Frequency Domain Phase Research Articles

DAC phase and amplitude noise experimental studies in RZ, NRZ and RF modes

Amplitude and duration of pulses generated by current cells of multimode DAC may differ in real DACs. In this case the problem arises of current pulse parameters defining, their relationship with frequency domain response and noise parameters of DAC, which can be experimentally determined. The article describes the measurements of frequency domain response, phase noise and amplitude noise of multimode DAC output signal in RZ, NRZ and RF (mixed) mode. Phase noise and amplitude noise of multimode DAC in RZ and RF modes calculation uses experimental data obtained in RZ mode. Measurement and consequent calculation of AD9739 DAC phase and amplitude noise confirmed the theoretical analysis.

Defect detection and imaging using electromagnetic acoustic transducer with butterfly coil.

Electromagnetic ultrasonic detection technology utilizes the electromagnetic coupling method to generate and receive ultrasonic waves without a couplant, which is suitable for rapid detection. However, the detection can be affected by the spatial distribution of the acoustic field and the polarization direction of the shear wave, which can result in suboptimal detection performance. The acoustic field directivity of the shear wave generated by the butterfly coil electromagnetic acoustic transducer was measured using the transmission method. The data indicate that the acoustic pressure amplitude of the shear wave is maximized along the axis of the acoustic field, thereby meeting the requirements of synthetic aperture focusing technique imaging. We used the reflection method to detect the through-hole defects and investigated the effect of shear wave polarization direction. By comparing the experimental data and imaging results, it can be concluded that higher echo amplitudes are obtained when the polarization direction of the shear wave is perpendicular to the axis of the through-hole defects. Based on the explosive reflection model, the frequency domain phase shift migration (PSM) method converts the time-domain signal to the frequency domain for processing and uses a phase-shift factor for layer-by-layer imaging. We used the PSM method to process the experimental data, which not only produced high-resolution images but also had a high computational speed.

Laser ultrasonic frequency-domain imaging and phase weighted optimization based on full matrix capture

Far-field laser technology has greatly promoted the progress of nondestructive ultrasonic imaging of bulk structures. However, under thermoelastic excitation, the body waves exhibit a relatively low signal-to-noise ratio, resulting in images with low resolution and contrast. Based on the motivation, this paper developed a frequency-domain phase weighted imaging method to improve the quality of laser ultrasonic defect imaging. Firstly, laser ultrasonic scanning was performed on the sample with artificial transverse hole defects. The cylindrical lens focused line source was used to improve the intensity of the body wave signals, and ensure that there was no damage on the material surface under high laser energies. Then, the frequency-domain phase shift migration (PSM) algorithm was used to perform multimode imaging of defects, achieving frequency-domain synthetic aperture focusing technique (F-SAFT) and total focused method (F-TFM) imaging based on full matrix capture. Furthermore, the phase circular statistical vector (PCSV) was proposed for weighted optimization, which improved the image quality, suppressed the background noise and multimode artifacts. Finally, the imaging quality of several algorithms were discussed. The results indicate that frequency-domain images were superior to time-domain results. After phase weighting, the imaging quality can be further improved, and the detection blind zone was significantly reduced. This work will contribute to the rapid and high-quality defect imaging of laser ultrasonic.

Defect detection in additively manufactured AlSi10Mg and Ti6Al4V samples using laser ultrasonics and phase shift migration

Laser ultrasonics (LU) is a non-contact and non-destructive method with a high data acquisition rate, making it a promising candidate for in-situ monitoring of defects in different additive manufacturing (AM) processes, including laser powder bed fusion (LPBF) and directed energy deposition, as well as final part inspection. In order to see the effect of various artificial defect types on an LU sub-surface reconstruction, AlSi10Mg samples with side through-holes, as well as Ti6Al4V samples with bottom blind holes and trapped powder were printed using LPBF, and then ultrasound B-scans of the samples were obtained using an LU system. The resulting scan data was processed using a custom frequency domain phase shift migration (PSM) algorithm, to reconstruct the defects and their locations. Novel ways of pre-processing the B-scan, used as an input to PSM, and taking advantage of its frequency representation, are demonstrated. Newton’s method was used to find a stationary phase approximation, used to account in the frequency domain for the fixed offset emitter-receiver arrangement within the PSM calculation. The Newton’s method calculation time was reduced by 33%, by using an approximation of the phase function to find an initial guess. The smallest defects that were detected using this method were in the size range between 200 to 300μm for the bottom hole defects, using an 8 ns laser pulse duration. The effect of the laser on the surface of a part being built, and the challenges and further work needed to integrate LU in a LPBF machine for in-situ inspection are discussed.

Volumetric T2 -weighted spin echo imaging with improved SNR using localized quadratic encoding and a spiral readout trajectory.

To demonstrate T2 -weighted (single-echo) spin-echo (SE) imaging with near-optimal acquisition efficiency by applying SNR-efficient RF slice encoding and spiral readout. A quadratic-phase (frequency swept) excitation RF pulse replaced the conventional excitation in T2 -weighted SE sequence to excite a thick slab that is internally spatially encoded by a variable phase along the slice direction. Highly overlapping slabs centered at every desired slice location were acquired in multiple passes, such that the entire imaging volume was excited by contiguous slabs in any given pass. Following 90° excitation, each slab was refocused with a conventional 180° RF to produce a SE signal, followed by a spiral in-out readout. A noise-insensitive reconstruction removed the quadratic phase in the spatial frequency domain, yielding desired slice resolution and improved SNR. Increasing the RF frequency sweep (hence, excitation width) allowed more frequent encoding of each slice over the multiple passes, improving final image SNR, until crosstalk ensued at excessive slab widths compared to their center-to-center spacing. With an optimized slab width, the proposed technique used all passes to acquire every prescribed slice, with substantially improved SNR over conventional SE or 2D-turbo-spin-echo (TSE) scans. Quantitative SNR measurements indicated similar SNR as 3D-TSE, but radiologist scoring favored 3D-TSE, mainly because of spiral-related artifacts and possibly because of regularized reconstructions in 3D-TSE. Using SNR-efficient slice excitation scheme and spiral readout helped eliminate SNR and temporal inefficiencies in conventional T2 -weighted imaging, yielding SNR independent of TR or number of passes.

Multi-domain-fusion deep learning for automatic modulation recognition in spatial cognitive radio

Automatic modulation recognition (AMR) is a critical technology in spatial cognitive radio (SCR), and building high-performance AMR model can achieve high classification accuracy of signals. AMR is a classification problem essentially, and deep learning has achieved excellent performance in various classification tasks. In recent years, joint recognition of multiple networks has become increasingly popular. In complex wireless environments, there are multiple signal types and diversity of characteristics between different signals. Also, the existence of multiple interference in wireless environment makes the signal characteristics more complex. It is difficult for a single network to accurately extract the unique features of all signals and achieve accurate classification. So, this article proposes a time–frequency domain joint recognition model that combines two deep learning networks (DLNs), to achieve higher accuracy AMR. A DLN named MCLDNN (multi-channel convolutional long short-term deep neural network) is trained on samples composed of in-phase and quadrature component (IQ) signals, to distinguish modulation modes that are relatively easy to identify. This paper proposes a BiGRU3 (three-layer bidirectional gated recurrent unit) network based on FFT as the second DLN. For signals with significant similarity in the time domain and significant differences in the frequency domain that are difficult to distinguish by the former DLN, such as AM-DSB and WBFM, FFT (Fast Fourier Transform) is used to obtain frequency domain amplitude and phase (FDAP) information. Experiments have shown that the BiGUR3 network has superior extraction performance for amplitude spectrum and phase spectrum features. Experiments are conducted on two publicly available datasets, the RML2016.10a and RML2016.10b, and the results show that the overall recognition accuracy of the proposed joint model reaches 94.94% and 96.69%, respectively. Compared to a single network, the recognition accuracy is significantly improved. At the same time, the recognition accuracy of AM-DSB and WBFM signals has been improved by 17% and 18.2%, respectively.

Fractional-order PID controller design via optimal selection strategy of frequency domain specifications

In this paper, a novel fractional-order Proportional-Integral Derivative (PID) controller design depending on optimal selection of frequency domain specifications is proposed for time delay systems. The frequency domain specification sets, namely, (i) phase margin and gain crossover frequency and (ii) phase margin and gain margin are determined so that the reference model is optimal according to three time domain performance indices, i.e. Integral Square Error (ISE), Integral Time Square Error (ITSE) and Integral Absolute Error (IAE). Here, the delayed Bode's ideal transfer function is employed in the reference model. Moreover, the stability region of the reference model is given via a theorem. In simulation studies, the proposed methodology is compared with other two different methods using the same frequency domain specifications. It is observed that the proposed optimal fractional-order PID controllers outperform according to the mentioned performance indices, and they also possess considerably acceptable performance in terms of other time domain specifications such as overshoot, settling time, etc.

Periodic Traction Force Fluctuations Suppression Strategy of Maglev Train Based on Flux Linkage Observation and Harmonic Current Injection

To suppress the periodic traction force fluctuations of maglev train caused by the current harmonics and the excitation flux linkage harmonics of long stator linear synchronous motor (LSLSM), a strategy based on flux linkage observation and harmonic current injection is proposed in this paper. First, the mathematical models of the LSLSM in single feed (SF) and double feed (DF) modes are established and simplified. Then, considering the characteristics of variable frequency and phase lag of the LSLSM, an improved discrete resonant controller (RSC) with pole correction and whole frequency domain phase compensation is designed to suppress the traction force fluctuation caused by current harmonics. To suppress the traction force fluctuations caused by the flux linkage harmonics, a sliding mode flux linkage observer (SMFO) is designed and combined with an adaptive notch filter (ANF) to estimate specific flux linkage harmonics in real-time. By analyzing the flux linkage component in the traction force equation, appropriate harmonic currents are injected and tracked by the RSC. Hardware-in-the-loop (HIL) experiments verify the effectiveness of the proposed strategy.

Time-Frequency Range Estimation Method for Pulsed LiDAR

Light detection and ranging (LiDAR) is the key sensor behind the 3D perception of self-driving vehicles. The range, precision and accuracy of a LiDAR sensor mainly depend on how each received pulse is captured, sampled and processed. However, to the best of our knowledge, such a topic is yet to be investigated in depth. This work aims to fill such a gap by introducing a new range estimation method. The method takes only 3 samples from the rising edge of the received pulse and combines two simple estimation techniques: time-domain edge detection and frequency-domain phase estimation. The proposed method was demonstrated over a dynamic range of 45 dB, proving to be robust over the entire interval. Sub-centimeter precision and accuracy were achieved over a dynamic range of 24 dB. The obtained estimations are bounded by a derived theoretical limit that is identical to a Cramér-Rao lower bound of a phase estimator.

Deterministic learning-based neural network control with adaptive phase compensation

Under the persistent excitation (PE) condition, the real dynamics of the nonlinear system can be obtained through the deterministic learning-based radial basis function neural network (RBFNN) control. However, in this scheme, the learning speed and accuracy are limited by the tradeoff between the PE levels and the approximation capabilities of the neural network (NN). Inspired by the frequency domain phase compensation of linear time-invariant (LTI) systems, this paper presents an adaptive phase compensator employing the pure time delay to improve the performance of the deterministic learning-based adaptive feedforward control with the reference input known a priori. When the adaptive phase compensation is applied to the hidden layer of the RBFNN, the nonlinear approximation capability of the RBFNN is effectively improved such that both the learning performance (learning speed and accuracy) and the control performance of the deterministic learning-based control scheme are improved. Theoretical analysis is conducted to prove the stability of the proposed learning control scheme for a class of systems which are affine in the control. Simulation studies demonstrate the effectiveness of the proposed phase compensation method.

A Low-Complexity SLM PAPR Reduction Scheme for OFDMA

In orthogonal frequency division multiplexing (OFDM) systems, selected mapping (SLM) techniques are widely used to minimize the peak to average power ratio (PAPR). The candidate signals are generated in the time domain by linearly mixing the original time-domain transmitted signal with numerous cyclic shift equivalents to reduce the amount of Inverse Fast Fourier Transform (IFFT) operations in typical SLM systems. The weighting factors and number of cyclic shifts, on the other hand, should be carefully chosen to guarantee that the elements of the appropriate frequency domain phase rotation vectors are of equal magnitude. A low-complexity expression is chosen from among these options to create the proposed low-complexity scheme, which only requires one IFFT. In comparison to the existing SLM technique, the new SLM scheme achieves equivalent PAPR reduction performance with significantly less computing complexity. MATLAB tool is used for simulating the proposed work.

Practical Assessment of Real‐Time Suspended Sediment Load Monitoring Using Time Domain Reflectometry

AbstractSuspended sediment concentration (SSC) and sediment load are essential factors in water resource management, hydraulic engineering, soil and water conservation, sedimentation, and environmental ecology. A novel real‐time SSC monitoring approach involving time domain reflectometry (TDR) was proposed. The previous results obtained indicated that the soil particle size does not affect the TDR SSC method and that this method is applicable at high suspended sediment concentrations (≥600,000 ppm). To further improve the measurement stability and accuracy of this method for long‐term monitoring of rivers and reservoirs, several practical problems were solved in this study, including noise interference, probe fouling, and the effect of ambient temperature on the TDR device used. With the proposed improvements in the analysis of the modified frequency domain phase velocity, the new method was examined in a laboratory setting, and a measurement standard deviation of 1,000 ppm was found. In addition, a novel carbon fiber rod with a novel detachable probe was proposed to decrease the nonlinear SSC variations resulting from the coating effect as part of the electromagnetic sensing field. As observed during the experiment, the use of a fully digital TDR device decreased the ambient temperature effect. Several TDR SSC measurements were performed at the Shihgang Reservoir, revealing long‐term SSC and sediment load typhoon events from 2013 to 2021. Subsequently, real‐time TDR SSC monitoring with sediment load quantification was performed to establish the relationship between sediment load and outflow discharge, and the results obtained indicated that this method helped characterize suspended sediment load.

Diagnostic and prognostic performance of Mxa and transfer function analysis-based dynamic cerebral autoregulation metrics

Dynamic cerebral autoregulation is often assessed by continuously recorded arterial blood pressure (ABP) and transcranial Doppler-derived mean cerebral blood flow velocity followed by analysis in the time and frequency domain, respectively. Sequential correlation (in the time domain, yielding e.g., the measure mean flow index, Mxa) and transfer function analysis (TFA) (in the frequency domain, yielding, e.g., normalised and non-normalised gain as well as phase in the low frequency domain) are commonly used approaches. This study investigated the diagnostic and prognostic performance of these metrics. We included recordings from 48 healthy volunteers, 19 patients with sepsis, 36 with traumatic brain injury (TBI), and 14 patients admitted to a neurorehabilitation unit. The diagnostic (between healthy volunteers and patients) and prognostic performance (to predict death or poor functional outcome) of Mxa and the TFA measures were assessed by area under the receiver-operating characteristic (AUROC) curves. AUROC curves generally indicated that the measures were ‘no better than chance’ (AUROC ∼0.5) both for distinguishing between healthy volunteers and patient groups, and for predicting outcomes in our cohort. No metric emerged as superior for distinguishing between healthy volunteers and different patient groups, for assessing the effect of interventions, or for predicting mortality or functional outcome.

A case study in controlled source electromagnetism: Near seabed hydrocarbon seep systems of Coal Oil Point, California, USA

Marine hydrocarbon seeps are found on all continental margins and release significant amounts of greenhouse gases and hydrocarbons into the atmosphere and hydrosphere. Most methods of studying seeps rely on seafloor or near-seafloor observations, but seepage rates and seep locations can be variable, leading to uncertainty. We exploit the fact that hydrocarbons are electrically resistive compared with surrounding sediments and use marine electromagnetic methods to study the deeper sources and accumulation sites, which are controlled by local geology and should be more stable than the seabed expressions. Our surface towed marine controlled source electromagnetic system used a horizontal electric dipole transmitter and floating electric dipole receivers spaced 100–400 m from the transmitter, collecting frequency domain amplitude and phase data at ∼2 Hz and harmonics. The survey targeted known and inferred hydrocarbon seeps within the Coal Oil Point seep field offshore Santa Barbara, California, USA. Two dimensional inversions of the data indicate that the method is sensitive to the shallowly buried (<400 m) structure of the marine hydrocarbon seeps and is an efficient and effective tool in their identification and characterization. The results show spatial variability of seafloor hydrocarbons along the Coal Oil Point seep field and indicate at least two previously unidentified intermediate depth accumulation sites. The depth and lateral extent of these hydrocarbon accumulation sites may improve seep emission models for the Coal Oil Point seep field.

Mixed Numerology Interference Recognition Approach for 5G NR

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.

Effectiveness of energy separation-based instantaneous frequency estimation for cochlear cepstral features for synthetic and voice-converted spoofed speech detection

In this article, we propose Cochlear Filter Cepstral Coefficient-Instantaneous Frequency feature set using Energy Separation Algorithm (CFCCIF-ESA) feature set to detect the speech synthesis (SS) and voice conversion (VC)-based spoofing attacks. The SS- and VC-based spoof generation techniques predominantly uses the magnitude spectrum information, neglecting the phase information. Hence, SS and VC generated speech signal possess the distorted phase in time or frequency-domain. In this work, we exploit this anomaly in phase to efficiently detect the spoofing attack. Here, instantaneous frequency (IF) is utilized to represent the phase information as IF is nothing but the derivative of unwrapped instantaneous (analytic) phase. The experiments are performed on ASVSpoof-2015 challenge dataset, which is specifically designed to do Spoof Speech Detection (SSD) task for SS and VC. In ASVSpoof-2015 challenge during INTERSPEECH 2015, SSD system designed using Cochlear Filter Cepstral Coefficient-Instantaneous Frequency (CFCCIF) feature set was the relatively best performing system. The CFCCIF feature set composed of the information obtained from the magnitude envelope derived using cochlear filterbank and instantaneous frequency (IF) which is derived from Hilbert transform-based approach. However, Hilbert transform-based estimation requires a speech segment of 10–30 ms and thus, it limits time resolution of IF estimation and hence, defeats the key objective of IF estimation to be able to fit the frequency of a sinusoid (corresponding to a monocomponent signal) locally and almost instantaneously. Energy Separation Algorithm (ESA) is known to accurately estimate the modulation patterns due to their relatively low computational complexity, high time resolution, and instantaneously adapting nature. To that effect, we exploit the ESA instead of Hilbert transform to estimate the IFs of the subband filtered signal using cochlear filterbank. The significant improvement in performance, in particular, relative reduction of 51.21% and 46.87% in EER is observed on development and evaluation subsets, respectively, for CFCCIF-ESA feature set over its CFCCIF counterpart, using Gaussian Mixture Model (GMM)-based classifier. This improvement in the performance indicates that the IFs estimated using ESA-based approach are able to efficiently capture the artefacts produced in the instantaneous phase by the SS- and VC-based spoof signals. Furthermore, experiments are also performed with convolutional neural network (CNN) classifier, which further enhances the performance.

Correlation-Based Out-of-Plane Displacement Measurement for Optical Fiber Material

Due to the monitoring requirement of optical fiber industrial production, an out-of-plane displacement measurement method is proposed. Firstly, the in-plane displacements between two consecutive images, captured through a microscope with a CCD camera, are estimated by the Digital Speckle Correlation Method (DSCM). Subsequently, the out-of-plane displacement of optical fiber material can be obtained by the wedge model. Finally, the effectiveness of the method is verified experimentally by comparing the measurement data of routine practice with its theoretical values. Simulation and experimental results indicate that the absolute errors and the relative errors of the measurement by the optical microscope with a magnification of 50× are less than ±0.2 μm and 5%, respectively. The new method only needs two images obtained by the microscopic imaging system with a single camera to accomplish the measurement, which can significantly reduce the measurement time and complexity of the arrangement. Further, the method needs neither frequency domain conversion nor phase unwrapping operation, therefore, it is especially suitable for dynamic out-of-plane displacement measurement. The proposed method has been applied to the industrial uniformity and micro–nano-scale deformation monitoring of optical fiber image transmission materials.

Beam-Steerable Transponder Based on Antenna Array and Phased Modulators

This letter presents a novel concept of beam-steerable transponder based on antenna array and phased modulators. The steerability of the beam is achieved by optimally delaying the modulated reflected signal at each antenna element (i.e., changing the phase in frequency domain) with load modulation technique. In this manner, the reflected backscattered signal power can be maximized at one particular direction. The approach makes transponder to collect ambient waves from any direction and further modulate and redirect it to any other direction. The results obtained through experimentation proves the feasibility of omnidirectional transponder and pave way for future lower power smart devices. The transponder functions in the Wi-Fi frequency band of 2.4 GHz.

Guidelines for generalised quantitative frequency domain design of optimised multi‐resonant AC current regulators for grid‐connected inverters

This study establishes a set of frequency-domain phase margin-oriented quantitative design guidelines for deriving coefficients of optimised multi-resonant AC current regulators, consisting of series-connected terms. Such an arrangement allows straightforward extension of existing design guidelines for developing coefficients of optimised single-resonant AC current regulators. Practical restrictions imposed by, relatively, low switching frequency are also considered, yielding corresponding design guidelines generalisation.

Application of fractional calculus to the ultrasonic characterization of human bone tissue

Transient ultrasonic propagation in human bone tissue is considered according to Biot's theory. The bone is modeled as a porous medium with an elastic structure. The viscous fluid/structure interactions are described by fractional calculus in the time domain. The slow and fast compressional waves, as well as the rotational shear wave predicted by Biot's theory obey fractional propagation equations. The fractional equation system is solved analytically in the time domain, thus obtaining the expressions of the reflection and transmission scattering operators. Inverse identification of the intrinsic microstructure of the pores and of the mechanical properties of the bone is performed in the time domain (waveforms) and frequency domain (attenuation and phase velocities), by adopting a statistical Bayesian inference technique using ultrasonic transmitted and reflected signals, which allows to find the identified parameters and their associated uncertainty.