Signal Amplitude Research Articles

Piezoelectric nanogenerator enabled fully self-powered instantaneous wireless sensor system

Self-powered real-time wireless sensor networks (WSNs) are vital for smart manufacturing, intelligent healthcare, and smart cities etc. Various nanogenerators have extensively exploited for powering these systems. Compared with triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs) have smaller dimensions and can be easier to integrate into ICs, offering broader applications. However, fully self-powered instantaneous wireless sensor systems with PENGs are yet to be realized. Here, we present a PENG based, self-powered, instantaneous wireless (PSIW) sensor system. The integration of the PENG with an RLC sensing circuit facilitates the spontaneous generation of oscillating signals with encoded sensing information. To address the high internal impedance issue of PENGs, a piezoelectric voltage driven electronic switch is employed, ensuring that the oscillating signal's frequency and amplitude remain highly stable. The effects of variables on signal frequency are investigated in detail. The sensor system is utilized to sense bending (strain), and to monitor physical actions such as elbow swing, wrist bending, walking, running etc. Results showed that the PSIW sensor system can effectively monitor strain and bending angles, and judiciously detect body’s physical movements. By employing either frequency-based or amplitude-based sensing methods, the PSIW sensor system demonstrates versatility and adaptability for various sensing applications.

Imaging method of surface defect using leaky Rayleigh wave with ultrasonic phased array

ABSTRACT Leaky Rayleigh wave testing has the advantages of non-contact, high sensitivity, and good beam directivity. However, current leaky Rayleigh wave testing mostly uses monolithic transducers and is seriously affected by acoustic wave attenuation and diffusion, resulting in poor image quality, limited detection range and low imaging efficiency. In this paper, a novel leaky Rayleigh wave testing method using an ultrasonic phased array is developed, and the total focusing method (TFM) is used to achieve high-quality imaging of defects. To improve imaging efficiency, the root-mean-square velocity (RMSV) model is utilised to rapidly acquire an approximate analytical solution for the time of flight of the acoustic wave. To obtain more accurate defect images further, the amplitude of the total focusing imaging signal is calibrated. The experimental results demonstrate that the proposed method achieves a 48% improvement in the imaging accuracy of a 1 mm hole, compared to the traditional Synthetic Aperture Focusing Technique (SAFT) imaging method. The proposed method achieves an 92% reduction in imaging time compared to the conventional TFM. The proposed method provides a new non-contact approach for high-accuracy and high-efficiency defect imaging using leaky Rayleigh wave.

Biases in Ecoacoustics Analysis: A Protocol to Equalize Audio Recorders.

Eco-acoustic indices allow us to rapidly evaluate habitats and ecosystems and derive information about anthropophonic impacts. However, it is proven that indices' values and trends are not comparable between studies. These incongruences may be caused by the availability on the market of recorders with different characteristics and costs. Thus, there is a need to reduce these biases and incongruences to ensure an accurate analysis and comparison between soundscape ecology studies and habitat assessments. In this study, we propose and validate an audio recording equalization protocol to reduce eco-acoustic indices' biases, by testing three soundscape recorder models: Song Meter Micro, Soundscape Explorer Terrestrial and Audiomoth. The equalization process aligns the signal amplitude and frequency response of the soundscape recorders to those of a type 1 level meter. The adjustment was made in MATLAB R2023a using a filter curve generated comparing a reference signal (white noise); the measurements were performed in an anechoic chamber using 11 audio sensors and a type 1 sound level meter (able to produce a .WAV file). The statistical validation of the procedure was performed on recordings obtained in an urban and Regional Park (Italy) assessing a significant reduction in indices' biases on the Song Meter Micro and Audiomoth.

Seismoacoustic Wavefield at Popocatépetl Volcano, Mexico, Captured by a Temporary Broadband Network from 2021 to 2022

Abstract Popocatépetl is a highly active stratovolcano in central Mexico with recurrent activity of Vulcanian-type explosions and frequent degassing. The proximity of Popocatépetl volcano to Mexico City, one of the most populated cities in the world, demands continuous monitoring to achieve an adequate volcano risk assessment. We present an overview of the first high-dynamic-range and high-broadband (0.01–200 Hz; 400 Hz sampling rate) seismoacoustic network (PoPiNet), which we operated around Popocatépetl volcano from August 2021 to May 2022. Here, we show preliminary results of the explosions recorded in September 2021. We deployed five seismoacoustic stations within 4–25 km horizontal distance (range) from the vent. We identify infrasonic waveforms associated with tremor and explosions, with pressures ranging from 16 to 134 Pa and dominant frequencies between 0.2 and 5.0 Hz. The frequency content of the recorded signals at the closest stations to the volcano spans the sub-bass (20–60 Hz) and bass (60–250 Hz) ranges. The associated seismic signals of moderate explosions exhibit air-to-ground coupled waves with maximum coherence values at frequencies up to 5 and 25 Hz for the farthest and closest stations to the volcano, respectively. Conversely, we observe infrasound signal amplitudes from relatively small explosions reaching maximum pressures of 10 Pa that do not couple into the ground, even at the closest stations. These infrasound signals are associated with type-I long-period events as reported in previous investigations. The waveform consistency suggests repetitive and nondestructive sources beneath the volcano.

Application of Fractional-Order Multi-Wing Chaotic System to Weak Signal Detection

This work investigates a fractional-order multi-wing chaotic system for detecting weak signals. The influence of the order of fractional calculus on chaotic systems’ dynamical behavior is examined using phase diagrams, bifurcation diagrams, and SE complexity diagrams. Then, the principles and methods for determining the frequencies and amplitudes of weak signals are examined utilizing fractional-order multi-wing chaotic systems. The findings indicate that the lowest order at which this kind of fractional-order multi-wing chaotic system appears chaotic is 2.625 at a=4, b=8, and c=1, and that this value decreases as the driving force increases. The four-wing and double-wing change dynamics phenomenon will manifest in a fractional-order chaotic system when the order exceeds the lowest order. This phenomenon can be utilized to detect weak signal amplitudes and frequencies because the system parameters control it. A detection array is built to determine the amplitude using the noise-resistant properties of both four-wing and double-wing chaotic states. Deep learning images are then used to identify the change in the array’s wing count, which can be used to determine the test signal’s amplitude. When frequencies detection is required, the MUSIC method estimates the frequencies using chaotic synchronization to transform the weak signal’s frequencies to the synchronization error’s frequencies. This solution adds to the contact between fractional-order calculus and chaos theory. It offers suggestions for practically implementing the chaotic weak signal detection theory in conjunction with deep learning.

Signal enhancement of the gas detection based on quartz-enhanced photothermal spectroscopy technology.

This paper presents an improved gas sensor based on the dual-excitation of quartz-enhanced photothermal spectroscopy (QEPTS) using a single quartz tuning fork (QTF) for signal detection. The silver coating on one side of the QTF was chemically etched to increase the laser power interacted with QTF for QEPTS signal excitation. By etching the silver coating on one side of QTF, the reflection structure between the silver coating of the other side of QTF and the external flat mirror was established. The device uses an absorption gas cell with an optical range length of 3 m, making the laser beam interact with the gas more completely and posing more gas concentration information. Acetylene was selected as the target gas to verify the performance of the sensor. The experimental results show that the signal amplitude with a flat mirror was 1.41 times that without a flat mirror, and 2.47 times that of traditional QEPTS sensor. The system has a minimum detection limit (MDL) of 1.10 ppmv, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 7.14 × 10-9 cm-1·W·Hz-1/2. Allan variance analysis results show that when the integration time is 700 s, the MDL of the system is 0.21 ppmv. The proposed gas sensor can play an important role on detecting trace gas in many fields.

A fast modeling method of pulsed eddy current with double-coil based on model-driven BP neural network

ABSTRACT The analytical model serves as an important guiding role in practical testing. Currently, there is scarce research on the building of the analytical model for pulsed eddy current (PEC) with double-coil excited by a voltage source. This paper derives a frequency-domain model for PEC from a double-coil’s equivalent circuit. To tackle low modeling efficiency due to complex impedance changes and mutual inductance changes, a BP neural network (BPNN) maps lift-off, thickness, and frequency to impedance changes and mutual inductance changes. Training samples for the BPNN come from analytical models of impedance change and mutual inductance change. The study finds that a BPNN with four hidden layers and Bayesian regularization significantly improves prediction accuracy. Finally, the correctness of the BPNN method is verified. The PEC signals obtained by the BPNN method, the analytical model, and the Comsol finite element method are compared under different thicknesses and lift-offs, and it is found that the consistency of the PEC signals of these three methods is very well. And, the amplitudes of the residual signals between the BPNN method and the analytical model of the PEC signals are relatively small, and the relative errors at the peaks are no more than 0.4%.

Hydrodynamic cavitation overall intensity evaluation via noise characterization and its effect on phenol oxidative degradation

In the present research, noise signals generated by Hydrodynamic cavitation (HC) are charcterized to provide an applicable direct measuring for overall intensity estimation of the phenomenon. The signals amplitude plots as well as spectrogram graph and Fast Fourier Transform (FFT) are used for the characterization of the noise generated by the cavitation phenomenon for each experiment. In order to validate the outcomes, results are complied with the chemical activity. For such a purpose, phenol oxidative degradation kinetics is selected. Experiments are performed at various HC inlet pressure (4–7 bar), operating temperature (20–40 °C) and concentration of H2O2 (0–1000 mg L−1). It was found that the amplitude mean square of the cavitation noise signals could be introduced as a quantitative index for HC overall intensity. The results imply a direct correlation of HC overall intensity with chemical reaction at constant temperature. According to the results, it can also be also concluded that H2O2 presence can affect the intensity of HC and facilitate the super cavitation occurrence, as well as its expected beneficial effect on phenol degradation. The optimum values of the signal characterization and chemical experiments are in the same wavelength showing an 88.9 % degradation in the presence of 1000 mg L−1 H2O2 at 5 bar inlet pressure.

Assessment of Anisotropic Acoustic Properties in Additively Manufactured Materials: Experimental, Computational, and Deep Learning Approaches.

The influence of acoustic anisotropy on ultrasonic testing reliability poses a challenge in evaluating products from additive technologies (AT). This study investigates how elasticity constants of anisotropic materials affect defect signal amplitudes in AT products. Experimental measurements on AT samples were conducted to determine elasticity constants. Using Computational Modeling and Simulation Software (CIVA), simulations explored echo signal changes across ultrasound propagation directions. The parameters A13 (the ratio between the velocities of ultrasonic transverse waves with vertical and horizontal polarizations at a 45-degree angle to the growth direction), A3 (the ratio for waves at a 90-degree angle), and Ag (the modulus of the difference between A13 and A3) were derived from wave velocity relationships and used to characterize acoustic anisotropy. Comparative analysis revealed a strong correlation (0.97) between the proposed anisotropy coefficient Ag and the amplitude changes. Threshold values of Ag were introduced to classify anisotropic materials based on observed amplitude changes in defect echo signals. In addition, a method leveraging deep learning to predict Ag based on data from other anisotropy constants through genetic algorithm (GA)-optimized neural network (NN) architectures is proposed, offering an approach that can reduce the computational costs associated with calculating such constants.

Electrical trace analysis of superconducting nanowire photon-number-resolving detectors

We apply principal component analysis (PCA) to a set of electrical output signals from a commercially available superconducting nanowire single-photon detector (SNSPD) to investigate their photon-number-resolving capability. We find that the rising edge as well as the amplitude of the electrical signal have the most dependence on photon number. Accurately measuring the rising edge while simultaneously measuring the voltage of the pulse amplitude maximizes the photon-number resolution of SNSPDs. Using an optimal basis of principal components, we show unambiguous discrimination between one- and two-photon events, as well as partial resolution up to five photons. This expands the use case of SNSPDs to photon-counting experiments, without the need of detector multiplexing architectures. Published by the American Physical Society 2024

Nugget and corona bond size measurement through active thermography and transfer learning model

Resistance spot welding (RSW) is considered a preferred technique for joining metal parts in various industries, mainly for its efficiency and cost-effectiveness. The mechanical properties of spot welds are pivotal in ensuring structural integrity and overall assembly performance. In this work, the quality attributes of resistance spot welding, such as both nugget and corona bond sizes, are assessed by analyzing the thermal behavior of the joint using a physical information neural network (PINN). Starting from the thermal signal phase gradient and amplitude gradient maps, a convolutional neural network (CNN) estimates the size of nuggets and corona bonds. The CNN architecture is based on the Inception V3 architecture, a state-of-the-art neural network that excels in image recognition tasks. This study suggests adopting a new methodology for automatic RSW quality control based on thermal signal analysis.

Response analysis of NMRG system considering Rb–Xe coupling

Abstract The dynamic range of the nuclear magnetic resonance gyroscope can be effectively improved through the closed-loop control scheme, which is crucial to its application in inertial measurement. This paper presents the analytical transfer function of Xe closed-loop system in the nuclear magnetic resonance gyroscope considering Rb-Xe coupling effect. It not only considers the dynamic characteristics of the system more comprehensively, but also adds the influence of the practical filters in the gyro signal processing system, which can obtain the accurate response characteristics of signal frequency and amplitude at the same time. The numerical results are compared with a experimentally verified simulation program, which indicate great agreement. The research results of this paper are of great significance to the practical application and development of the nuclear magnetic resonance gyroscope.

Efficiency Improvement for Chipless RFID Tag Design Using Frequency Placement and Taguchi-Based Initialized PSO.

Frequency encoding chipless Radio Frequency Identification (RFID) tags have been frequently using the radar cross section (RCS) parameter to determine the resonant frequencies corresponding to the encoded information. Recent advancements in chipless RFID design have focused on the generation of multiple frequencies without considering the frequency position and signal amplitude. This article proposes a novel method for chipless RFID tag design, in which the RCS response can be located at an exact position, corresponding to the desired encoding signal spectrum. To achieve this, the empirical Taguchi method (TM), in combination with particle swarm optimization (PSO), is used to automatically search for optimal design parameters for chipless RFID tags with a fast response time, to comply with the frequency encoding requirements in the presence of the mutual coupling effect. The proposed design method is validated using I-slotted chipless tag structures that are fabricated and measured with different sets of resonant frequencies.

Size effect in CoFeB-based anomalous Hall sensor and its applications in pulse detection

Anomalous Hall effect (AHE) sensor has attracted significant attention in recent years due to its simpler structure and better sensitivity as compared to semiconductor-based Hall sensors. However, up to now, most of the work has been focused on its basic functionalities, with very little attention given to optimizing its performance for practical applications. In this work, we systematically investigated how the lateral dimension affects the performance of CoFeB-based AHE sensors. By adjusting the sensor width while keeping the length and other parameters constant, it is found that although the output signal amplitude decreases with increasing the sensor width, the noise and power consumption also decrease simultaneously, leading to an overall enhancement of the sensor performances with a sensitivity, detectivity, and power efficiency of 8960 Ω/T, 205nT/Hz@1Hz, and 2009 V/WT, respectively. To illustrate the potential applications of AHE sensors, we demonstrate a proof-of-concept experiment to showcase the potential AHE sensor in pulse detection.

A 56-Gb/s,0.708 pJ/bit single-ended simultaneous bidirectional transceiver with hybrid errors cancellation techniques for die-to-die interface

This paper presents a low-power Simultaneous Bidirectional (SBD) transceiver with a power consumption of 0.708 pJ/bit, supporting a 56-Gb/s single-ended high-bandwidth density for die-to-die communication. To address the issue of separating signal amplitude mismatch in the SBD hybrid circuit, a transmitter driver with feed-forward equalization (FFE) is designed to realize adjustable impedance and amplitude. Instead of using a transconductance amplifier (TIA), the R-CTLE circuit is designed to compensate for insertion loss while improving the Signal Integrity (SI). Designed in a 28 nm standard CMOS process, the transceiver demonstrates 56 Gb/s/wire (28 Gb/s each direction) over a 21 mm on-chip channel. The energy efficiency is 0.708 picojoules per bit (0.708 pJ/bit) and a bit error rate (BER) ¡10e−12 with compensating insertion loss of −4.16 dB.

Characterization of shale oil and water micro-occurrence based on a novel method for fluid identification by NMR T2 spectrum

Nuclear magnetic resonance (NMR) has become a crucial technique for determining complex fluid occurrence characteristics. However, the overlap of fluid signals limits the application of the one-dimensional (1D) T2 (transverse relaxation time). Conventional methods are unable to accurately detect fluid signals that overlap entirely and cannot directly determine the petrophysical significance of each component spectrum. Leveraging the additional T1 (longitudinal relaxation time) information of two-dimensional (2D) NMR T1-T2, this study introduced a novel T2 spectra fluid identification method using the mixed Gaussian function. Six states of T2 and T1-T2 experiments were conducted on shale samples, including as-received (AR), water restoration of AR (WR-AR), water–oil restoration of AR (WOR-AR), oil-washed and dry (dry), oil-saturated (SO), and water-saturated (SW). By introducing the T2 projection spectrum, linear relationships between the 1D and 2D NMR signal amplitudes of various fluids were established to help separate the overlapping signals in the T2 spectrum. The results have demonstrated an inconsistent linear correlation between the 1D and 2D NMR signal amplitudes of various fluids, influenced by fluid composition and occurrence state. The conversion coefficient of adsorbed oil is 1.745 times higher than that of bound oil in the SO state. The adsorbed-to-bound ratio conversion coefficient was proposed as an additional input in the mixed Gaussian function to identify overlapping fluids in the oil–water coexistence state. The conversion coefficient of bound water determined by peak area analysis of 1D and 2D NMR before and after water restoration validated the identification results. The pore water altered the occurrence characteristics of oil, reducing the conversion coefficient. Furthermore, the pore-size characteristics of fluid occurrence were described. Compared with the conventional method, the proposed method bridges the gap between 1D and 2D NMR, significantly enhancing the accuracy and reliability of fluid identification by T2 spectra and providing new insights for NMR fluid evaluation in shale reservoirs.

Cooperation mechanism between the vacuum levels and interface dipole energy of triboelectric materials for real-time vibration recognition

Triboelectric Nanogenerator (TENG) device, acting as sensors of energy harvesting and material identification, are attracting intensive attention. Nevertheless, vibration wave recognition cannot be achieved by the developed devices owing to device structure and working mechanism limitations. Here, a cooperation mechanism between the vacuum level and interface dipole energy of triboelectric materials is proposed to explore triboelectric charge generation and migration. Hence, metallized cottons of high specific surface area encapsulated by polydimethylsiloxane (PDMS) of low elastic modulus are developed as TENG-based sensors to realize vibration recognition. The results indicate electrons flow from PDMS to metallized cotton with the generation of an extremely thin interfacial dipole barrier layer because the vacuum level of metallized cotton is higher than that of PDMS, resulting in the accumulation of triboelectric charges on the PDMS surface under vibration, further leading to generating distinct triboelectric signals that can be simply differentiated using signal amplitude or peak shape with 98.3 % recognition accuracy for vibration waveform. Besides, combined with the deep machine learning and triboelectric effect, a vibration perception system integrated with a TENG-based sensor, data processing and display modules is also developed for real-time vibration monitoring. This work contributes to further clarifying the theoretical mechanisms of triboelectric charge excitation under vibration.

Compensation of high-power interference using eigen vectors of the correlation matrix

Formulation of the problem. The main means of protecting the navigation equipment of consumers of the global navigation satellite system from intentional interference is the spatial selection of information signals using an adaptive antenna array. In this case, the weight coefficients of the antenna array must be calculated without using information about the directions to the signal and interference sources. To calculate the weighting coefficients, the eigenvectors of the correlation matrix of the input signals of the antenna array, obtained during its spectral decomposition, can be used. Purpose. The purpose of this work is to present an algorithm for spatial interference compensation based on the spectral decomposition of the correlation matrix of the input signals of an adaptive antenna array and to evaluate its effectiveness. Results. An algorithm for spatial compensation of interference in a four-element adaptive antenna array is presented, based on the spectral decomposition of the correlation matrix of its input signals. The effectiveness of the presented algorithm was assessed using computer simulation. It was found that when the input of the antenna array was exposed to three interferences with an amplitude 500-2000 times greater than the amplitude of the information signal, the level of interference at the output of the antenna array decreased by more than 4000 times. Practical significance. The results obtained can be used in the development of a compensator for interference signals of navigation equipment of consumers of the global navigation satellite system.

Distributed estimation cascade second-order tri-stable stochastic resonance method for random noise suppression in continuous-wave mud pulse telemetry

The extraction of high-quality useful signals in downhole environments has perennially posed a technical challenge for continuous wave mud pulse transmission systems. Traditional denoising methods, such as adaptive filtering and wavelet transform, inevitably compromise the integrity of the original signal's useful components while removing noise. Different from the traditional denoising methods, stochastic resonance (SR) is capable of transferring the energy of noise to the signal, thereby accomplishing the objective of noise suppression. In this paper, based on the second-order tri-stable stochastic resonance detection method combined with the estimation of distribution algorithm (EDA), a distributed estimation cascade second-order tri-stable stochastic resonance (EDA- CSTSR) detection method is proposed for the first time. By constructing 5 groups of simulation signals with different signal-to-noise ratios (SNRs), the denoising performance of distributed estimation CSTSR, Langevin SR, and Duffing SR is analyzed in detail. The results show that compared with Langevin SR and Duffing SR, the EDA-CSTSR can increase the SNR of the input simulation signal by at least 17 dB and significantly increase the amplitude of the useful pulse signal. Moreover, the EDA-CSTSR is applied to process actual data obtained from a drilling site, enabling the recognition of weak signals amidst strong background noise, which highlights the method's excellent practical application value and underscores its potential for further enhancement.

Relative intensity noise reduction for noise-like pulses based on synchronous intensity modulation

Extra-cavity synchronous intensity modulation is a straightforward and effective approach to suppress the full-bandwidth relative intensity noise (RIN) for noise-like pulse (NLP) fiber lasers. In this work, the picosecond NLPs are produced by a homemade Er/Yb co-doped mode-locked fiber laser and subsequent optimized by the synchronous intensity modulation device. The photodetector (PD) and electro-optical modulator (EOM) allow the real-time measurement and modulation for each optical pulse. A detailed description of key parameters for components is presented. The impact of DC bias voltage and RF signal amplitude on resulting RIN is experimentally investigated. We demonstrate a noteworthy reduction in RIN from 5.62% before synchronous intensity modulation to 1.91% afterward. Numerical investigations suggest that this method is potential to suppress RIN to below 1% under ideal conditions. The key factor in RIN reduction is correct operating region and high photoelectric-conversion linearity. The synchronous intensity modulation is an effective method to obtain low-RIN NLP laser sources, valuable for some practical applications.