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.

Designing a Low-Cost, Single-Supply ECG System for Suppression of Movement Artifact from Contaminated Magnetocardiogram.

Measurement of the late potentials and His-bundle activity is crucial for many clinical studies using the noncontact and noninvasive magnetocardiography (MCG) technique; these weak signals are extracted by averaging many cardiac cycles aligned using the R-peak of the cardiac cycle identified using an electrocardiography (ECG) lead. ECG is measured simultaneously with MCG using a conventional dual-supply ECG amplifier, which requires either two separate batteries or a single battery with a switching voltage inverter circuit for its proper operation. The ECG circuitry based on two separate batteries requires a relatively large voltage supply (-18 to +18 V). The single-supply (low voltage: 0-9 V) ECG circuitry may be implemented using a switching voltage inverter; however, this mode of operation introduces switching noise in the system. The objective of the present work is to overcome these problems by carefully designing a low-voltage, single-supply ECG system, which can be used simultaneously with the MCG setup without introducing a significant level of additional noise in the MCG measurement system.

Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach.

Electroencephalography (EEG) is a non-invasive way of recording brain activities, making it useful for diagnosing various neurological disorders. However, artifact signals associated with eye blinks or the heart spread across the scalp, contaminating EEG recordings and making EEG data analysis difficult. To solve this problem, we implement a common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording. In the proposed methodology, the clean EOG and EKG signals are extracted by subjecting the raw reference time-series data to ensemble empirical mode decomposition to obtain the intrinsic mode functions. Then, an unsupervised technique is used to capture the artifact components. We compare the distortion introduced into the brain signal after artifact suppression using the proposed method with those obtained using conventional regression alone and with a wavelet-based approach. The results show that the proposed method outperforms the other techniques, with an additional advantage of being a common methodology for the suppression of two types of artifact.

Baseline drift removal and denoising of MCG data using EEMD: role of noise amplitude and the thresholding effect.

We adopt the Ensemble Empirical Mode Decomposition (EEMD) method, with an appropriate thresholding on the Intrinsic Mode Functions (IMFs), to denoise the magnetocardiography (MCG) signal. To this end, we discuss the two associated problems that relate to: (i) the amplitude of noise added to the observed signal in the EEMD method with a view to prevent mode mixing and (ii) the effect of direct thresholding that causes discontinuities in the reconstructed denoised signal. We then denoise the MCG signals, having various signal-to-noise ratios, by using this method and compare the results with those obtained by the standard wavelet based denoising method. We also address the problem of eliminating the high frequency baseline drift such as the sudden and discontinuous changes in the baseline of the experimentally measured MCG signal using the EEMD based method. We show that the EEMD method used for denoising and the elimination of baseline drift is superior in performance to other standard methods such as wavelet based techniques and Independent Component Analysis (ICA).

Programmable System-on-Chip (PSoC) Embedded Readout Designs for Liquid Helium Level Sensors.

This article reports the development of programmable system-on-chip (PSoC)-based embedded readout designs for liquid helium level sensors using resistive liquid vapor discriminators. The system has been built for the measurement of liquid helium level in a concave-bottomed, helmet-shaped, fiber-reinforced plastic cryostat for magnetoencephalography. This design incorporates three carbon resistors as cost-effective sensors, which are mounted at desired heights inside the cryostat and were used to infer the liquid helium level by measuring their temperature-dependent resistance. Localized electrical heating of the carbon resistors was used to discriminate whether the resistor is immersed in liquid helium or its vapor by exploiting the difference in the heat transfer rates in the two environments. This report describes a single PSoC chip for the design and development of a constant current source to drive the three carbon resistors, a multiplexer to route the sensor outputs to the analog-to-digital converter (ADC), a buffer to avoid loading of the sensors, an ADC for digitizing the data, and a display using liquid crystal display cum light-emitting diode modules. The level sensor readout designed with a single PSoC chip enables cost-effective and reliable measurement system design.