High Sampling Rate Research Articles

Body Heat Powered Wirelessly Wearable System for Real‐time Physiological and Biochemical Monitoring

AbstractThrough harvesting energy from the environment or human body, self‐power wearable electronics have an opportunity to break through the limitations of battery supply and achieving long‐term continuous operation. Here, a wireless wearable monitoring system driven entirely by body heat is implemented. Based on the principle of maximizing heat utilization, while optimizing internal resistance and heat dissipation, the stretchable TEG improves the power density of previous similar devices from only a few microwatts per square centimeter to tens and makes it possible to continuously drive wireless wearable electronic systems. Furthermore, ceaseless self‐power energy gives wearable electronics unparalleled continuous working ability, which can realize the tracking and monitoring of biochemical and physiological indicators at different time scale. A practical system demonstrates the ability to real‐time monitor heart rate, sweat ingredient and body motion at a high sampling rate. This study marks an important advance of self‐powered wearable electronics for wirelessly real‐time healthy monitoring.

PO-03-007 CROSS VALIDATION OF A MACHINE LEARNING ALGORITHM DESIGNED TO IDENTIFY PULMONARY VEIN SIGNALS USING COMPETING TRAINING DATA

State of the art electrophysiology (EP) recording systems allow recording of both the surface electrocardiograms and intracardiac electrograms (EGMs) in an unfiltered fashion with improved quality, lower noise and higher sample rate. Legacy EP recording systems sample and store irreversibly pre-filtered EGMs, often susceptible to noise and interference. This study hypothesized that EGM quality impacts the performance of machine learning algorithms later trained on these data. This study compares the performance of a previously validated machine learning algorithm that classifies pulmonary vein potentials trained on simultaneous recorded legacy and novel EP recording system datasets. Data from pulmonary vein isolation procedures were simultaneously collected using a conventional EP recording system (EP-Tracer, CardioTek and LabSystem Pro, Boston Scientific) and a novel electrophysiology recording system (ECGenius System, CathVision, Denmark) connected in parallel. A machine learning classification algorithm, originally designed for legacy data, was adapted to use data from both the conventional and ECGenius EP recording systems. The performance of the algorithm was evaluated using k-fold validation without overlap in patient data between folds. Classified labels across all folds were summarized and compared to the common ground truth labels of the two datasets. In total, 1536 samples from 521 EGMs were extracted from 89 patients prospectively enrolled in the PVISION study. The trained algorithm using the ECGenius recorded EGMs resulted in a higher predictive value (91% vs 84%), sensitivity (93% vs 90%) and overall accuracy (91% vs 86%) compared to the algorithm trained with EGMs from the conventional dataset, resulting in superiority of the ECGenius System algorithm (p <0.05). Receiver operating characteristic (ROC) curves show superiority of the ECGenius-trained algorithm at all relevant operating points (Fig. 1). Performance of machine learning algorithms using intracardiac EP data are dependent on EGM quality and sample rate. Using high quality, unfiltered unipolar EGMs from a novel EP recording system resulted in superior machine learning algorithm performance compared to the use of EGMs recorded with legacy EP systems.

Ultrawideband RF-IQ Modulator Using Segmented Nonlinearly Scaled RF-DACs and Nonoverlapping LO Signals

A wideband Cartesian in-phase and quadrature (I/Q) modulator based on dual 10 b RF-digital-to-analog converters (DACs) is presented. Nonoverlapping local oscillator (LO) signals and a segmented nonlinear scaling with scaled unit cells contribute to a high linearity and allow for a low-complexity digital predistortion (DPD). Unit-cell flip-flops (FFs) and a balanced clock distribution enable a high sample rate with little skew. Out-of-band emissions are reduced through drive-slope control of the data-switch inputs. The modulator, implemented in the 22-nm fully-depleted silicon-on-insulator (FDSOI) CMOS, operates between 20 and 26 GHz with a peak sample rate of 11 GS/s. It has been used to demonstrate the transmission of a 64-quadrature amplitude modulation (QAM) single-carrier (SC) signal at 13.2 Gb/s, a 256-QAM SC signal at 7.33 Gb/s, and an orthogonal frequency division multiplexing (OFDM) signal comprising four aggregated 400-MHz 64-QAM channels with an error vector magnitude (EVM) of 6.43%. These results demonstrate the potential of the proposed modulator for the realization of ultrawideband transmitters in high-performance millimeter-wave (mmW) systems.

Experimental investigation of the supersonic cavity by spectral-POD of high-sampling rate pressure-sensitive paint data

Experimental investigation of the supersonic cavity by spectral-POD of high-sampling rate pressure-sensitive paint data

Power Gain from Energy Harvesting Sources at High MPPT Sampling Rates.

Energy harvesting (EH) sources require the tracking of their maximum power point (MPP) to ensure that maximum energy is captured. This tracking process, performed by an MPP tracker (MPPT), is performed by periodically measuring the EH transducer's output at a given sampling rate. The harvested power as a function of the sampling parameters has been analyzed in a few works, but the power gain achieved with respect to the case of a much slower sampling rate than the EH source's frequency has not been assessed so far. In this work, simple expressions are obtained that predict this gain assuming a Thévenin equivalent for the EH transducer. It is shown that the power gain depends on the relationship between the square of AC to DC open circuit voltage of the EH transducer. On the other hand, it is proven that harvested power increases, using a suitable constant signal for the MPP voltage instead of tracking the MPP at a low sampling rate. Experimental results confirmed the theoretical predictions. First, a function generator with a series resistor of 1 kΩ was used, emulating a generic Thévenin equivalent EH. Three waveform types were used (sinus, square, and triangular) with a DC voltage of 2.5 V and AC rms voltage of 0.83 V. A commercial MPPT with a fixed sampling rate of 3 Hz was used and the frequency of the waveforms was changed from 50 mHz to 50 Hz, thus effectively emulating different sampling rates. Experimental power gains of 11.1%, 20.7%, and 7.43% were, respectively, achieved for the sinus, square, and triangular waves, mainly agreeing with the theoretical predicted ones. Then, experimental tests were carried out with a wave energy converter (WEC) embedded into a drifter and attached to a linear shaker, with a sinus excitation frequency of 2 Hz and peak-to-peak amplitude of 0.4 g, in order to emulate the drifter's movement under a sea environment. The WEC provided a sinus-like waveform. In this case, another commercial MPPT with a sampling period of 16 s was used for generating a slow sampling rate, whereas a custom MPPT with a sampling rate of 60 Hz was used for generating a high sampling rate. A power gain around 20% was achieved in this case, also agreeing with the predicted gain.

Moth‐flame‐optimisation based parameter estimation for model‐predictive‐controlled superconducting magnetic energy storage‐battery hybrid energy storage system

AbstractSuperconducting magnetic energy storage‐battery hybrid energy storage system (HESS) has a broad application prospect in balancing direct current (DC) power grid voltage due to its fast dynamic response ability under low‐frequency/high‐frequency disturbances. Model‐predictive‐control (MPC) with characteristics such as high sampling rate and wide applicability could be applied to HESS. However, considering that the relevant circuit parameters would change with ambient temperature, interference and ageing, the effect of MPC may deteriorate inevitably. This article proposes an improved MPC strategy for SMES‐Battery HESS, taking moth‐flame‐optimisation (MFO) algorithm to calculate the circuit parameters in real time. The actual parameters are updated by MFO and then sent to model predictive controller to minimise the model mismatches. The advantages of high accuracy and fast convergence speed is verified by comparison with grey wolf optimisation and particle swarm optimisation. The simulation shows that by taking the proposed scheme, DC bus voltage are more stable and the superconducting magnetic energy storage can maintain more than 95% capacity utilisation and avoid over‐discharge even if the model parameters are inconsistent with the actual ones under circumstances of alternating current grid fault and fluctuation of new energy output.

Gradient adaptive sampling and multiple temporal scale 3D CNNs for tactile object recognition.

Tactile object recognition (TOR) is very important for the accurate perception of robots. Most of the TOR methods usually adopt uniform sampling strategy to randomly select tactile frames from a sequence of frames, which will lead to a dilemma problem, i.e., acquiring the tactile frames with high sampling rate will get lots of redundant data, while the low sampling rate will miss important information. In addition, the existing methods usually adopt single time scale to construct TOR model, which will induce that the generalization capability is not enough for processing the tactile data generated under different grasping speeds. To address the first problem, a novel gradient adaptive sampling (GAS) strategy is proposed, which can adaptively determine the sampling interval according to the importance of tactile data, therefore, the key information can be acquired as much as possible when the number of tactile frames is limited. To handle the second problem, a multiple temporal scale 3D convolutional neural networks (MTS-3DCNNs) model is proposed, which downsamples the input tactile frames with multiple temporal scales (MTSs) and extracts the MTS deep features, and the fused features have better generalization capability for recognizing the object grasped with different speed. Furthermore, the existing lightweight network ResNet3D-18 is modified to obtain a MR3D-18 network which can match the tactile data with smaller size and prevent the overfitting problem. The ablation studies show the effectiveness of GAS strategy, MTS-3DCNNs, and MR3D-18 networks. The comprehensive comparisons with advanced methods demonstrate that our method is SOTA on two benchmarks.

A machine learning model for predicting progressive crack extension based on experimental data obtained using DCPD measurement technique

A series of low cycle fatigue loading experiments were performed, using Inconel 718 (IN718) superalloy, to produce time history data using Direct Current Potential Drop (DCPD) approach at a range of temperatures and it was used to train a Bidirectional Long-Short Term Memory Neural Network (BiLSTM) model. The experimental investigations were conducted to understand the statistical nature of crack jumps during the fatigue loading. The BiLSTM model was trained on high sampling rate experimental data from crack initiation up through the Paris regime. For a single sample, the trained machine learning model was able to predict future crack events based on prior crack jumps in the time history. The model was able to predict the progressive crack extension at intermediate temperatures and stress intensities that lie between experimental conditions. The BiLSTM model demonstrated the potential to be used as a tool for future investigation into fundamental mechanisms such as high-temperature oxidation at the crack tips.

Time series data for process monitoring in injection molding: a quantitative study of the benefits of a high sampling rate

Abstract Process monitoring systems are playing an increasingly important role in reducing production capacity losses in injection molding. Process monitoring and optimization systems are mostly based on processing data of injection molding machine control systems. These data consist of scalar data and time series. This paper introduces a novel approach to modelling injection molding processes using only time series data and evaluates the quantitative influences of varying sampling times on calculation of integral values and model quality. On the basis of the first experiment, it is shown that the sampling rates of these time series have a large influence on information which can be derived from this data (e.g. injection work). These findings provide an assessment of whether the effort is justified for the respective requirements on the accuracy of the injection work and other parameters derived from the time series. In the second experiment, a model is presented which uses only the injection flow and injection pressure profile as input and achieves high coefficients of determination for the prediction of the part weight, despite the absence of mold sensor data and scalar data. It is shown that higher sampling rates of time series results in higher prediction quality of these models. This improves the understanding of the data needed for high quality machine learning models of injection molding processes and enable users to estimate a lower bound for the sample rates of time series for their use cases.

Instability measurement of cavitation conditions in a spool valve through the definition of a cavitation instability index

Cavitation phenomenon is commonly known as a cause of deterioration of hydraulic components and a source of noise. In a proportional spool control valve for fluid power applications, the reduction of the cross section area at small openings can lead to the growth of gas bubbles inside the fluid phase. This phenomenon modifies the operating efficiency of the valve and can damage the material over time. The oil flow has been visualised through an experimental activity by using a 2-way 2-position spool valve with a plexiglass case and analysed by a CMOS type camera, with a high sampling rate. The recorded images focus the interaction between gas and liquid phases and highlights how the shape of cavitation bubbles is modified when the surface stress increases. In the present work, the interaction between liquid and gas phase has been studied by applying a non-linear method on the numerical matrices, derived from the acquired images. A cavitation instability index has been proposed, which is able to describe the cavitation behaviour and to define the instability of the equilibrium between the two phases. The trend of this index gives information about the two phase flow conditions, in order to reduce the cavitation phenomenon.

A new electromagnetic probe array diagnostic for analyzing electrostatic and magnetic fluctuations in EAST plasmas

A novel electromagnetic probe array (EMPA) diagnostic, which consists of a magnetic probe array and an electrostatic probe array, has recently been developed on EAST. The EMPA is fixed near the first wall at horizontal port P. The magnetic probe array of the EMPA consists of 24 identical magnetic probes, each of them capable of measuring toroidal, poloidal and radial magnetic fluctuations simultaneously, providing additional toroidal magnetic fluctuation measurements compared with the regular magnetic probes on EAST. With a higher sampling rate and self-resonant frequency, the EMPA magnetic probes can provide higher frequency magnetic fluctuation measurements. The magnetic probe array of the EMPA is composed of two parallel layers of magnetic probes with a radial distance of 63 mm, and each layer of magnetic probes is arranged in four poloidal rows and three toroidal columns. The compact arrangement of the EMPA magnetic probe array largely improves the toroidal mode number measurement ability from to and also improves the high poloidal wave number measurement ability of magnetic fluctuations compared with the regular high frequency magnetic probes on EAST. The electrostatic probe array of the EMPA consists of two sets of four-tip probes and a single-tip probe array with three poloidal rows and four toroidal columns. It complements the electrostatic parameter measurements behind the main limiter and near the first wall in EAST. The engineering details of the EMPA diagnostic, including the mechanical system, the electrical system, the acquisition and control system, and the effective area calibration, are presented. The preliminary applications of the EMPA in L-mode and H-mode discharges on EAST have demonstrated that the EMPA works well for providing information on the magnetic and electrostatic fluctuations and can contribute to deeper physical analysis in future EAST experiments.

Ultrasonic imaging of damage in plates in spectral ripple frequency domain

The ultrasonic C-scan method is a well-known non-destructive inspection method which typically analyzes ultrasound echoes in time domain. Although demonstrated for a variety of defects and materials, it has certain challenges in correctly assessing complex and distributed defects in multi-layer heterogeneous materials, e.g. barely visible impact damage in composite laminates.This paper proposes a C-scan imaging technique in the spectral ripple frequency domain by exploiting interference phenomena between multiple ultrasonic echoes in the response signal. The spectral ripple frequency domain imaging method processes the full A-scan signal without having to define an evaluation time gate. As such, it is highly suited for inspection of curved or tilted parts, or parts having different thickness sections. The method is demonstrated on several 5.5 mm thick carbon fiber reinforced polymer laminates with barely visible impact damage, revealing its capability to resolve neighboring delaminations and small defect fragments over the entire depth of the sample. The spectral ripple frequency domain imaging method shows good performance on datasets with high noise levels and/or low sampling rates, and is computationally very efficient, allowing real-time defect imaging.

Body condition changes at sea: Onboard calculation and telemetry of body density in diving animals

Abstract The ability of marine mammals to accumulate sufficient lipid energy reserves is vital for mammals' survival and successful reproduction. However, long‐term monitoring of at‐sea changes in body condition, specifically lipid stores, has only been possible in elephant seals performing prolonged drift dives (low‐density lipids alter the rates of depth change while drifting). This approach has limited applicability to other species. Using hydrodynamic performance analysis during transit glides, we developed and validated a novel satellite‐linked data logger that calculates real‐time changes in body density (∝lipid stores). As gliding is ubiquitous amongst divers, the system can assess body condition in a broad array of diving animals. The tag processes high sampling rate depth and three‐axis acceleration data to identify 5 s high pitch angle glide segments at depths &gt;100 m. Body density is estimated for each glide using gliding speed and pitch to quantify drag versus buoyancy forces acting on the gliding animal. We used tag data from 24 elephant seals (Mirounga spp.) to validate the onboard calculation of body density relative to drift rate. The new tags relayed body density estimates over 200 days and documented lipid store accumulation during migration with good correspondence between changes in body density and drift rate. Our study provided updated drag coefficient values for gliding (Cd,f = 0.03) and drifting (Cd,s = 0.12) elephant seals, both substantially lower than previous estimates. We also demonstrated post‐hoc estimation of the gliding drag coefficient and body density using transmitted data, which is especially useful when drag parameters cannot be estimated with sufficient accuracy before tag deployment. Our method has the potential to advance the field of marine biology by switching the research paradigm from indirectly inferring animal body condition from foraging effort to directly measuring changes in body condition relative to foraging effort, habitat, ecological factors and anthropogenic stressors in the changing oceans. Expanding the method to account for diving air volumes will expand the system's applicability to shallower‐diving (&lt;100 m) species, facilitating real‐time monitoring of body condition in a broad range of breath‐hold divers.

Homogeneity Enforced Calibration of Stage Nonidealities for Pipelined ADCs

Pipelined analog-to-digital converters (ADCs) are fundamental components of various signal processing systems requiring high sampling rates and a high linearity. Over the past years, calibration techniques have been intensively investigated to increase the linearity. In this brief, we propose an equalization-based calibration technique which does not require knowledge of the ADC input signal for calibration. For that, a test signal and a scaled version of it are fed into the ADC sequentially, while only the corresponding output samples are used for calibration. Several test signal sources are possible, such as a signal generator (SG) or the system application (SA) itself. For the latter case, the presented method corresponds to a background calibration technique. Thus, slowly changing errors are tracked and calibrated continuously. Because of the low computational complexity of the calibration technique, it is suitable for an on-chip implementation. Ultimately, this brief contains an analysis of the stability and convergence behavior as well as simulation results.

An ARL-Unbiased Modified np-Chart for Autoregressive Binomial Counts

Abstract Independence between successive counts is not a sensible premise while dealing, for instance, with very high sampling rates. After assessing the impact of falsely assuming independent binomial counts in the performance of np-charts, such as the one with 3-σ control limits, we propose a modified np-chart for monitoring first-order autoregressive counts with binomial marginals. This simple chart has an in-control average run length (ARL) larger than any out-of-control ARL, i.e., it is ARL-unbiased. Moreover, the ARL-unbiased modified np-chart triggers a signal at sample t with probability one if the observed value of the control statistic is beyond the lower and upper control limits L and U. In addition to this, the chart emits a signal with probability γ L {\gamma_{L}} (resp. γ U {\gamma_{U}} ) if that observed value coincides with L (resp. U). This randomization allows us to set the control limits in such a way that the in-control ARL takes the desired value ARL 0 {\operatorname{ARL}_{0}} , in contrast to traditional charts with discrete control statistics. Several illustrations of the ARL-unbiased modified np-chart are provided, using the statistical software R and resorting to real and simulated data.

Extreme-Low-Speed Heavy Load Bearing Fault Diagnosis by Using Improved RepVGG and Acoustic Emission Signals.

With the worldwide carbon neutralization boom, low-speed heavy load bearings have been widely used in the field of wind power. Bearing failure generates impulses when the rolling element passes the cracked surface of the bearing. Over the past decade, acoustic emission (AE) techniques have been used to detect failure signals. However, the high sampling rates of AE signals make it difficult to design and extract fault features; thus, deep neural network-based approaches have been proposed. In this paper, we proposed an improved RepVGG bearing fault diagnosis technique. The normalized and noise-reduced bearing signals were first converted into Mel frequency cepstrum coefficients (MFCCs) and then inputted into the model. In addition, the exponential moving average method was used to optimize the model and improve its accuracy. Data were extracted from the test bench and wind turbine main shaft bearing. Four damage classes were studied experimentally. The experimental results demonstrated that the improved RepVGG model could be employed for classifying low-speed heavy load bearing states by using MFCCs. Furthermore, the effectiveness of the proposed model was assessed by performing comparisons with existing models.

An improved correlation stack method for near‐surface seismic data

AbstractThe seismic reflection method is the primary tool to provide detailed information on the near‐surface. This paper proposes a common receiver correlation stack method. A towed seismograph is used to perform the rapid acquisition with small geophone spacing and a high sample rate, while broadband (from 1 Hz to 1000 Hz) seismic data are collected. Results of modelled and measured data indicate that the proposed stack method: (1) does not need normal move‐out velocity; (2) does not lose shallow information; and (3) improves the stacking fold by twice as much as the conventional stack method. Compared with the conventional stack method, the proposed method can suppress surface waves better and improve the quality of the final sack section, especially for high‐frequency data. Combined with the towed seismograph, the proposed stack method can perform the rapid collection and enhance the accuracy of stack imaging of near‐surface seismic data.

Hardware Efficient Reconfigurable Digital Down Converter for Software Radio Receiver

ABSTRACT This paper describes a reconfigurable digital down converter (DDC) that can reduce the input sampling frequency from 128 MHz to an output of 1 MHz on a field-programmable gate array (FPGA) device. The proposed design employs a polyphase mixer followed by a half-band (HB) filter and a finite impulse response (FIR) filter. All the decimation filters involve polyphase architectures, enabling efficient hardware implementation. The polyphase mixer can reduce the high sampling rate frequency, and combining both HB and FIR filters results in a complex down-converted baseband spectrum. In addition, the modified HB filter reduces multiplier units, and the proposed FIR filter saves the delay elements. Using the optimal hardware description language (HDL) programming style, the design can further improve the area efficiency without sacrificing functionality. To evaluate the performance, the design has been implemented on the Kintex-7 Xilinx FPGA device. Experimental outcomes show that the proposed design significantly reduces hardware costs associated with other existing designs. Finally, a verification test certifies that the DDC has achieved a satisfactory spurious-free dynamic range (SFDR) of 115 dB. The high sampling rate and parallel processing capabilities of the proposed DDC make it an attractive solution for any software radio standard.

Feasibility of using the Leap Motion Controller to administer conventional motor tests: a proof-of-concept study

Although upper-limb movement impairments are common, the primary tools for assessing and tracking impairments in clinical settings are limited. Markerless motion capture (MMC) technology has the potential to provide a large amount of quantitative, objective movement data in routine clinical use. Many past studies have focused on whether MMC are sufficiently accurate. However, another necessary step is to create meaningful clinical tests that can be administered via MMC in a robust manner. Four conventional upper-limb motor tests common in clinical assessments (visually guided movement, finger tapping, postural tremor, and reaction time) were modified so they can be administered via a particular MMC sensor, the Leap Motion Controller (LMC). In this proof-of-concept study, we administered these modified tests to 100 healthy subjects and present here the successes and challenges we encountered. Subjects generally found the LMC and the graphical user interfaces of the tests easy to use. The LMC recorded movement with sufficiently high sampling rate (>106 samples/s), and the rate of LMC malfunctions (mainly jumps in time or space) was low, so only 1.9% of data was discarded. However, administration of the tests also revealed some significant weaknesses. The visually guided movement test was easily implemented with the LMC; the modified reaction time test worked reasonably well with the LMC but is likely more easily implemented with other existing technologies; and the modified tremor and finger tapping tests did not work well because of the limited bandwidth of the LMC. Our findings highlight the need to develop and evaluate motor tests specifically suited for MMC. The real strength of MMC may not be in replicating conventional tests but rather in administering new tests or testing conditions not possible with conventional clinical tests or other technologies.

Technical note: Measurement of the bunch structure of a clinical proton beam using a SiPM coupled to a plastic scintillator with an optical fiber.

Recent proposals of high dose rate plans in protontherapy as well as very short proton bunches may pose problems to current beam monitor systems. There is an increasing demand for real-time proton beam monitoring with high temporal resolution, extended dynamic range and radiation hardness. Plastic scintillators coupled to optical fiber sensors have great potential in this context to become a practical solution towards clinical implementation. In this work, we evaluate the capabilities of a very compact fast plastic scintillator with an optical fiber readout by a SiPM and electronics sensor which has been used to provide information on the time structure at the nanosecond level of a clinical proton beam. A 3×3×3mm3 plastic scintillator (EJ-232Q Eljen Technology) coupled to a 3×3mm2 SiPM (MicroFJ-SMA-30035, Onsemi) has been characterized with a 70 MeV clinical proton beam accelerated in a Proteus One synchrocyclotron. The signal was read out by a high sampling rate oscilloscope (5 GS/s). By exposing the sensor directly to the proton beam, the time beam profile of individual spots was recorded. Measurements of detector signal have been obtained with a time sampling period of 0.8 ns. Proton bunch period (16 ns), spot (10 μs) and interspot (1 ms) time structures could be observed in the time profile of the detector signal amplitude. From this, the RF frequency of the accelerator has been extracted, which is found to be 64 MHz. The proposed system was able to measure the fine time structure of a clinical proton accelerator online and with ns time resolution.