High Sampling Rate Research Articles

Improvement of Electromagnetic Side-Channel Information Measurement Platform

Research has shown that when a microcontroller (MCU) is powered up, the emitted electromagnetic radiation (EMR) patterns are different depending on the executed instructions. This becomes a security concern for embedded systems or the Internet of Things. Currently, the accuracy of EMR pattern recognition is low. Thus, a better understanding of such issues should be conducted. In this paper, a new platform is proposed to improve EMR measurement and pattern recognition. The improvements include more seamless hardware and software interaction, higher automation control, higher sampling rate, and fewer positional displacement alignments. This new platform improves the performance of previously proposed architecture and methodology and only focuses on the platform part improvements, while the other parts remain the same. The new platform can measure EMR patterns for neural network (NN) analysis. It also improves the measurement flexibility from simple MCUs to field programmable gate array intellectual properties (FPGA-IPs). In this paper, two DUTs (one MCU and one FPGA-MCU-IP) are tested. Under the same data acquisition and data processing procedures with similar NN architectures, the top1 EMR identification accuracy of MCU is improved. The EMR identification of FPGA-IP is the first to be identified to the authors' knowledge. Thus, the proposed method can be applied to different embedded system architectures for system-level security verification. This study can improve the knowledge of the relationships between EMR pattern recognitions and embedded system security issues.

Calibration of frequency response mismatches in time‐interleaved analog‐to‐digital converter based on adaptive method

AbstractThe time‐interleaved analog‐to‐digital converter (TIADC) system could achieve high sampling rate effectively, but the frequency response inconsistency among channels will introduce frequency response mismatches, which will seriously degrade the signal quality. In this paper, a novel multi‐channel frequency response mismatches adaptive calibration method is proposed. The frequency response of the compensation filter is expanded into a polynomial of a digital differentiator, and the least mean square adaptive strategy is used to calibrate frequency response mismatches. It does not require measuring the channel frequency responses in advance and uses digital post‐processing technology to effectively compensate the frequency response of the multi‐channel TIADC system and improve the spurious‐free dynamic range. The experimental results verify the effectiveness of the algorithm.

Waveform Measurement Unit-Based Fault Location in Distribution Feeders via Short-Time Matrix Pencil Method and Graph Neural Network

This article proposes the use of the Short-Time Matrix Pencil method (STMPM) and Graph Neural Network (GNN) for fault location in active distribution feeders based on an emerging class of sensors, known as Waveform Measurement Units (WMUs). WMUs record synchronized voltage and current waveforms in the time domain with high sampling rates. The proposed fault location framework consists of two stages. In the first stage, STMPM is adopted to capture the dominant modes of the transient changes of WMUs' sinusoidal signals due to faults in different locations of the distribution grid. The second stage is to use a grid-informed GNN model to identify the fault location and type using the captured features of the signal before, during, and post-fault with STMPM. GNN can capture the spatial-temporal relationship between data from different sensors in different locations to enhance situational awareness and fault location accuracy. The proposed method is examined on a modified IEEE network with distributed energy resource (DER) and for transient symmetrical and asymmetrical faults under different loading, DER generation level, noises, and sensors' sampling rate conditions. The results show the merits of the proposed two-stage fault location framework compared to the conventional approaches; while a challenging problem is addressed in active distribution grids.

Non-Visual Sensing of Metallic Pavement Markers From a Moving Vehicle

Snow and ice covering paved surfaces cause problems for roads and other vehicles operating in cold regions. There are notable risks for road accidents and reduced performance of vehicle movements, e.g., in industrial facilities such as ports, when road markings and marked pathways are not visually observable. In this study, metallic paved surface markers identified by metal sensors are tested. The sound signals captured by a metal detector attached to the vehicle are used to detect the markings. Results indicate that the tested method for reading markings through snow is effective at lower speeds. The main advantages of the tested technology are effectiveness in snowy and icy surface conditions, low upfront cost detection technology in some environments, robust system without moving parts, high sampling rate, and low operating costs. The procedure is not restricted to specific environmental conditions, such as snow and ice; it also has the potential to detect markings through layers of mud, tree leaves or sand. The detectors have application prospects in intelligent transportation system applications at airports, logistics terminals and industrial facilities.

A hybrid compressive sensing approach to noise source visualization: Application to a diesel engine

To identify sound source locations by using Fourier-based Near-field Acoustical Holography (NAH), a large number of microphone measurements is generally required to span the source region and ensure a sufficiently high spatial sampling rate. As a result, such measurements are costly, a fact which has discouraged the industrial application of NAH to identify sound source locations. However, recently, compressive sensing approaches have made it feasible to identify concentrated sound sources with a limited number of microphone measurements. In the present work, sound radiation from the front face of a diesel engine was measured by using one set of measurements from a 35-channel combo-array. The locations of significant noise sources were then identified by using three compressive sensing algorithms: Wideband Acoustical Holography (WBH), l1-norm minimization, and a hybrid approach which combined WBH and l1-norm minimization. The latter approach takes advantage of the l1’ norm’s ability to locate spatially distinct sources, and WBH’s ability to suppress “ghost” sources. It was found that the hybrid algorithm can localize and visualize the major noise sources over a broad range of frequencies, even though using a relatively small number of microphones. Finally, comments are made regarding sound field reconstruction differences between the algorithms.

Nonlinear Harmonic Electric Energy Metering System Based on the Wavelet Transform

Background: To solve the problem of accurate measurement of nonlinear harmonic electric energy, a method for improving harmonic electric energy measurement based on the discrete wavelet packet decomposition and reconstruction algorithm was proposed. Objectives: A reactive energy measurement method combined with Hilbert transformation was designed, and the frequency characteristics of wavelet function were discussed. An excessively high sampling rate could reduce the accuracy of harmonic power measurement. By setting the discrete wavelet packet transformation decomposition, the stepping of the wavelet form resulting from the excessively large wavelet packet decomposition scale was eliminated using the moncoband reconstruction algorithm. Methods: Experimental results showed that the detection error obtained by numerical simulation using the wavelet transform algorithm was close to the standard instrument, with the maximum reference error of 0.013%-0.030%. Results and Conclusion: The feasibility and measurement accuracy of the nonlinear harmonic electric energy metering system were verified, providing an effective method for accurately detecting the harmonic, interharmonic, and time-varying harmonic components of the electric power system.

A Predictive Methodology for Temperature, Heat Generation and Transfer in Gigacycle Fatigue Testing

Recently, a trend in fatigue testing related to increasing excitation frequencies during experiments has been observed. This tendency is a product of both necessity and available technological capabilities. Regarding the last, advances in control and excitation systems made it possible to perform tests at impressive frequencies, beyond the tenths of kHz. Performing fatigue tests much faster is indeed very motivating, representing less time and money spent. On the other hand, such high testing frequencies create some challenges, such as the requirement of measurement systems capable of working with high sample rates and excessive heat generation on the testing samples. The last one is especially critical for fatigue once the mechanical properties, such as the elasticity modulus and yield strength, are highly dependent on the temperature. Therefore, being able to predict and control the sample temperature during fatigue testing is essential. The main goal of the present work is to provide a formulation for estimating the heat generation and specimen temperature during high frequency testing, namely in the ultra-high cycle fatigue (UHCF) regime. Several metallic alloys and specimen geometries were tested, and the model results were compared with experimental temperature measurements. The developed model was able to properly characterize the temperature trend over time. In addition, a script was developed and made publicly available.

MISARA: Matlab Interface for Seismo-Acoustic aRray Analysis

AbstractVolcanic activity produces a broad spectrum of seismic and acoustic signals whose characteristics provide important clues on the underlying magmatic processes. Networks and arrays of seismic and acoustic sensors are the backbone of most modern volcano monitoring programs. Investigation of the signals gathered by these instruments requires efficient workflows and specialist software. The high sampling rates, typically 50 Hz or greater, at which seismic and acoustic waveforms are recorded by multistation networks and dense arrays leads to the rapid accumulation of large volumes of data, making the implementation of efficient data analysis workflows for volcano surveillance a challenging task. Here, we present an open-source MATLAB graphical user interface, MISARA (Matlab Interface for Seismo-Acoustic aRray Analysis), designed to provide a user-friendly workflow for the analysis of seismoacoustic data in volcanic environments. MISARA includes efficient algorithm implementations of well-established techniques for seismic and acoustic data analysis. It is designed to support visualization, characterization, detection, and location of volcano seismoacoustic signals. Its intuitive, modular, structure facilitates rapid, semiautomated, inspection of data and results, thus reducing user effort. MISARA was tested using seismoacoustic data recorded at Etna Volcano (Italy) in 2010, 2011, and 2019, and is intended for use in education and research, and to support routine data analysis at volcano observatories.

Advances in silicon-based in-sensor computing for neuromorphic vision sensors

With the development of the Internet of Things (IoT), intelligent security systems, assisted driving and artificial intelligence, traditional machine vision sensors no longer meet the needs of low power consumption, high sampling rate, high dynamic range and low data redundancy, therefore, the emergence of new vision sensors is urgently needed. Drawing on the information extraction and encoding methods of biological retinas, in-sensor computing photoelectric detection systems have been developed. It has a more efficient sampling model and data transfer mode than frame-based and separated sensory-computing systems. This paper provides an overview of several major silicon-based in-sensor computing neuromorphic vision sensors. Using bio-vision mechanisms as an entry point, the architecture and working principles of these neuromorphic optoelectronic sensors are focused on, their application scenarios, advantages and limitations are compared, and the challenges and possible directions for development are analysed.

Transverse oscillation tensor velocity imaging using a row–column addressed array: Experimental validation

Tensor velocity imaging (TVI) performance with a row–column probe was assessed for constant flow in a straight vessel phantom and pulsatile flow in a carotid artery phantom. TVI, i.e., estimating the 3-D velocity vector as a function of time and spatial position, was performed using the transverse oscillation cross-correlation estimator, and the flow was acquired with a Vermon 128+128 row–column array probe connected to a Verasonics 256 research scanner. The emission sequence used 16 emissions per image, and a TVI volume rate of 234 Hz was obtained for a pulse repetition frequency (fprf) of 15 kHz. The TVI was validated by comparing estimates of the flow rate through several cross-sections with the flow rate set by the pump. For the constant 8 mL/s flow in the straight vessel phantom with relative estimator bias (RB) and standards deviation (RSD) was found in the range of −2.18% to 0.55% and 4.58% to 2.48% in measurements performed with an fprf of 15, 10, 8, and 5 kHz. The pulsatile flow in the carotid artery phantom the was set to an average flow rate of 2.44 mL/s, and the flow was acquired with an fprf of 15, 10, and 8 kHz. The pulsatile flow was estimated from two measurement sites: one at a straight section of the artery and one at the bifurcation. In the straight section, the estimator predicted the average flow rate with an RB value ranging from −7.99% to 0.10% and an RSD value ranging from 10.76% to 6.97%. At the bifurcation, RB and RSD values were between −7.47% to 2.02% and 14.46% to 8.89%. This demonstrates that an RCA with 128 receive elements can accurately capture the flow rate through any cross-section at a high sampling rate.

A fast 3-D inversion for airborne EM data using pre-conditioned stochastic gradient descent

SUMMARY Airborne electromagnetic (AEM) exploration produces large amounts of data due to its high sampling rate, so that the 3-D inversions take extremely big computation and time consumption. We present a fast 3-D inversion framework for large-scale AEM explorations using a pre-conditioned stochastic gradient descent combined with Gauss–Newton (PSG-GN) method. We adopt a compressed sensing (CS) in the 3-D forward modelling, in which a random undersampling is used to reduce the calculation, while the responses for all survey stations are obtained via a reconstruction technique. For our 3-D AEM inversions, a method of combining the stochastic gradient descent with Gauss–Newton (SG-GN) that requires only a small data set in each iteration instead of the conventional full-batch data (complete original data) inversion have been investigated. To further speed up the 3-D inversion, we develop a pre-conditioner considering the random sampling rate and gradient noise to achieve a fast convergence. We use two synthetic models to test the accuracy, convergence and efficiency of our algorithm. The results show that the conventional inversion with full-batch data and the PSG-GN method can both converge quickly, but our method can enhance the inversion efficiency up to 78 per cent. Finally, we invert a field data set acquired from a massive sulfide deposit in Ireland and obtain the results that agree well with the known geologies.

Norm and interpolation based absolute stress evaluation in steel members using the Lcr wave

The uniaxial absolute stress in steel members is a vital indicator for evaluating the performance of steel structures. Non-destructive ultrasonic detection is widely applied in the absolute stress measurement of steel members with the advantages of easy implementation, no limits on materials and high precision. The acquisition algorithm of time-of-flight (TOF), the sampling rate of ultrasonic signal and the processing of outliers determine the accuracy of the stress measurement. In this study, we proposed a norm and interpolation based algorithm for absolute stress measurement using the longitudinal critically refracted (Lcr) waves, which improves the accuracy and velocity of the absolute stress measurement. Firstly, the norm was designed to obtain the TOF of the ultrasonic signal. When only the Lcr wave arose in the ultrasonic signal, the two-norm similarity algorithm was found to be consistent with the cross-correlation-based algorithm and the corresponding theoretical expressions were derived. With the non-Lcr wave in the ultrasonic echo, the two-norm similarity algorithm has stronger anti-interference ability. Secondly, the pchipslopes, spline and linear interpolation algorithms were introduced via which the TOF of ultrasound with low sampling rate reaches the same accuracy as that of high sampling rate signal. Finally, a special class of outliers in TOF was explored, and a threshold based on the number of shift points and the stress feature was proposed to calibrate these outliers to normal values. Experiments demonstrate the proposed algorithm improves the accuracy and velocity of uniaxial absolute stress measurement in structural steel members.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

A simple scalable linear time algorithm for horizontal visibility graphs

Horizontal Visibility Graphs establish a connection between time series and complex networks. As a feature, they have shown strong results in time series classification. For real-world applications, algorithms for computing HVGs are necessary that work efficiently on streamed data, that can be parallelized, and whose runtime is independent of the type of time series. Our proposed algorithm extends the fast horizontal visibility algorithm of Zhu et al. satisfying all these desirable properties. The extended version stays worst-case in O(n), works additionally efficiently on streamed data, and becomes parallelizable. Contrary to recent publications, it does not require a complex data structure. This approach enables the computation of HVGs with millions of vertices in a short period, opening up new application areas of HVGs for time series generated batch-wise or resulting from measurements with a high sampling rate.

High-speed ADC based on photonic time-stretched technology with dispersion-tunable CFBG.

We propose a photonic time-stretched analog-to-digital converter (PTS-ADC) based on dispersion-tunable chirped fiber Bragg grating (CFBG), by demonstrating a economical ADC system with seven different stretch factors. The stretch factors are tunable by changing the dispersion of CFBG, in order to obtain different sampling points. Accordingly, the total sampling rate of the system can be improved. Only a single channel is required to increase the sampling rate and achieve the effect of multi-channel sampling. Finally, seven groups of different stretch factors ranging from 1.882 to 2.206 are obtained, which are equivalent to seven groups of different sampling points. We successfully recover the input radio frequency (RF) signals with frequencies from 2 GHz to 10 GHz. In addition, the sampling points are increased by 14.4 times and the equivalent sampling rate is increased to 288 GSa/s. The proposed scheme is suitable for commercial microwave radar systems, which can obtain a much higher sampling rate at a low cost.

Prediction of Welding Spatter Generation Rate in Short-Circuit Transfer by Convolutional Neural Network

In this study, a machine learning method is proposed to predict the weld spatter generation rate, regardless of the type of short-circuit waveform. Short-circuit waveform data are collected at a high sampling rate of 10?20 kHz, and then, compressed using a new preprocessing method to effectively process the data at a high sampling rate. To predict the spatter generation rate, the welding waveform is converted into an image using the proposed data preprocessing method, and the converted data are fed into the convolutional neural network (CNN). A parametric study on data augmentation and data resolution is conducted concurrently to enhance the prediction accuracy with limited amount of data.

Wideband Spectrum Sensing Using Modulated Wideband Converter and Data Reduction Invariant Algorithms.

Wideband spectrum sensing is a challenging problem in the framework of cognitive radio and spectrum surveillance, mainly because of the high sampling rates required by standard approaches. In this paper, a compressed sensing approach was considered to solve this problem, relying on a sub-Nyquist or Xsampling scheme, known as a modulated wideband converter. First, the data reduction at its output is performed in order to enable a highly effective processing scheme for spectrum reconstruction. The impact of this data transformation on the behavior of the most popular sparse reconstruction algorithms is then analyzed. A new mathematical approach is proposed to demonstrate that greedy reconstruction algorithms, such as Orthogonal Matching Pursuit, are invariant with respect to the proposed data reduction. Relying on the same formalism, a data reduction invariant version of the LASSO (least absolute shrinkage and selection operator) reconstruction algorithm was also introduced. It is finally demonstrated that the proposed algorithm provides good reconstruction results in a wideband spectrum sensing scenario, using both synthetic and measured data.

Multi-route fusion method of GNSS and accelerometer for structural health monitoring

Displacement extraction of structures is of great significance to structural health monitoring (SHM). Global Navigation Satellite System (GNSS) can provide absolute position, but the sampling rate is limited; while the accelerometer has a higher sampling rate, its displacement is obtained through double integration, which is not suitable for low-frequency deformation monitoring. The data fusion of GNSS and accelerometer is conducive to taking advantage of both for SHM. There has been substantial research undertaken on the data fusion of GNSS and accelerate for structural health monitoring. Most studies in the field of fusion algorithms between GNSS and accelerometer for structural health monitoring have only focused on large displacement extraction. Few empirical studies have focused on the fusion algorithm of slight displacement extraction. The present study aimed to explore an on-demand fusion method with GNSS and accelerometer measurement. The methodological approach taken in this study is a mixed methodology based on the interactive multiple model (IMM) Kalman filter. Three groups of experiments were carried out to verify the proposed method. The findings show that the low-frequency displacement or absolute position is maintained by the Kalman filter of GNSS kinematic positioning, and the dynamic feature or high-frequency displacement is extracted by the IMM Kalman filter of the accelerometer. On the one hand, long-term displacement is ensured, on the other hand, the sensitivity of short-term displacement detection will not be reduced. It is hoped that this research will contribute to a deeper understanding of displacement extraction in SHM.

Comparison of Low Power High Speed Comparator for Flash ADC

The analog to digital converter (ADC), a bridge between digital world and analog world, plays a crucial role in the modern semiconductor industry. Among different types of ADCs, the flash ADC (also known as the direct-conversion ADC) is exceedingly fast, whose high sample rate enables many large bandwidth applications, such as optical communication, radar detection. The comparator circuit is one of the critical components of flash ADC. The characteristics, like latency, gain and power consumption, of comparators determine the overall performance of flash ADCs. This paper analyzes and compares different types of comparator architecture and suggests a high-performance design for flash ADC.

Multiple conductance states of lipid pores during Voltage-Clamp electroporation

We used voltage-clamp electroporation to obtain single-channel recordings of lipid pores and analyzed the idealized dwell-time sequences using Maximum-Likelihood Fitting. We observed traces with multiple current levels and determined whether they were a result of the presence of multiple pores or a single pore with multiple conductance states. We found that, within the same recording, the bilayer can have a single pore with multiple conductance states or multiple independent pores. Using high sampling rates (100 kHz) we were able to observe pores with 40 μs lifetimes, and in experiments using high-voltage pulses we observed the existence of long-lived fluctuations minutes after the removal of the electric field. These results come closer to reconciling the nanosecond lifetime pores in molecular dynamics simulations and the long-lived permeability of cells after electroporation.