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Go to Editorial ManagerVarious methods have been exploited in the blind source separation problems, especially in cocktail party problems. The most commonly used method is the independent component analysis (ICA). Many linear and nonlinear ICA methods, such as the radial basis functions (RBF) and self-organizing map (SOM) methods utilise neural networks and genetic algorithms as optimisation methods. For the contrast function, most of the traditional methods, especially the neural networks, use the gradient descent as an objective function for the ICA method. Most of these methods trap in local minima and consume numerous computation requirements. Three metaheuristic optimisation methods, namely particle, quantum particle, and glowworm swarm optimisation methods are introduced in this study to enhance the existing ICA methods. The proposed methods exhibit better results in separation than those in the traditional methods according to the following separation quality measurements: signal-to-noise ratio, signal-to-interference ratio, log-likelihood ratio, perceptual evaluation speech quality and computation time. These methods effectively achieved an independent identical distribution condition when the sampling frequency of the signals is 8 kHz.
The advancement of pressure sensors customized for purposes marks notable progress, in healthcare diagnostics and patient supervision. This article delves into creating and assessing of a capacitive pressure sensor designed to measure physiological pressures with utmost accuracy and sensitivity. The sensor’s structure integrates materials compatible with the body to ensure safety and dependability when interacting with bodily tissues. Thorough simulations and validations showcase the sensors performance emphasizing its responsiveness across various pressures in medical settings. The assessment encompasses an analysis of the sensor’s sensitivity at (12.4 fF/mmHg) exceptional linearity within a nonlinearity range of ±0.015% with a small diaphragm diameter (0.5 mm) and long-term reliability. The results indicate that the suggested capacitive pressure sensor exhibits promising possibilities for use in fields like blood pressure monitoring, intracranial pressure measurement and other crucial areas of biomedicine, providing a nonintrusive and cost-efficient method, for real-time health monitoring and diagnostic purposes.