Arduino-Based Novel Hardware Design for Liquid Helium Level Measurement.

Liquid helium (LHe) is used as a cryogen in a variety of applications involving superconductivity and is routinely monitored for conducting low-temperature experiments. Thermoacoustic oscillations, which are inevitably present inside closed LHe containers, are utilized for level detection by sensing the vibrations at the warm end of a thin capillary tube inserted into the Dewar. The position of the capillary tube at which a sudden change occurs in these oscillations is manually sensed to identify the liquid level. The present work proposes a novel hardware design to identify the thermoacoustic oscillations in a reliable way using an accelerometer driven by an Arduino microcontroller. Further, an automated approach has been devised to quantify the rate of change of these helium oscillations to measure the LHe level. The proposed method has been tested during several trials on a 120 L and 100 L capacity Dewar using the proposed hardware, and the mean error in measuring the LHe level was calculated to be less than 1 cm in comparison with the gold standard niobium-titanium level sensor. The results encourage the use of the proposed method to evolve as a cost-effective alternative to the widely used superconducting level sensors in measuring LHe level.

Design and Implementation of a Discrete-Time Proportional Integral (PI) Controller for the Temperature Control of a Heating Pad.

Contact heat evoked potentials (CHEPs) are recorded from the brain by giving thermal stimulations through heating pads kept on the surface of the skin. CHEP signals have crucial diagnostic implications in human pain activation studies. This work proposes a novel design of a digital proportional integral (PI) controller based on Arduino microcontroller with a view to explore the suitability of an electric heating pad for use as a thermode in a custom-made, cost-effective CHEP stimulator. The purpose of PI controller is to set, regulate, and deliver desired temperatures on the surface of the heating pad in a user-defined pattern. The transfer function of the heating system has been deduced using the parametric system identification method, and the design parameters of the controller have been identified using the root locus technique. The efficiency of the proposed PI controller in circumventing the well-known integrator windup problem (error in the integral term builds excessively, leading to large transients in the controller output) in tracking the reference input and the controller effort (CE) in rejecting output disturbances to maintain the set temperature of the heating pad have been found to be superior compared with the conventional PI controller and two of the existing anti-windup models.

A Combined Methodology to Eliminate Artifacts in Multichannel Electrogastrogram Based on Independent Component Analysis and Ensemble Empirical Mode Decomposition.

Cutaneous measurements of electrogastrogram (EGG) signals are heavily contaminated by artifacts due to cardiac activity, breathing, motion artifacts, and electrode drifts whose effective elimination remains an open problem. A common methodology is proposed by combining independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD) to denoise gastric slow-wave signals in multichannel EGG data. Sixteen electrodes are fixed over the upper abdomen to measure the EGG signals under three gastric conditions, namely, preprandial, postprandial immediately, and postprandial 2 h after food for three healthy subjects and a subject with a gastric disorder. Instantaneous frequencies of intrinsic mode functions that are obtained by applying the EEMD technique are analyzed to individually identify and remove each of the artifacts. A critical investigation on the proposed ICA-EEMD method reveals its ability to provide a higher attenuation of artifacts and lower distortion than those obtained by the ICA-EMD method and conventional techniques, like bandpass and adaptive filtering. Characteristic changes in the slow-wave frequencies across the three gastric conditions could be determined from the denoised signals for all the cases. The results therefore encourage the use of the EEMD-based technique for denoising gastric signals to be used in clinical practice.