Multichannel Measurement Research Articles

Cross Validation of GOES-16 and NOAA Multi-Radar Multi-Sensor (MRMS) QPE over the Continental United States

The Geostationary Operational Environmental Satellite-R (GOES-R) series provides new opportunities for continuous observation of precipitation at large scales with a high resolution. An operational quantitative precipitation estimation (QPE) product has been produced based on multi-channel measurements from the Advanced Baseline Imager (ABI) aboard the GOES-16 (formerly known as GOES-R). This paper presents a comprehensive evaluation of this GOES-16 QPE product against a ground reference QPE product from the National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor (MRMS) system over the continental United States (CONUS) during the warm seasons of 2018 and 2019. For the first time, the accuracy of GOES-16 QPE product was quantified using the gauge-corrected MRMS (GC-MRMS) QPE product, and a number of evaluation metrics were applied to adequately resolve the associated errors. The results indicated that precipitation occurrence and intensity estimated by the GOES-16 QPE agreed with GC-MRMS fairly well over the eastern United States (e.g., the probability of detection was close to 1.0, and the Pearson’s correlation coefficient was 0.80 during September 2019), while the discrepancies were noticeable over the western United States (e.g., the Pearson’s correlation coefficient was 0.64 for the same month). The performance of GOES-16 QPE was downgraded over the western United States, in part due to the limitations of the GOES-16 rainfall retrieval algorithm over complex terrains, and in part because of the poor radar coverage analyzed by the MRMS system. In addition, it was found that the GOES-16 QPE product significantly overestimated rainfall induced by the mesoscale convective systems in the midwestern United States, which must be addressed in the future development of GOES satellite rainfall retrieval algorithms.

A Robust Multi-Channel EMG System for Lower Back and Abdominal Muscles Training

Abstract EMG is an established method to acquire the action potentials of contracted muscles. Although commercial EMG systems are available and it is one of the most researched biosignals, it has never become widely used in rehabilitation or fitness training monitoring. The reasons are technical challenges of wearable EMG systems regarding electrode placement, motion artefacts and the complex connectivity of multi-channel EMG measurements. We address this problem for the lower back and abdominal musculature, through a novel dry electrodes belt, multi-channel high density EMG circuitry and problem-specific signal processing. The subject can easily strap the dry electrodes belt around himself which provides 16 EMG channels. Interferences from the ECG and motion artefacts are reduced by a stationary wavelet decomposition. Afterwards, an inter-channel filter is applied to increase the robustness of the signals. Subject measurements during different kinds of typical abdominal and lower back training exercises were performed wearing the novel dry electrodes belt. The results show the possibility of robust EMG measurements from the lower back and abdominal muscles by utilizing the gathered redundancy, appropriately. The additional information obtained via the multi-channel EMG circuitry and spatial oversampling can be used to address current problems of EMG applications. It combines the advantages of robustness and the capability of using comfortable dry electrodes. Therefore, the proposed measurement method for acquiring spatial information about the muscle contractions from the lower trunk can be used for rehabilitation or fitness training monitoring.

A Bayesian approach to eliminate correlated noise using an independent reference-Application to supersonic jet noise extraction.

A Bayesian method to remove correlated noise from multi-channel measurements is introduced. It is based on Bayesian factor analysis coupled with prior but uncertain knowledge of the correlation structure of the noise. This technique is well suited to denoise cross-spectral matrices measured in the frame of aeroacoustic experiments when background noise measurements are available, because it allows separating the engine noise contribution from the turbulent boundary layer and uniform noise components that are all sensed by in-flow microphones. In-flight data measured on flush-mounted microphones on an aircraft fuselage are denoised using this method. It is shown that it has a significant benefit for studying the broadband shock-associated noise generated by the engines in realistic flight conditions.

Neuronal biomarkers of Parkinson's disease are present in healthy aging

The prevalence of Parkinson's disease (PD) increases with aging and both processes share similar cellular mechanisms and alterations in the dopaminergic system. Yet it remains to be investigated whether aging can also demonstrate electrophysiological neuronal signatures typically associated with PD. Previous work has shown that phase-amplitude coupling (PAC) between the phase of beta oscillations and the amplitude of gamma oscillations as well as beta bursts features can serve as electrophysiological biomarkers for PD. Here we hypothesize that these metrics are also present in apparently healthy elderly subjects. Using resting state multichannel EEG measurements, we show that PAC between beta oscillation and broadband gamma activity (50–150 Hz) is elevated in a group of elderly (59–77 years) compared to young volunteers (20–35 years) without PD. Importantly, the increase of PAC is statistically significant even after ruling out confounds relating to changes in spectral power and non-sinusoidal shape of beta oscillation. Moreover, a trend for a higher percentage of longer beta bursts (> 0.2 s) along with the increase in their incidence rate is also observed for elderly subjects. Using inverse modeling, we further show that elevated PAC and longer beta bursts are most pronounced in the sensorimotor areas. Moreover, we show that PAC and longer beta bursts might reflect distinct mechanisms, since their spatial patterns only partially overlap and the correlation between them is weak. Taken together, our findings provide novel evidence that electrophysiological biomarkers of PD may already occur in apparently healthy elderly subjects. We hypothesize that PAC and beta bursts characteristics in aging might reflect a pre-clinical state of PD and suggest their predictive value to be tested in prospective longitudinal studies.

A Multichannel Strain Measurement Technique for Nanomodified Smart Cement-Based Sensors in Reinforced Concrete Structures.

Nanomodified smart cement-based sensors are an emerging self-sensing technology for the structural health monitoring (SHM) of reinforced concrete (RC) structures. To date, several literature works demonstrated their strain-sensing capabilities, which make them suited for damage detection and localization. Despite the most recent technological improvements, a tailored measurement technique allowing feasible field implementations of smart cement-based sensors to concrete structures is still missing. In this regard, this paper proposes a multichannel measurement technique for retrieving strains from smart cement-based sensors embedded in RC structures using a distributed biphasic input. The experiments performed for its validation include the investigation on an RC beam with seven embedded sensors subjected to different types of static loading and a long-term monitoring application on an RC plate. Results demonstrate that the proposed technique is effective for retrieving time-stable simultaneous strain measurements from smart cement-based sensors, as well as for aiding the identification of the changes in their electrical outputs due to the influence of environmental effects variable over time. Accordingly, the proposed multichannel strain measurement technique represents a promising approach for performing feasible field implementations of smart cement-based sensors to concrete structures.

Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering.

This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind‐source separation methods, and helps enable in the wild EEG recordings to be performed.

Ранговый классификатор состояний природной среды

An approach to the multivariate classification of the states of natural-technical objects and systems is considered, based on the development of methods for dynamic detection of anomalies in information data flows. The approach is based on an estimate of the statistical discrepancy between the probability distributions of random variables over variably changeable time intervals, as well as an estimate of the probabilities of errors of the first and second kind. The structure of a multichannel software and measurement complex for detecting anomalous states of PTO and PTS is proposed, and the results of model calculations are presented. The use of the multivariate approach allows optimizing the processes of processing, analysis and integration of heterogeneous data, as well as increasing the sensitivity, reliability and efficiency of decisions.

Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes.

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.

A generalised eigenvalue reweighting covariance matrix estimation algorithm for airborne STAP radar in complex environment

To improve the space-time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target-free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non-homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi-channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.

Linear optical circuits characterization by means of thermal field correlation measurement

We present a novel technique for linear optical circuits characterization, based on thermal field correlation measurements. Unlike the other known methods for multichannel interferometers measurements, proposed technique is robust to the input and output phase fluctuations and does not require any single-photon sources and detectors. This method has been tested in a numerical experiment and has proven to be effective even in the presence of noise.

Multichannel gas electron multiplier based soft x-ray field-programmable gate array measurement system for W-Environment in Steady-state Tokamak (WEST): Hardware, installation, and first plasma acquisition.

The work describes a novel approach to the design of a fast, multichannel measurement system for plasma diagnostics [A. Wojenski et al., Fusion Eng. Des. 123, 727 (2016)]. Its main scope is to provide measurements of soft X-ray (SXR) emission during plasma phenomena at the W-Environment in Steady-state Tokamak (WEST), especially for monitoring and tracing tungsten impurities. This paper describes the vertical Gas Electron Multiplier (GEM) camera installed at the WEST [M. Chernyshova et al., J. Instrument. 10, P10022 (2015)]. The designed GEM detector readout board has more than 100 channels, resulting in high-performance requirements for the data acquisition and processing system. The novel system construction approach is that the unit works on the raw signals providing a high quality of the data, especially in the scope of pileup effect analysis. In the case of doubtful results, the source data can be easily reviewed offline. The data selection and transmission are done in Field-Programmable Gate Arrays (FPGAs) on the custom boards with the custom Peripheral Component Interconnect (PCI)-Express Gen2 switch that allows us to register signals from multiple FPGAs and then process the data by complex algorithms [G. Kasprowicz et al., J. Fusion Energy 38, 480 (2019)]. The firmware is replaceable and different working modes can be applied (some under verification): global trigger mode, high-speed data serialization, and extended signal registration. Low level optimized central processing unit software for data readout was also designed [P. Linczuk et al., J. Instrum. 14, C05001 (2019)]. The installation of the system is described due to complex system components' distribution. The first results of the successful acquisition of the plasma at the WEST are discussed. The corresponding SXR energy and topology spectra were computed. Those are the first technical measurements of the system to ensure verification of data quality.

Development of a multi-channel 320GHz interferometer for high density plasma measurement in Heliotron J.

We report the development of a new interferometer with two stable, high-power, 320GHz solid-state sources in Heliotron J. A heterodyne Michelson interferometer optical scheme is employed. Two solid-state oscillators are utilized as sources with a fixed frequency at 320GHz and frequency tunable of 312-324GHz. Quasi-optical techniques are used for beam transmission. The beam is elongated in the vertical direction with two off-axis parabolic mirrors and injected into the plasma as a sheet beam for the multi-channel measurement (>5 ch.). Passing through the plasma, the beam is reflected at a retroreflector-array installed at the vacuum chamber wall. The retroreflector-array is a bunch of retroreflector structures, which can suppress the beam refraction caused by plasma without much space inside a vacuum chamber unlike a single retroreflector and can facilitate the system design. The source, detectors, and the retroreflector-array are tested to evaluate their basic performance on a tabletop experiment.

Error Correction of Weak Current Measurement System Based on Wavelet Denoising and Generalized Regression Neural Network

Aiming at the problems that the weak current signal circuit is susceptible to noise interference and leakage current at the input terminal affects the measurement accuracy, a weak current measurement error correction scheme based on the combination of wavelet threshold denoising and generalized regression neural network (GRNN) was proposed. The scheme was applied to the error correction of multi-channel weak current measurement system based on the ADAS1134 chip: the wavelet threshold denoising was used to preprocess the original current data measured by the system and the current measurement value was corrected after the system measurement error correction model established with GRNN was constructed. Compared with the correction method based on least square method and back propagation neural network (BPNN), this method has many advantages such as high accuracy, anti-interference ability and strong generalization ability. The experimental results showed that RMSE=0.0911 nA, MAE=0.0354 nA, and MAPE=0.0078%, without increasing the complexity of the measurement circuit, which achieved the purpose of correcting the measurement error of the weak current measurement system.

Identifying suitable time periods for infrasound measurement system response estimation using across-array coherence

SUMMARY Microbarometers deployed to measure atmospheric infrasound are often connected to, or housed within, a wind noise reduction system (WNRS). At infrasound arrays of the International Monitoring System (IMS), being deployed as part of Comprehensive Nuclear-Test-Ban Treaty verification measures, the WNRS typically comprises an 18 m diameter pipe array. Over the past decade an in situ method has been developed to estimate the measurement system (sensor + WNRS) response characteristics, by comparing its recordings with those made on a colocated reference sensor with known response and no WNRS. The method relies upon the identification of time periods for which the reference sensor and measurement system are subject to the same input pressure field. It has proven difficult to reliably identify such time periods at frequencies $\lt 0.1\,$ Hz using recordings at a single location, resulting in a negative bias in estimated measurement system gain values (the ‘dip artefact’) in the 0.02–0.1 Hz passband. The IMS is deploying arrays of microbarometers, and we show that a measure of across-array coherence can be used to identify time periods associated with acoustic signal propagation. Amplitude response estimates, using 1 yr of data from four IMS arrays, indicate that the dip artefact can be removed by retaining for analysis only those time periods that exhibit high across-array coherence. Moreover, our analysis confirms the hypothesis that the dip artefact is associated with time periods during which wind-generated pressure fluctuations dominate, leading to partial suppression of noise with length scales less than the extent of the WNRS. At two arrays within continental forests accurate amplitude responses are estimated across the 0.02–4 Hz passband, as acoustic signals at all frequencies can be identified. At two oceanic island arrays, the low numbers of time windows with above-noise acoustic signal in the 0.02–0.1 Hz passband make reliable response estimation at these frequencies difficult or impossible. It is recommended that the methodology for estimating the response of an infrasound measurement system at an array should incorporate a multichannel coherence measure; data centres may already routinely compute such measures in their signal detection algorithms.

High-Throughput Chiral Molecule Determination Based on Multi-Channel Weak Measurement

A multi-channel high-throughput chiral molecule determination method based on weak value amplification (WVA) is proposed for the first time. This method can be used for high-throughput and high-precision determination of chiral molecule. Through the design of multi-channel weak measurement system, we realized the real-time, high-throughput, high-sensitivity and high-resolution detection of chiral molecules by selecting proper measurement states. In this work, we have realized a sensitivity of 11.8 nm/° and a resolution of 3.13 × 10-4° for measuring optical rotation. Moreover, the proposed method is successfully applied for detection of lactose content in milk samples. Furthermore, the system has the advantages of simple and robust real-time detection settings, strong stability, high measurement efficiency and good scalability. This method has important significance in clinical diagnosis, food safety and biological system.

Correction to: Estimation of Self Noise of Geomagnetic and Geoelectric Field Sensors by the Method of Multichannel Measurements

To the article “Estimation of Self Noise of Geomagnetic and Geoelectric Field Sensors by the Method of Multichannel Measurements,” by P. F. Astaf’ev and A. D. Konoplin, Vol. 63, No. 9, pp. 751–757, December, 2020, DOI 10.1007/s11018-021-01850-3.

Multiscale wavelet-based regularized reconstruction algorithm for three-dimensional compressed sensing magnetic resonance imaging

Nowadays, the most dynamic and safe imaging technique used in hospitals to diagnose is magnetic resonance imaging (MRI). In clinical applications, such as follow-up of patients with planning surgery, radiation therapy, therapy response in bone metastases assessment and treatment for brain disorders, repeated MRI scans are performed. Unfortunately, the slow acquisitions of MRI drive cost high by limiting patient throughput as well as limiting the potential indications for use. A mathematical framework called compressed sensing (CS) to accelerate MRI acquisition is used for generating undersampled measurements. Then, the iterative nonlinear numerical method is required for perfect reconstruction from highly undersampled measurements. In this work, the multiscale wavelet domain regularization prior-based iterative reconstruction algorithm is developed to address the above-mentioned reconstruction problem, in which multiscale wavelet domain generalized Gaussian mixture model is utilized for the regularization prior. The regularization-based algorithm is applied to generate three-dimensional (3D) high-quality MRI volume from multichannel k-space measurements in a compressed sensing framework. Several experiments using one synthetic and one real 3D CS multichannel k-space data were used to evaluate and validate the performance of the proposed algorithm. The total variation (TV)-based and soft-thresholding (ST) based-reconstruction methods were implemented to compare with the proposed algorithm. Extensive experimental results demonstrated that the proposed method has improved results in terms of visual quality and quantitative measurements compared to the existing methods.

Multi-channel impedance measurement system for polymer humidity sensors

The design of a high performance, multi-channel impedance measurement system for humidity sensing characteristics test of polymer humidity sensors is presented. The relay modules with negligible contact resistance are designed to accomplish 32-channel measurement and automatic switching range. The division of the wide impedance range and the selection of the corresponding feedback resistance for automatic switching range are so designed as to satisfy the actual scale range of the ADC module for getting high accuracy measurement. An algorithm of compensation considering the parasitic parameters both in the measurement and feedback circuits is designed to further improve measurement accuracy. The whole measurement is automatically implemented by a field-programmable gate array for signal generation and logic calculation and a digital signal processor for frequency-spectra analysis. The results of the uncertainty analysis and tests revealed that our system was quite adequate to efficient humidity sensing characteristics test as opposed to the existing devices.

A distributed high-density resistivity meter working on a two-wire cable

High-density resistivity meter is a common instrument used in shallow geophysical exploration. At present, the structure of the centralized high-density resistivity meter is bulky, while the distributed instrument needs more than 7 core cables, and the long measuring line needs to relay power supply to the intelligent electrode to provide enough voltage, so it is not convenient to use in the field. Aiming at the above problems, a new type of distributed high-density meter is developed, which can realize the power supply, communication, and measurement functions of distributed high-density meter and carry out multi-channel measurement through the time-division multiplexing of a two-wire cable and the short-time power supply of supercapacitor. Finally, the performance of the instrument was verified by experiments.

A time-to-digital-converter utilizing bits-counters to decode carry-chains and DSP48E1 slices in a field-programmable-gate-array

This paper presents the implementation of a field-programmable-gate-array based high-resolution time-to-digital converter, which utilizes the carry-chains and the digital-signal-processor slices for time interpolating. Bits-counter decoders are employed to manage the output codes from both the carry-chains and the digital-signal-processor slices, in order to achieve a high utilization rate of the time interpolating cells. A single channel TDC has a 2.03 ps averaged bin size and a 2.8 ps single-shot precision. The differential-non-linearity (DNL) of the single channel TDC is −1.82 ps/+12.56 ps, and the integral-non-linearity (INL) is within −6.55 ps/+47.95 ps. The TDC performance can be further improved by implementing multiple chains in a single time measurement channel, and a 2.2 ps single-shot precision is obtained by employing four parallel channels to measure the same input signal. The reported TDC can achieve a better precision with less resources comparing to previous studies, therefore the reported architecture is favorable for those applications that require high resolution multichannel time measurements.