Phase Of Signal Research Articles

Dynamic mater-slave control strategy for transient coordination of inverter based microgrids under asymmetric faults

The ever-expanding inverter-based microgrids (MGs) require the grid-forming distributed renewable energy resources (DERs) to enhance the system safety. However, the different types of grid-forming DERs are prone to have transient conflicts with each other under short circuit faults, leading to unexpected outage or even system collapse. The fast response and poor overcurrent capability of DERs make transient coordination a challenge. This paper proposes a dynamic master–slave architecture for transient coordination control in islanded MGs under asymmetric faults. A dynamic reconfigurable voltage reference unit (DVRU) is designed according to the real faults. In this manner, the other DERs could comply with this DVRU to achieve both system voltage support, inrush current restraining, and power oscillation suppression. It is action signals instead of phase signals need to be transferred by the communication bus with the proposed local fault current differential time delay method (FCDDM). Thus, the low-bandwidth and the enhanced robust communication could be met under transient state. Additionally, the feasibility of the proposed method to combined with relay protection is analyzed. Finally, case study results on islanded MGs consisting of various grid-forming and grid-following DERs demonstrate the effectiveness and robustness of the proposed method under short circuit faults as well as communication failures.

Transglutaminase 2 Regulates HSF1 Gene Expression in the Acute Phase of Fish Optic Nerve Regeneration.

Fish retinal ganglion cells (RGCs) can regenerate after optic nerve lesions (ONLs). We previously reported that heat shock factor 1 (HSF1) and Yamanaka factors increased in the zebrafish retina 0.5-24 h after ONLs, and they led to cell survival and the transformation of neuro-stem cells. We also showed that retinoic acid (RA) signaling and transglutaminase 2 (TG2) were activated in the fish retina, performing neurite outgrowth 5-30 days after ONLs. In this study, we found that RA signaling and TG2 increased within 0.5 h in the zebrafish retina after ONLs. We examined their interaction with the TG2-specific morpholino and inhibitor due to the significantly close initiation time of TG2 and HSF1. The inhibition of TG2 led to the complete suppression of HSF1 expression. Furthermore, the results of a ChIP assay with an anti-TG2 antibody evidenced significant anti-TG2 immunoprecipitation of HSF1 genome DNA after ONLs. The inhibition of TG2 also suppressed Yamanaka factors' gene expression. This rapid increase in TG2 expression occurred 30 min after the ONLs, and RA signaling occurred 15 min before this change. The present study demonstrates that TG2 regulates Yamanaka factors via HSF1 signals in the acute phase of fish optic nerve regeneration.

Fault Diagnosis of Marine Diesel Engine Based on Multi-scale Time Domain Decomposition and Convolutional Neural Network

Abstract Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.

Correlation-Assisted Pixel Array for Direct Time of Flight.

Time of flight is promising technology in machine vision and sensing, with an emerging need for low power consumption, a high image resolution, and reliable operation in high ambient light conditions. Therefore, we propose a novel direct time-of-flight pixel using the single-photon avalanche diode (SPAD) sensor, with an in-pixel averaging method to suppress ambient light and detect the laser pulse arrival time. The system utilizes two orthogonal sinusoidal signals applied to the pixel as inputs, which are synchronized with a pulsed laser source. The detected signal phase indicates the arrival time. To evaluate the proposed system's potential, we developed analytical and statistical models for assessing the phase error and precision of the arrival time under varying ambient light levels. The pixel simulation showed that the phase precision is less than 1% of the detection range when the ambient-to-signal ratio is 120. A proof-of-concept pixel array prototype was fabricated and characterized to validate the system's performance. The pixel consumed, on average, 40 μW of power in operation with ambient light. The results demonstrate that the system can operate effectively under varying ambient light conditions and its potential for customization based on specific application requirements. This paper concludes by discussing the system's performance relative to the existing direct time-of-flight technologies, identifying their strengths and limitations.

A Proof-of-Concept FPGA-Based Clock Signal Phase Alignment System

Phase alignment of periodic events between multiple systems is required in multiple fields and applications. Most of the existing solutions focus on either low frequency and relatively low accuracy or high complexity, high accuracy and precision. In contrast, this work aimed to develop an intermediate solution, supporting high frequencies and relatively high accuracy and precision, with relatively low complexity. A hypothetical concept and mathematical model is presented with a hardware test implementation based entirely on FPGA resources. Deliberate resource selection and utilization enables a significant simplification of calculations and, as a result, a reduction in logic resource utilization. The proposed concept was implemented and verified using the AMD/Xilinx Artix 7 35T FPGA platform.

Dixon-based B0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging.

To develop a Dixon-based self-navigation approach to estimate and correct temporal variations in radial stack-of-stars gradient echo imaging for quantitative body MRI. The proposed method estimates temporal variations using a self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. The self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodic variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporal variations on the field-map, proton density fat fraction (PDFF) and map, and to validate the proposed method. Temporal variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation of , while PDFF mapping was less affected. The self-navigator captured slowly varying temporal drifts and temporal variations caused by respiratory motion. While the 1D correction effectively corrected drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporal variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map and and reduced image artifacts. Temporal variations particularly affect mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct for drift and physiological motion-induced variations, especially in the presence of fat.

Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD

Defects in welds can affect the structural safety and reliability of workpieces. Currently, the method of using phased array ultrasonic inspection technology for non-destructive testing of weld structures with high detection efficiency, good sensitivity, and good visualization of the results is widely used. However, the defective A-scan data collected by the ultrasonic phased array detector inevitably contain noise data, including the test piece material structure noise, equipment noise, and environmental noise, which undoubtedly affects the analysis of the A-scan signal. In addition, when defects are interpreted, the presence of noise also interferes with the process, which affects the accuracy of the interpretation. Therefore, to enhance the accuracy of defect identification based on phased array ultrasonic inspection technology, we must prevent the series of consequences caused by misjudgments. In this study, ultrasonic phased array inspection experiments were carried out, and the specific process flow of ultrasonic phased array inspection of flat plate butt welds was summarized. Utilizing pre-fabricated flat plate butt specimen blocks containing five types of typical defects, defect A-sweep signals based on ultrasonic phased array inspection were obtained. Combining the sparrow optimization algorithm (SSA), variational mode decomposition (VMD), and wavelet packet decomposition (WPD), a defect signal noise reduction method based on parameter optimization was studied. A noise reduction study was carried out using the noise-added simulated signal, and the results indicated that the noise reduction method proposed in this paper had a better noise reduction effect and the proposed method could effectively retain the detailed features of the ultrasonic phased array defective A-scan signal and realize the noise reduction processing of the defective A-scan signal.

One-stage estimation of cyclic arrival rates using license plate recognition data

Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.

Distributed Acoustic Sensing Signal Model Under Static Fiber Conditions

This paper presents a statistical model for distributed acoustic sensor interrogation units that utilizes laser pulses transmitted into fiber optics. Interactions within the fiber lead to localized acoustic energy, resulting in backscatter, which is a reflection of the light. Explicit equations were used to calculate the amplitudes and phases of backscattered signals. The proposed model accurately predicts the amplitude signal spectrum and autocorrelation, aligning well with experimental observations. This study also explores the phase signal characteristics relevant to optical time-domain reflectometry (OTDR) system sensing applications, demonstrating consistency with the experimental results. The experiments were conducted using Python coding, enabling the analysis of the individual components of the Distributed Acoustic Sensing (DAS) system. The assumptions of the model include the static condition of the fiber, implying the absence of external forces or vibrations. Consequently, no external acoustic disturbances were considered. The backscattered signal comprises a random noise component resulting from intrinsic fiber imperfections, and a coherent component arising from the interplay between the laser pulse and the fiber. Keywords: distributed acoustic sensing, fiber optics, optical time-domain reflectometry, Rayleigh scattering.

A novel capacitive sensor interface based on a simple capacitance-to-phase converter

Abstract A novel room temperature capacitive sensor interface circuit is proposed and successfully tested, which uses a modified All-Pass filter architecture combined with a simple series resonant tank circuit with a moderate Q-factor. It is fashioned from a discrete inductor with small dissipation resonating with a grounded capacitor acting as the sensing element to obtain a resolution of ∆C ~ 2 zF in a capacitance range of 10 – 30 pF. The circuit converts the change in capacitance to the change in the phase of a carrier signal in a frequency range with a central frequency set up by the tank circuit’s resonant frequency and is configured to act as a close approximation of the ideal All-Pass filter. This cancels out the effects of amplitude modulation when the carrier signal is imperfectly tuned to the resonance. The proposed capacitive sensor interface has been specifically developed for use as a front-end constituent in ultra-precision mechanical displacement measurement systems, such as accelerometers, seismometers, gravimeters and gravity gradiometers, where moving plate grounded air gap capacitors are frequently used. Some other applications of the proposed circuit are possible including the measurement of the electric field, where the sensing capacitor depends on the applied electric field, and cost effective capacitive gas sensors. In addition, the circuit can be easily adapted to function with very small capacitance values (1 - 2 pF) as is typical in MEMS-based transducers.

Investigation of Weld Seams of FFP2 Medical Mask by Air-Coupled Ultrasound

This work addresses the critical concern of ensuring the quality of FFP2 medical masks weld seam, focusing on air-coupled ultrasonic testing (ACUT) as a non-contact inspection method. Through C-scan procedures and through-transmission techniques at varying frequencies, the research explores the acoustic properties of medical mask materials and welding seams. Attenuation and signal phase shift measurements reveal insights into ultrasound wave interactions with different components, aligning with slow longitudinal waves. Comparative analyses of welding seam inspection setups identify the normal through-transmission setup as practical. The innovative 2nd derivative technique of received signal amplitudes, proves effective in defect detection, presenting potential advancements in non-destructive testing for medical masks, ensuring swift and contamination-free inspection while preserving mask integrity.

High resolution X-ray phase contrast imaging for non-destructive testing of composite materials

X-ray imaging is widely used in non-destructive testing dedicated to industry, medical or security domain. In aircraft production, the aerospace industry increasingly integrates carbon fibre reinforced polymers (CFRP), which may develop defects from operational wear, like lightning strikes. To robustly characterize density changes resulting from induced lightning damage in CFRP, multilateral shearing interferometry (MLSI) – a phase imaging technique – have been incorporated on a X-ray laboratory bench. Indeed, such method provides higher contrasts than X-ray attenuation imaging for composite materials and brings complementary information of the imaged sample. However, extracting the phase signal requires conditions of the imaging system and algorithmic strategies, which can lead to the emergence of artifacts in phase images. This thesis work involved the characterisation of these artifacts in the context of MLSI applied to X-ray phase imaging, as well as the development of tracking methods and corrective strategies.

An average-phase calibration method for the sensing matrix of a Modulated Wideband Converter

As a sub-Nyquist sampling structure based on compressed sensing (CS), a modulated wideband converter (MWC) is used for spectral blind sampling and reconstruction of multiband sparse signals. Sinusoidal signals with known phases have been theoretically used to study the calibration of the sensing matrix of MWC, but in practice, the initial phase of sinusoidal signals is unfortunately difficult to know in advance. Consequently, phase error will exist in the calibrated sensing matrix, which reduces the reconstruction signal-to-noise ratio (RSNR) of the reconstructed signal.In this paper, we first analyze the phase error uncertainty caused by the DC component of the periodic pseudo-random sequence. In addition, an average-phase calibration method based on a 0/1 mixing sequence is proposed to calibrate the sensing matrix. Each column of the sensing matrix is calibrated according to its average phase, which avoids introducing phase error when using sinusoidal signals with arbitrary unknown phases. Comparative experiments are conducted to evaluate the performance of the proposed method in terms of the RSNR and the reconstruction mean squared error (RMSE). The results show that the RSNR obtained by the proposed method is 40 dB, which is close to the signal-to-noise (SNR) of the input RF signal.

Creating a novel method for chicken primordial germ cell health monitoring using the fluorescent ubiquitination-based cell cycle indicator reporter system

The most current in vitro genetic methods, including gene preservation, gene editing and developmental modelling, require a significant number of healthy cells. In poultry species, primordial germ cells (PGCs) are great candidates for all the above-mentioned purposes, given their easy culturing and well-established freezing method for chicken. However, the constant monitoring of cultures can be financially challenging and consumes large amounts of solutions and accessories. This study aimed to introduce the Fluorescent Ubiquitination-based Cell Cycle Indicator (FUCCI) complex into the chicken PGCs. FUCCI is a powerful transgenic tool based on the periodic protein expression changes during the cell cycle. It includes chromatin licensing and DNA replication factor 1 attached monomeric Kusabira-Orange and Geminin-attached monomeric Azami-Green fluorescent proteins, that cause the cells to express a red signal in the G1 phase and a green signal in S and G2 phases. Modification of the chicken PGCs was done via electroporation and deemed to be successful according to confocal microscopy, DNA sequencing and timelapse video analysis. Stable clone cell lines were established, cryopreserved, and injected into recipient embryos to prove the integrational competency. The cell health monitoring was tested with medium change experiments, that proved the intended reactions of the FUCCI transgene. These results established the future for FUCCI experiments in chicken, including heat treatment and toxin treatment.

Solution-processed 10 nm thick ferroelectric polymer film on a TiO2 nanolayer for flexible nonvolatile memories

Poly(vinylidene fluoride)-based ferroelectric polymers, known for their exceptional flexibility, cost-effectiveness, and ease of processing, have garnered significant attention in the field of nonvolatile data storage. However, a persistent challenge lies in their elevated driving voltage and subpar thermal stability, leading to excessive power consumption and restricted operational conditions. Herein, we propose a flexible ferroelectric random access memory (FeRAM) based on a ceramic/polymer bilayer consisting of a solution-processed ferroelectric poly(vinylidene fluoride-trifluoroethylene) [P(VDF-TrFE)] nanofilm on top of a TiO2 nanolayer. The polarized Fourier transform infrared spectroscopy spectra and piezoresponse force microscopy phase signals demonstrate that the surface effect of the underlying TiO2 induces a consistent dipole orientation along the out-of-plane direction in the P(VDF-TrFE) nanofilm. These aligned dipoles in the P(VDF-TrFE) can be efficiently switched by a low driving voltage of 5 V while maintaining a high thermal stability exceeding 100 °C. Furthermore, the FeRAM based on the TiO2/P(VDF-TrFE) bilayer exhibits a remarkable storage density of approximately 60 GB in.–2, coupled with exceptional flexibility, high transparency, and superior read/write durability. These outstanding performances indicate that the TiO2/P(VDF-TrFE) bilayer holds significant potential for applications in high-density information storage within flexible electronics.

Parallel indirect time-of-flight ranging using on-chip dual-frequency combs

The significant advancements in autonomous vehicle applications demand detection solutions capable of swiftly recognizing and classifying objects amidst rapidly changing and low-visibility conditions. Light detection and ranging (LiDAR) has emerged as a robust solution, overcoming challenges associated with camera imaging, particularly in adverse weather conditions or low illumination. Rapid object recognition is crucial in dynamic environments, but the speed of conventional LiDARs is often constrained by the 2D scanning of the laser beam across the entire scene. In this study, we introduce a parallelization approach for the indirect time-of-flight (iToF) ranging technique. This method enables efficient and high-speed formation of 1D clouds, offering the potential to have extended range capabilities without being constrained by the laser coherence length. The application potential spans mid-range autonomous vehicles ranging to high-resolution imaging. It utilizes dual-frequency combs with slightly different repetition rates. The method leverages the topology of the target object to influence the phase of the beating signal between the comb lines in the RF domain. This approach enables parallel ranging in one direction, confining the scanning process to a single dimension, and offers the potential for high-speed LiDAR systems. A tri-comb approach will be discussed that can provide an extended unambiguous range without compromising the resolution due to the range–resolution trade-off in iToF techniques. The study starts by explaining the technique for parallel detection of distance and velocity. It then presents a theoretical estimation of phase noise for dual combs, followed by an analysis of distance and velocity detection limits, illustrating their maximum and minimum extents. Finally, a study on the mutual interference conditions between two similar LiDAR systems is presented, demonstrating the feasibility of designing simultaneously operating LiDARs to avoid mutual interference.

A modified dispersed frequency and phase consensus algorithm based on differential evolution and credibility weighting matrix

In distributed antenna array systems, accurate synchronization of the frequency and phase of the transmission signals among subarrays is a premise for beamforming. To solve this problem, a modified dispersed frequency and phase consensus (MDFPC) algorithm, which is based on differential evolution (DE) and the credibility weighting matrix, is proposed in this paper. DE algorithm is adopted to optimize the mixing matrix of MDFPC algorithm, and the convergence speed of consensus algorithm can be improved. Moreover, Lanczos method is adopted to calculate the fitness of individuals during optimization, thus the computation amount of the eigenvalue decomposition of the large-scale, sparse and symmetric mixing matrix can be effectively reduced. In addition, the influence of the transmission distance between subarrays on the received Signal-to-Noise Ratio (SNR) is considered, and the credibility weighting matrix can be obtained. Then the contribution of low-credible frequency and phase messages to the iterative updated values can be decreased by the credibility weighting matrix, thus more accurate frequency and phase updated values can be obtained in each iteration. It is shown from simulation results that the proposed algorithm can further reduce the phase synchronization error by at least 8% and achieve at least 18% convergence speed improvement compared to dispersed frequency and phase consensus (DFPC) algorithm.

Estimation of catalytic cracking of vacuum gas oil by ZSM-5- and β-zeolite-containing two-layered and novel three-layered hierarchical catalysts using Curie point pyrolyzer

ZSM-5- and β-zeolite-containing two-layered hierarchical catalysts (2 L-Z and 2 L-β) were prepared using a template of malic acid and novel three-layered hierarchical catalysts were also prepared combining mesoporous silica derived from the gel skeletal reinforcement (GSR) method with hexamethyldisiloxane (HMDS) - acetic anhydride (AA) as reinforcing agents. 2 L-Z and 2 L-β catalysts contained mesoporous silica of 50 wt% and microporous zeolite of 50 wt% and were called 2 L-Z-(Z50) and 2 l-β-(Z50) in which the amount of zeolite is shown in parenthesis. 3 L-co-gel and 3 L-mix, two types of three-layered hierarchical catalysts, were prepared by making GSR-silica in the presence of a 2 L catalyst for the former and by combining GSR-silica-gel with 2 L catalyst for the latter. 3 L catalysts contained 50 wt% of the 2 L catalyst and 50 wt% large-mesoporous GSR-silica and revealed three layered properties of microporous zeolite, small mesoporous silica and large mesoporous silica. Signals of zeolite crystals and amorphous phase were observed in XRD measurement for 3 L catalysts. Both smaller size of mesopores and larger size of mesopores were observed in N2 adsorption-desorption measurement. Vacuum gas oil (VGO) was catalytically cracked using 2 L and 3 L hierarchical catalysts with the use of Curie point pyrolyzer (CPP) method. ZSM-5-containing 3 L catalysts exhibited the lower conversions than corresponding 2 L catalyst, kaolin-mixed catalyst and ZSM-5 single while 3 L-co-gel-β-(Z25), which included 25 wt% of β-zeolite, exhibited the highest activity among β-zeolite-containing catalysts. The higher selectivity for p-xylene in xylene mixture was detected in catalytic cracking using 3 L-co-gel-Z-(Z25) and 3 L-mix-Z-(Z25) catalysts. When the relative activity against XRD integral intensity and amounts of acid sites was plotted against average pore diameter derived from N2 adsorption-desorption measurement, the strong correlation was observed, suggesting that the intrinsic activity of active sites of zeolite could be related to the pore size of the whole catalyst.

Determining beam transverse absolute position by triangulation of multi-electrode signal phase differences

Determining beam transverse absolute position by triangulation of multi-electrode signal phase differences

Tag-free indoor fall detection using transformer network encoder and data fusion

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96.5\\%$$\\end{document}. This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.