the receiver operating characteristic
curve (AUC) in detecting COVID-19 cases
Square root central difference-based FastSLAM approach improved by differential evolution
This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling's polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square root central difference Kalman filter-based FastSLAM, called SRCD-FastSLAM. In this method, autonomous vehicle (or robot) position, landmarks' position estimations, and importance weight calculations of the particle filter are provided by the SRCD-Kalman filter. The second approach is an improved version of the SRCD-FastSLAM in which particles are improved by a differential evolution (DE) algorithm for reducing the risk of the particle depletion problem. Simulation results are given as a comparison of FastSLAM II, unscented (U)-FastSLAM, SRCD-Kalman filter-aided FastSLAM, SRCD particle filter-based FastSLAM, SRCD-FastSLAM, and DE-SRCD-FastSLAM. The results show that SRCD-based FastSLAM approaches accurately compute mean and precise uncertainty of the robot position in comparison with FastSLAM II and U-FastSLAM methods. However, the best results are obtained by DE-SRCD-FastSLAM, which provides significantly more accurate and robust estimation with the help of DE with fewer particles. Moreover, consistency of the DE-SRCD-FastSLAM is more prolonged than that of FastSLAM II, U-FastSLAM, and SRCD-FastSLAM
Slot Parameter Optimization for Multiband Antenna Performance Improvement Using Intelligent Systems
This paper discusses bandwidth enhancement for multiband microstrip patch antennas (MMPAs) using symmetrical rectangular/square slots etched on the patch and the substrate properties. The slot parameters on MMPA are modeled using soft computing technique of artificial neural networks (ANN). To achieve the best ANN performance, Particle Swarm Optimization (PSO) and Differential Evolution (DE) are applied with ANN's conventional training algorithm in optimization of the modeling performance. In this study, the slot parameters are assumed as slot distance to the radiating patch edge, slot width, and length. Bandwidth enhancement is applied to a formerly designed MMPA fed by a microstrip transmission line attached to the center pin of 50 ohm SMA connecter. The simulated antennas are fabricated and measured. Measurement results are utilized for training the artificial intelligence models. The ANN provides 98% model accuracy for rectangular slots and 97% for square slots; however, ANFIS offer 90% accuracy with lack of resonance frequency tracking