High-precision Signal Research Articles

Large bandwidth and dynamic range current sensor based on micro-PCB Rogowski coil

A micro-PCB Rogowski coil with a volume of only 0.032 cm3 was proposed in this paper, and a magnetic flux concentration structure was introduced while ensuring the high-frequency performance of the coil to improve the measurement ability of the Rogowski coil sensor for weak current characterization. We have systematically investigated the magnetic core materials suitable for high-frequency current sensing, and the performance of core at high frequency has also been verified theoretically and experimentally. In addition, a high-precision signal processing circuit, whose main functions are amplification and integration, has also been developed. Experiments have shown that the developed PCB Rogowski coil current sensor can measure the current in the μA level with a bandwidth of 10 MHz, a sensitivity of up to 180 V A−1 and a resolution of up to 50 μA. The response time of the sensor to a step signal is within 30 ns, and it also has good waveform following ability. Compared with commercial TMR sensors, the sensor proposed in this work also has certain advantages in terms of noise.

Achieving a Wide-Range Linear Piezoresistive Response in Electrowritten Soft-Hard Polymer Blends via Salami-Inspired Heterostructure Design.

Flexible electronics capable of acquiring high-precision signals are in great demand for the development of the internet of things and intelligent artificial. However, it is currently a challenge to simultaneously achieve high signal linearity and sensitivity for stretchable resistive sensors over a wide strain range toward advanced application scenarios requiring high signal accuracy, e.g., sophisticated physiological signal discrimination and displacement measurement. Herein, a film strain sensor, which has an electrical and mechanical dual heterostructure, was fabricated via a direct near-field electrowriting and molecule-guided in situ growth of silver nanoparticles with different concentrations on high-modulus polystyrene domains and low-modulus styrene-butadiene copolymers with a salami-like morphology. Mechanism analyses from both theoretical and experimental investigations reveal that the salami-like heteromodulus microstructure regulates microcrack propagation routes, while the heteroconductivity changes the electron transport paths and amplifies the resistance increase during crack propagation. Therefore, the as-designed strain sensor shows a linear resistive response within ca. 70% strain with a gauge factor of 25, unveiling a simple and scalable strategy for trading off signal linearity and sensitivity over a wide strain range for the fabrication of high-performance linear strain sensors.

An Off-Grid Compressive Sensing Algorithm Based on Sparse Bayesian Learning for RFPA Radar

In the application of Compressive Sensing (CS) theory for sidelobe suppression in Random Frequency and Pulse Repetition Interval Agile (RFPA) radar, the off−grid issues affect the performance of target parameter estimation in RFPA radar. Therefore, to address this issue, this paper presents an off−grid CS algorithm named Refinement and Generalized Double Pareto (GDP) distribution based on Sparse Bayesian Learning (RGDP−SBL) for RFPA radar that utilizes a coarse−to−fine grid refinement approach, allowing precise and cost−effective signal recovery while mitigating the impact of off−grid issues on target parameter estimation. To obtain a high-precision signal recovery, especially in scenarios involving closely spaced targets, the RGDP−SBL algorithm makes use of a three−level hierarchical prior model. Furthermore, the RGDP−SBL algorithm efficiently utilizes diagonal elements during the coarse search and exploits the convexity of the grid energy curve during the fine search, therefore significantly reducing computational complexity. Simulation results demonstrate that the RGDP−SBL algorithm significantly improves signal recovery performance while maintaining low computational complexity in multiple scenarios for RFPA radar.

Binary‐Stochasticity‐Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better‐than‐Software Accuracy

In this work, the requirement of using high‐precision (HP) signals is lifted and the circuits for implementing deep learning algorithms in memristor‐based hardware are simplified. The use of HP signals is required by the backpropagation learning algorithm since the gradient descent learning rule relies on the chain product of partial derivatives. However, it is both challenging and biologically implausible to implement such an HP algorithm in noisy and analog memristor‐based hardware systems. Herein, it is demonstrated that the requirement for HP signals handling is not necessary and more efficient deep learning can be achieved when using a binary stochastic learning algorithm. The new algorithm proposed in this work modifies elementary neural network operations, which improves energy efficiency by two orders of magnitude compared to traditional memristor‐based hardware and three orders of magnitude compared to complementary metal–oxide–semiconductor‐based hardware. It also provides better accuracy in pattern recognition tasks than the HP learning algorithm benchmarks.

High-Resolution Enlarged Open-Bore Narrowband Magnetic Particle Imaging Based on Double-Layer Linear Scanning Structure

We developed a novel high resolution open-bore narrowband magnetic particle imaging (MPI) system for big animal and future clinical usage. Optimized strength and direction of excitation field was invested for enlarged field of view (FOV), and a 4T/m gradient field as well as its linear scanning trajectory were formed by 16 square coils without coupling to excitation, forming a maximum 120mm×120mm open bore FOV. Further, a new differential pick-up structure consisted of multi Helmholtz excitation and pickup coils away from the FOV was proposed in order to suppress the system noises, which was very important for the limited coil setting space in open-bore MPI. The new differential structure contributed significantly to improve the signal-to-noise ratio as high as about 100dB without lose of harmonic signals. Several home-made resonant circuits, filters and lock-in amplifier was used to constitute the high precision signal acquisition system. Experimental results showed that the detection limit of the system was 1ug Fe of Resovist sample with a spatial resolution of 1mm after reconstruction, satisfying many clinical needs such as drug delivery and bedside-monitoring.

A Bionic Skin for Health Management: Excellent Breathability, In Situ Sensing, and Big Data Analysis.

Developing an intelligent wearable system is of great significance to human health management. An ideal health-monitoring patch should possess key characteristics such as high air permeability, moisture-wicking function, high sensitivity, and a comfortable user experience. However, such a patch that encompasses all these functions is rarely reported. Herein, an intelligent bionic skin patch for health management is developed by integrating bionic structures, nano-welding technology, flexible circuit design, multifunctional sensing functions, and big data analysis using advanced electrospinning technology. By controlling the preparation of nanofibers and constructing bionic secondary structures, the resulting nanofiber membrane closely resembles human skin, exhibiting excellent air/moisture permeability, and one-side sweat-wicking properties. Additionally, the bionic patch is endowed with a high-precision signal acquisition capabilities for sweat metabolites, including glucose, lactic acid, and pH; skin temperature, skin impedance, and electromyographic signals can be precisely measured through the in situ sensing electrodes and flexible circuit design. The achieved intelligent bionic skin patch holds great potential for applications in health management systems and rehabilitation engineering management. The design of the smart bionic patch not only provides high practical value for health management but also has great theoretical value for the development of the new generation of wearable electronic devices.

Spatial Resolution Optimization Technology of Raman OTDR Using Variable Sampling Rate

The technology to improve the spatial resolution of distributed sensing has always been a research hotspot. In this paper, a ROTDR (Raman-OTDR) system with a variable sampling rate and a spatial resolution optimization algorithm are designed, as shown in the figure, which makes the distribution of sampling points dense and improves spatial resolution. A point selection matrix based on the reconstruction algorithm is generated to reconstruct a one-dimensional high-precision signal. Furthermore, an optimization algorithm based on the reconstruction algorithm is designed, which can select the sampling rate and flexibly set the desired spatial resolution. Sampling rate combination and offset distance are set as the output of this algorithm to improve the response time and eliminate the blind area in the reconstruction process. The simulation results show that the reconstruction algorithm can improve the spatial resolution of the distributed temperature measurement system to 0.25 m at the cost of sampling ten times at different sampling rates (91 MHZ - 100 MHZ), representing a resolution gain of four times compared with the spatial resolution limited to 1 m by the acquisition card. This paper also compares the sampling results at a single sampling rate, random sampling rate combination, and optimized sampling rate combination to certify the effectiveness of this technology. The sampling result at an optimized sampling rate combination has the highest measurement accuracy.

Development and measurement of airborne oxygen sensor for aircraft inerting system based on TDLAS with pressure compensation and 2f/1f normalized method

Using Tunable Diode Laser Absorption Spectroscopy (TDLAS）technology to accurately measure the oxygen concentration in the ullage of the aircraft fuel tank is of great significance to ensure the safety of the aircraft and improve the working quality of the aircraft onboard inerting system. This paper focuses on designing a smart sensor for the detection of oxygen concentration applied to airborne inerting systems, which can reduce random environmental noise and lineshape errors during the flying process. First, a prototypeincluding a laser emission module with wavelength modulation and temperature control, a multi-reflection long optical path absorption gas cell, and a high-precision signal extraction module are designed. Then, ground experiments at 294 K, and 1 bar are carried out. The oxygen inversion method is designed, the oxygen concentration results are obtained, and the lower detection limit (3.3 ppm) and detection accuracy (9.6 ppm) of the sensor are discussed. Thirdly, the change of absorption line profile influenced by the change of pressure is discussed. The series of oxygen concentration measurement experiments with variable pressure (274 k, 1–0.1 bar) was carried out. and the measurement error is below 1%. Finally, a second harmonic (2f)/first harmonic (1f) normalized method is proposed to eliminate the errors caused by laser attenuation and environmental vibration in oxygen measurement.

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

A De-Noising Method for Microseismic Signal Based on the Synchrosqueezing Transform in the Time-Frequency Domain

The denoising for the microseismic signals in the strong background interferences during the microseismic monitoring of hydraulic fracturing is very important for events identification and first-arrival picking. As characteristics of microseismic signals are normally random, non-stationary, and time-varying, a powerful tool for weak signal extraction of microseismic data is still a great challenge. In this study, a denoising method for microseismic signals is proposed based on the synchrosqueezing transform in the time-frequency domain. Firstly, the microseismic signals are denoised by the adaptive threshold method in the time-frequency domain by the synchrosqueezing transform. Then, the frequency center of the effective signal is integrated and extracted in the time-frequency domain. At last, the weak signal is extracted by the reconstruction of synchrosqueezing transform. The results from the synthesized non-stationary signal with different noise intensities and the real microseismic single of a track record show that this method has better anti-noise ability and higher signal extraction accuracy. Moreover, according to the processing and analysis of microseismic data in field wells, it is proved that this denoising method has a good practical value and can extract the effective and high-precision signals in the strong noise background.

Construction and Application of On-Line Roof Separation Monitoring System Based on High-Precision FBG Indicator

Aiming at the existing problems of low efficiency, lagging monitoring results, and poor reliability and accuracy in the present roadway roof separation monitoring system, a high-precision Fiber Bragg Grating (FBG) separation indicator is designed through theoretical analysis, numerical simulation, and laboratory tests—according to the fiber grating sensing principle. Based on the Microsoft. Net platform, C# programming language, and VS2010 integrated development tools, the corresponding on-line monitoring software is developed. On this basis, a real-time on-line roof separation monitoring system is proposed. The high-precision FBG separator was calibrated and tested in the laboratory and results suggested that both the left and right arm demonstrate good sensitivity and linearity with linear fitting coefficient values of 0.9981 and 0.9979, respectively. The monitoring system was successfully applied to roof separation monitoring of the #14301 rail roadway in the Shaqu coal mine. The monitoring results showed that the on-line roof separation monitoring system based on the high-precision FBG separation indicator has the advantages of high precision, good stability, and long-distance signal transmission, which can achieve real-time dynamic monitoring and provides an effective method for long-term on-line monitoring of roadway roof separation.

Research on the Single-Double Interval Closed-Loop Method of Resonant Fiber Optic Gyro

Resonant fiber optic gyro (RFOG) is a type of inertial sensor with great application potential. Its closed-loop signal detection methods include single closed-loop, double closed-loop and other methods to ensure high system stability and high-precision signal output. In view of the respective advantages of single and double closed-loop systems, a single-double interval closed-loop scheme for RFOG is proposed in this letter. By introducing the threshold trigger mechanism, the advantages of low control signal noise of the single closed-loop system and large dynamic range of the double closed-loop system are fully combined. In addition, in order to improve the linearity of the demodulation curve, a linear fitting method is applied to the system. The experiment shows that the short-term and long-term stability of the system have been improved effectively.

Millihertz band low-intensity-noise single-frequency laser for space gravitational wave detection

A low-noise single-frequency laser is a key component of the space-based gravitational wave detector, and the intensity noise of the laser directly affects the sensitivity of the space-based GW detector. In this work, we report a low-noise single-frequency laser designed for space-based gravitational wave detector. The laser is based on a master oscillator power amplifier (MOPA), which is designed to possess a low-power, narrow-linewidth seed laser acting as master oscillator (MO) and an all polarization-maintaining fiber amplifier acting as power amplifier (PA). The amplifier that uses a robust mechanical design consists of an Yb-doped double-clad fiber forward pumped by wavelength-locked 976 nm pump laser diode (LD) to achieve 2.13 W of output power and 70 dB of signal-noise ratio (SNR). To suppress the relative intensity noise (RIN) in a millhertz regime (1 mHz–1 Hz), we characterize the power stabilization of a pump diode laser based on a proportional-integral-derivative (PID) feedback control loop where an in-loop photodetector is used. The power fluctuation can be converted into the fluctuation of the current signal by the photodiode, the current signal is converted into the voltage signal and amplified by a transimpedance circuit. Then, the voltage signal is compared with the voltage reference signal, and the error signal is achieved to adjust real-timely the drive current of the pump laser diode. This is a good way to significantly suppress the RIN of a laser at low frequencies, but the measured RIN below 4 mHz is still higher than –60 dBc/Hz. In order to further suppress the RIN to lower than 4 mHz, an active precise temperature control technology is used to suppress the thermal noise from pump LD and fiber coupler. To assess the RIN milliertz regime, we design an RIN measurement system consisting of a high-precision signal acquisition card (24 bit) and a computer program based on LabVIEW. The measurement range of the system is 2 μHz–102.4 kHz and the frequency resolution up to 2 µHz, much better than the counterparts of commercial instruments. By stabilizing the fiber amplifier pump LD current and the temperature of pump LD and the temperature of fiber coupler, the out-of-loop RINs are measured to be –63.4 dBc/Hz@1 mHz and –105.8 dBc/Hz@1 Hz , and in a milliertz regime of 1 mHz–1 Hz, the RIN is below –60 dBc/Hz. The results show that the feedback control of the fiber amplifier pump LD current and the temperature control of the key devices can effectively suppress the RIN in the millihertz frequency band, which lays a foundation for further improving the intensity noise performance in the low frequency band.

Application of matrix multiplication in signal sensor image perception

Abstract LOT wireless sensor nodes are limited by physical factors, usually have weak computing power and endurance, and wireless communication methods are very vulnerable to information theft. Therefore, it is of great significance to ensure the safe and efficient transmission of images in new application scenarios. In view of the need for an efficient image transmission, this paper combines compressed sensing technology with p-tensor product theory, applies the above theory to distributed wireless sensor networks, and uses the correlation of adjacent sensor nodes in wireless sensor networks to propose an improved a joint sparse model for measurement matrices and reduction algorithms. The feasibility is verified by simulation experiments, and the comparison between joint reconstruction and single reconstruction, and the application of various algorithms in other algorithms is carried out, and the actual completion time and storage capacity are analysed. The minimum completion time for wavelet transform is 1.29, the sparse estimated time for the selection of preliminary P waves is 0.07 and the compressed sensing time is 0.20. The maximum completion time for wavelet transform was 1.32, for sparse estimation, it is 0.62, for preliminary P-wave selection, it is 0.17, and for compressed sensing, it is 0.88. The processing time is no >3 s and the runtime is only 0.22–0.88 s. The results show that compared with the compressed sensing of a single node, the joint sparse model based on distributed compressed sensing has a smaller reconstruction error, and can achieve high-precision signal reconstruction when the measurement value is small.

Integrated interdigital electrode and thermal resistance micro-sensors for electric vehicle battery coolant conductivity high-precision measurement

Monitoring the conductivity of battery coolant is extremely important as it can significantly improve the safety of electric vehicles. The conductivity of coolant should be maintained in the range of 0-10 μS/cm to ensure the insulation level, requiring high-precision measurement of ultra-low detection range. However, polarization and capacitive effects, temperature effects, as well as poor stability, all pose significant challenges to achieving high-precision conductivity measurements. This work describes a novel integrated conductivity and temperature (CT) micro-sensor for the conductivity high-precision measurement of electric vehicle battery coolant. An interdigital micro-electrode is fabricated for conductivity sensing and a thin-film platinum resistor is employed as temperature sensor cell. The integrated CT sensor demonstrates an acceptable limit of detection with a resolution of 0.1 μS/cm. Besides, high-precision signal acquisition and processing circuit is designed for sensors and a desirable measuring full-scale error is observed. Compared to the current method of using bulky metal electrodes prepared by mechanical process as conductivity sensor, the proposed sensor can be mass produced by Micro-Electro-Mechanical System (MEMS), and favorable consistency, high sensitivity, excellent stability, smaller size are obtained. Consequently, the as-proposed CT micro-sensor has an opportunity to be developed as a competitive device for online conductivity monitoring in battery coolant.

KNN Based Denoising Algorithm for Photon-Counting LiDAR: Numerical Simulation and Parameter Optimization Design

Photon-counting LiDAR can obtain long-distance, high-precision target3D geographic information, but extracting high-precision signal photons from background noise photons is the key premise of photon-counting LiDAR data processing and application. This study proposes an adaptive noise filtering algorithm that adjusts parameters according to the background photon count rate and removes noise photons based on the local mean Euclidean distance. A simulated photon library that provides different background photon count rates and detection probabilities was constructed. It was then used to fit the distribution relationship between the background photon count rate and the average KNN (K-Nearest Neighbor) distance (k = 2–6) and to obtain the optimal denoising threshold under different background photon count rates. Finally, the proposed method was evaluated by comparing it with the modified density-based spatial clustering (mDBSCAN) and local distance-based statistical methods. The experimental results show that various methods are similar when the background noise rate is high. However, at most non-extreme background photon count rate levels, the F of this algorithm was maintained between 0.97–0.99, which is an improvement over other classical algorithms. The new strategy eliminated the artificial introduction of errors. Due to its low error rates, the proposed method can be widely applied in photon-counting LiDAR signal extraction under various conditions.

Human–robot interface based on sEMG envelope signal for the collaborative wearable robot

Surface electromyography (sEMG) control interface is a common method for human-centered robotics. Researchers have frequently improved the recognition accuracy of sEMG through multichannel or high-precision signal acquisition devices. However, this increases the cost and complexity of the control system. Therefore, this study developed a control interface based on the sEMG enveloped signal for a collaborative wearable robot to improve the accuracy of sEMG recognition based on the time-domain (TD) features. Specifically, an acquisition device is developed to obtain the sEMG envelope signal, and 11 types of TD features are extracted from the sEMG envelope signal acquired from the upper limb. Furthermore, a dimension reduction method based on the correlation coefficient is proposed, transforming the 11-dimensional feature into a five-dimensional envelope feature set without decreasing the accuracy. Moreover, a recognition algorithm based on a neural network has also been proposed for gesture classification. Finally, the recognition accuracy of the proposed method, principal component analysis (PCA) feature set, and Hudgins TD feature set is compared, with their accuracy at 84.39%, 72.44%, and 70.89%, respectively. Therefore, the results indicate that the envelope feature set performs better than the common gesture recognition method based on signal channel sEMG envelope signal.

Microwave photonic filter with a sub-kHz bandwidth based on a double ring Brillouin fiber laser

A scheme to achieve a sub-kHz bandwidth microwave photonic filter (MPF) based on a double-ring Brillouin fiber laser (DR-BFL) is proposed and experimentally verified in this study. The stimulated Brillouin scattering gain spectrum can be efficiently narrowed by the DR-BFL, which comprises a main-ring cavity with a 100-m single-mode fiber (SMF) and a sub-ring cavity with a 10-m SMF. A single passband narrow bandwidth MPF is accomplished by the DR-BFL with a single longitudinal mode laser output using the Vernier effect. The bandwidth of the MPF at the center frequency of 10.735 GHz is 114 Hz, and the corresponding Q-factor is 9.42 × 107, which is the highest Q value to the best of our knowledge. Since the measured linewidth of the DR-BFL is 1.2 Hz at the −20-dB power point, the theoretical bandwidth of the DR-BFL-based MPF is 0.16 Hz. This design provides a new idea for realizing high-precision signal extraction in future communication or sensing systems.

High-Precision Electrical Impedance Tomography for Electrical Conductivity of Metallic Materials

Metal materials are subject to deformation, internal stress distribution, and cracking during processing, all of which affect the distribution of electrical conductivity of the metal. Suppose we can detect the conductivity distribution of metal materials in real time. In that case, we can complete the inverse imaging of metal material properties, structures, cracks, etc. and realize nondestructive flaw detection. However, metal materials' small resistance, high electrical conductivity, and susceptibility of voltage signals to noise signal interference make an accurate measurement of metal conductivity challenging. Therefore, this paper addresses the problem of detecting the conductivity distribution of metals by investigating a high-precision four-electrode AC measurement method. This technical approach combines laminar imaging techniques with high-precision weak signal extraction methods. On this basis, a method and equipment for high-precision electrical impedance tomography of metallic materials’ electrical conductivity were established. The way specifies a new number of electrodes and adopts a model of spaced excitation reference measurements. Single-frequency sinusoidal AC signal is used for excitation, and Shannon wavelet analysis is used for signal extraction and noise reduction. Super-resolution reconstruction algorithms are used for resistivity distribution image reconstruction to improve image quality. Based on the results of various comparative experiments, it is clear that this new functional technique method has good imaging stability and operability and can perform tasks such as analyzing the internal conductivity distribution of metals. This research provides an effective way of new ideas for the safe detection of metal structures, the changes in crystal tissue structure, and the study of metal properties. In particular, it expands the scope of research in the development and application of resistance tomography, which has tremendous commercial potential research significance.

Time-Domain Signal Reconstruction of Vehicle Interior Noise Based on Data-Driven Method

Background: In the process of high-speed driving, there are many source signals that affect the ear noise of passengers in the car. It is important to obtain the reference signal of Active Noise Control (ANC) of the vehicle at high speed condition. Objective: This paper introduces a method to study the time-domain signal reconstruction of interior noise based on data-driven method. Methods: Based on the noise signal collected in a car, the key point signals affecting the interior noise are determined by the acoustic transfer path analysis method. Considering the time-varying characteristics of the noise signal and the complex nonlinear relationship of interior noise, a noise reconstruction model based on wavelet decomposition Radial Basis Function (RBF) neural network is established. And the BP neural network noise reconstruction model is set up to compare the reconstruction effect. Results: According to the reconstruction comparison, the average absolute error (0.0072) of the proposed algorithm model is smaller than the average absolute error of the noise reconstruction BP network model based on wavelet decomposition (0.0280), and the accuracy is improved by 74.29%. The average absolute error between the reconstructed value of RBF neural network and the real-value is smaller than that of BP neural network, and the error of the proposed model is less than 0.01. The method proposed in this paper can reconstruct the interior noise signal of vehicle accurately and effectively. Conclusion: This paper proposes a reconstruction model of vehicle interior noise signal based on wavelet decomposition RBF neural network algorithm driven by data-driven, and verifies the effectiveness of the algorithm with the real vehicle test data. The reconstruction method of RBF neural network based on data-driven wavelet decomposition provides a certain reference value for ANC to obtain high-precision reference signal.