Laminar flowmeter for mechanical ventilator: Manufacturing challenge of Covid-19 pandemic.

The rapid and sudden attack of the covid-19 pandemic has emerged the urgent need for pulmonary resuscitation devices (ventilators). The airflow sensor is a main element in the ventilator. Sensing very low airflow rates is an essential requirement to meet the least significant bit of the analogue to digital converter included in the ventilator. This short communication describes the fabrication and test of five flow sensors using basic and the 3D printing techniques to overcome the severe challenge arising from the pandemic under strict quarantine. The principle of these five flow sensors is based on Fleisch pneumotachograph technology, which creates a pseudo-laminar flow within a bundle of capillary tubes. Amongst the five tested sensors, those fabricated by 3D printing technique were the most accurate and reliable. Results show that the 3D printed sensor of 33 trapezoidal capillary tubes and displaced pressure taps meet the requirement of sensing flowrates with less resistance to patient at exhalation and more linearity figure. The experimental data were correlated using a sophisticated MMF correlation with an R-squared factor of 0.9999 and a percentage error of 1.68%.

Fast Averaged Cyclic Periodogram method to compute spectral correlation and coherence.

Cyclostationary signal analysis is increasingly adopted in many fields such as communications, meteorology, vibration analysis, and others. Spectral correlation and spectral coherence are two powerful tools that can be applied to detect cyclostationarities in the signal even when the useful information is concealed by a strong random noise. In this paper, a fast method is proposed to calculate the Averaged Cyclic Periodogram (ACP), which is widely applied in many fields and due to its robustness as an estimator of spectral correlation and coherence. The method calculates shifted transforms for part of the cyclic frequency range and utilizes the frequency shifting property of Fourier transform to extract the spectral correlation/coherence for the entire frequency range. The proposed method is proved to save considerable time in comparison to the normal ACP method and the fast spectral correlation method which was proposed by some scholars. The proposed method does not require large memory, making it suitable for low-memory platforms. The application examples using simulated and actual vibration signals have demonstrated the accuracy of the method and its applicability to analyze cyclostationary signals where the cyclic event interval is constant and also cyclo-non-stationary signals where the cyclic event interval is variable.

Data compression in wireless sensors network using MDCT and embedded harmonic coding.

One of the major applications of wireless sensors networks (WSNs) is vibration measurement for the purpose of structural health monitoring and machinery fault diagnosis. WSNs have many advantages over the wired networks such as low cost and reduced setup time. However, the useful bandwidth is limited, as compared to wired networks, resulting in relatively low sampling. One solution to this problem is data compression which, in addition to enhancing sampling rate, saves valuable power of the wireless nodes. In this work, a data compression scheme, based on Modified Discrete Cosine Transform (MDCT) followed by Embedded Harmonic Components Coding (EHCC) is proposed to compress vibration signals. The EHCC is applied to exploit harmonic redundancy present is most vibration signals resulting in improved compression ratio. This scheme is made suitable for the tiny hardware of wireless nodes and it is proved to be fast and effective. The efficiency of the proposed scheme is investigated by conducting several experimental tests.