Phase Information Research Articles

A positive semi definite tensor factorization method for separation of non stationary noise sources

In the aircraft industry, noise mitigation has emerged as an increasingly pressing issue, underscoring the critical importance of advancing our understanding of noise origins within turbofan engines. This paper presents the application of Positive Semi Definite Tensor Factorization (PSDTF), a potential method for the analysis of engine static tests conducted with far-field microphone arrays. By extending the capabilities of Non-negative Matrix Factorization (NMF), PSDTF offers an effective algorithm for source separation. Leveraging on cross spectral matrices to harness phase information across microphones, this approach aims at separating the contributions of several noise sources, avoiding the need for a precise acoustical model (sound propagation, source directivity, etc.). Experimental findings on a controlled experiment demonstrate the superiority of PSDTF over conventional NMF variants in achieving higher-quality source separation.

Enhancement of time-dependent coherent backscattering: Leveraging phase information

Enhancement of time-dependent coherent backscattering: Leveraging phase information

A complex neural network model by Hilbert Transform

The phase information of the optical wave plays a vital role in processing wave-related signals. In deep learning fields, complex-valued neural networks are put forward on the concept of complex amplitudes for full utilization of phase information. To build a complex-valued neural network, the common way is to exploit Fourier Transform of the observed signal to extract amplitude and phase information. However, this will lead to spectrum waste for a real-valued signal by introducing negative frequencies that have no physical meaning. To this end, we attempt to use Hilbert Transform as an alternative to yield a single sideband spectrum and avoid negative frequencies from interacting with positive ones. On the other hand, Fourier transform is a global analysis thus it tells nothing about the time domain. As our key insight, we further explore the usage of instantaneous frequency calculated by Hilbert Transform and propose a new method of constructing complex input from a time–frequency angle. Simple pixel-wise classification experiments are carried out on two hyperspectral datasets and MNIST dataset. Experimental results have demonstrated that Hilbert Transform with instantaneous frequency performs better by a large margin than Fourier Transform owing to the additional time information.

Phase Demodulation under Ultra-low sampling rate in heterodyne coherent [formula omitted]-OTDR

Heterodyne coherent phase-sensitive optical time-domain reflectometry(Φ-OTDR) requires a higher data sampling rate to demodulate phase in the “distance” direction effectively. Aiming at the problem of large amount of demodulated data, we propose a phase demodulation method that can effectively recover disturbance information from ultra-low sampling data. This method demodulates the phase of the two-dimensional reconstructed signal in the “time” direction. Thus, the influence of spectrum aliasing in the “distance” direction caused by undersampling is avoided. The undersampling rate is not limited by the spectrum aliasing effect of the detected signal when using this method to demodulate phase. Therefore, accurate phase information can be retrieved under ultra-low sampling rates, significantly reducing the data for phase demodulation in heterodyne coherent Φ-OTDR. In the experiment, three sampling rates (100 MSa/s, 10 MSa/s, 1 MSa/s) within the restricted area of the traditional undersampling demodulation method were selected to acquire data, and the proposed method can also accurately demodulate phase information.

Near-field terahertz electro-optical imaging based on a polarization image sensor

Abstract This paper presents a hyperspectral microscopy system that offers two-dimensional (2D) measurement of the spectral phase and amplitude information of terahertz (THz) radiation without the need for raster scanning. To achieve this, a new THz imaging method is introduced, wherein the distribution of the THz electric field is spatially measured using the electro-optic effect with a commercial polarization image sensor. This method enables the direct measurement of polarization components, eliminating the need for the polarization optics usually required in conventional electro-optical imaging. The performance of this imaging method is compared with a conventional 2D imaging system based on a standard visible camera. Finally, the sub-wavelength resolution capabilities of this new sensor are demonstrated by imaging a sample in the near field.

Localization of partial electrical discharges using compressive spherical frequency-difference beamforming.

Accurate localization of partial electrical discharges is essential for the diagnosis of high-voltage systems. The current study achieves this by employing an acoustic sensor array and a beamforming approach. The occurrence of a partial discharge is accompanied by the emission of high-frequency sounds in the ultrasonic range, making localization a challenging task requiring many sensors to avoid spatial aliasing. Compressive frequency-difference beamforming, as previously proposed, can be effective in addressing this issue. We expand the method to include near-field localization by utilizing a spherical wave and propose a two-step normalization process. This eliminates the bias associated with nonplanar waves and standardizes the field variables, thereby preserving only the phase and relative amplitude information. A distributed algorithm based on the alternating direction multiplier method is used to solve the associated convex optimization problem. The proposed method is demonstrated using simulated and experimental data.

Extreme Space Weather Impacts on GNSS Timing Signals for Electricity Grid Management

AbstractExtreme space weather events can have serious impacts on critical infrastructure, including Global Navigation Satellite Systems (GNSS). The use of GNSS, particularly as sources of accurate timing signals, is becoming more widespread, with one example being the measurement of electricity grid frequency and phase information to aid grid management and stability. Understanding the likelihood of extreme space weather impacts on GNSS timing signals is therefore becoming vital to maintain national electricity grid resilience. This study determines critical intensity thresholds above which the complete failure of a GNSS based timing system may occur. Solar radio bursts are identified as a simple example to investigate in more detail. The probability of occurrence of an extreme space weather event with an intensity equal to or greater than the critical intensity is estimated. Both a power law and extreme value theory were used to evaluate recurrence probabilities based on historical event frequencies. The probability was estimated to be between 3%–12% per decade to cause the complete failure of any GNSS‐based timing system.

Phase spectrogram of EEG from S-transform Enhances epileptic seizure detection

Automatic epilepsy seizure detection has high clinical value since it can alleviate the burden of manual monitoring. Nevertheless, it remains a technically challenging task to achieve a reliable system. In this study, we investigated the significance of the phase information in EEG signals in seizure detection using machine learning. We used the Stockwell transform (S-transform) to extract both phase and power spectra of the EEG signal in epilepsy patients. A dual-stream convolution neural network (CNN) model was adopted as the classifier, which takes both spectra as inputs. We demonstrated that the phase input allows the CNN model to capture the heightened phase synchronization among EEG channels in seizure and add network attention to both the low- and high-frequency features of the inputs in the CHB-MIT and Bonn databases. We improved the detection AUC-ROC by 6.68% on the CHB-MIT database when adding phase inputs to the power inputs. By incorporating a channel fusion post-processing to the outputs of this CNN model, it achieves a sensitivity and specificity of 79.59% and 92.23%, respectively, surpassing some of the state-of-the-art methods. Our results show that the phase inputs are useful features in seizure detection. This discovery has significant implications for improving the effectiveness of automatic seizure detection systems.

Unveiling the potential of operating time in improving machine learning models’ performance for waste biomass gasification systems

Biomass is a promising renewable energy source, especially regarding local energy production. Gasification is an outstanding thermochemical process for syngas production from biomass. The optimal modeling and control of syngas production from biomass gasification is vital in planning small-scale energy supply. Artificial intelligence methods are widely applied to reveal the nonlinear relations between the gasification parameters and the reaction products. In this study, special attention is paid to the effect of operating time as a predictor on the performances of the models formed, as its introduction provides the phase information to the model which allows the combination of different sequential experimental data. Unlike most preceding studies in which hold-out is preferred, nested cross-validation is utilized for model evaluation emphasizing the unbiased nature of the latter method. Least squares, gaussian kernel, generalized additive models, random forest, and artificial neural networks (ANN) are utilized to model the process's product syngas composition and high heating value (HHV). The significance of operating time on the gasification system is also reflected on the feature importance analysis which is performed. Along with the operating time, temperature is also shown to play a vital role on the hydrogen yield and calorific value of the products.

The Design of a Long-Distance Signal Transmission System in a Bistatic RCS Testing System

In wireless communication and radar systems, long-distance signal transmission poses significant challenges that affect overall system performance. In this paper, we propose an innovative bistatic radar cross section (RCS) testing system designed to address these challenges, with a particular focus on its long-distance signal transmission capabilities. This system is capable of accurately measuring the RCS of a target and improving multipath channel modeling accuracy by using precise RCS values. The integrated upper computer software extracts amplitude and phase information from received echo signals, processes these data, and provides detailed outputs including the target’s RCS, one-dimensional image, and two-dimensional image. The experimental results obtained prove that this system can not only achieve effective long-distance signal transmission but also substantially enhance the accuracy of RCS measurements, offering reliable support for multipath channel modeling. However, the conclusions drawn are preliminary and require further experimental validation to fully substantiate this system’s performance. Future work will focus on improving system accuracy, minimizing the impact of environmental noise, and optimizing data-processing methods to enhance the efficiency of wireless communication and radar applications.

Linear cooling of a levitated micromagnetic cylinder by vibration

We report feedback cooling of translational and librational degrees of freedom of a levitated m icromagnet cylinder, utilizing a piezoelectric actuator to apply linear feedback to high-Q mechanical modes. The normal modes are measured with a superconducting pick-up coil coupled to a dc SQUID, and phase information is fed back to the piezoelectric actuator to feedback cool a center-of-mass mode to ∼7K, and a librational mode to 830±200mK. Q-factors of 1.0×107 are evaluated for the center-of-mass mode. We find that it is plausible to achieve ground state cooling of the center-of-mass mode by introducing vibration isolation, optimizing the geometry of the pick-up coil to focus on the specific mode of interest and utilizing a state-of-the-art SQUID for detection. Published by the American Physical Society 2024

Fast autofocusing in off-axis digital holography based on search region segmentation and dichotomy

Autofocusing is a critical step in digital holography for reconstructing high-quality intensity and phase information, especially for the dynamic detection of moving objects. Some commonly used autofocusing methods in digital holography are time-consuming as they search over a large range to find a unique reconstruction distance. In this study, we propose a new fast autofocusing method based on search region segmentation and dichotomy. First, a new search region is obtained by segmenting the original region and solving a system of inequality equations. Second, improving the focusing evaluation function and combining this function with the dichotomy method transforms the discriminating minimum of the evaluation function into the detection of the zero-crossing position, reducing the number of searches required for the autofocusing method. The effectiveness of the proposed autofocusing method is validated through numerical simulations and experiments. The proposed method can significantly improve computational efficiency while guaranteeing the detection accuracy of the focusing plane compared with conventional methods.

Tag-Array-Based UHF Passive RFID Tag Attitude Identification of Tracking Methods.

Attitude information is as important as position information in describing and localizing objects. Based on this, this paper proposes a method for object attitude sensing utilizing ultra-high frequency passive RFID technology. This method adopts a double tag array strategy, which effectively enhances the spatial freedom and eliminates phase ambiguity by leveraging the phase difference information between the two tags. Additionally, we delve into the issue of the phase shift caused by coupling interference between the two tags. To effectively compensate for this coupling effect, a series of experiments were conducted to thoroughly examine the specific impact of coupling effects between tags, and based on these findings, a coupling model between tags was established. This model was then integrated into the original phase model to correct for the effects of phase shift, significantly improving the sensing accuracy. Furthermore, we considered the influence of the object rotation angle on phase changes to construct an accurate object attitude recognition and tracking model. To reduce random errors during phase measurement, we employed a polynomial regression method to fit the measured tag phase information, further enhancing the precision of the sensing model. Compared to traditional positioning modes, the dual-tag array strategy essentially increases the number of virtual antennas available for positioning, providing the system with more refined directional discrimination capabilities. The experimental results demonstrated that incorporating the effects of inter-tag coupling interference and rotation angle into the phase model significantly improved the recognition accuracy for both object localization and attitude angle determination. Specifically, the average error of object positioning was reduced to 12.3 cm, while the average error of attitude angle recognition was reduced to 8.28°, making the method suitable for various practical application scenarios requiring attitude recognition.

Cosmological correlators with double massive exchanges: bootstrap equation and phenomenology

Using the recently developed cosmological bootstrap method, we compute the exact analytical solution for the seed integral appearing in cosmological correlators with double massive scalar exchanges. The result is explicit, valid in any kinematic configuration, and free from spurious divergences. It is applicable to any number of fields’ species with any masses. With an appropriate choice of variables, the results contain only single-layer summations. We also propose simple approximate formulas valid in different limits, enabling direct and instantaneous evaluation. Supported by exact numerical results using CosmoFlow, we explore the phenomenology of double massive exchange diagrams. Contrary to single-exchange diagrams with ubiquitous Lorentz-covariant interactions, the size of the cubic coupling constant can be large while respecting perturbativity bounds. Because of this property, the primordial bispectrum from double-exchange diagrams can be as large as, coincidentally, current observational constraints. In addition to being sizable on equilateral configurations, we show that the primordial bispectrum exhibits a large cosmological collider signal in the squeezed limit, making the double massive exchanges interesting channels for the detection of massive primordial fields. We propose to decisively disentangle double-exchange channels from single-exchange ones with cosmological observations by exploiting the phase information of the cosmological collider signal, the inflationary flavor oscillations from multiple fields’ species exchanges and the double soft limit in the primordial trispectrum.

Fiber optic magnetic field sensor based on a magnetic-fluid-induced phase-shift FLRD

A magnetic field sensing system based on a phase-shift fiber loop ring-down (FLRD) technique and multi-mode interferometer (MI) coated with magnetic fluid (MF) is proposed and demonstrated. The MI is constructed by splicing a segment of no-core fiber between two sections of single-mode fibers, which is then immersed in MF to serve as a sensing head with the advantages of simple fabrication and specific magnetic sensitivity. Due to the magnetic refractive index tunable properties of the MF, the magnetic-field-dependent loss will be introduced in the fiber loop by the sensing head. Such magnetic-induced loss would be accumulated during the round trip of the optical carrier and reflected on the phase information of the modulated signal. The phase-shift changes with the applied magnetic field strength, enabling magnetic field sensing through phase-shift measurements. The sensing system is experimentally demonstrated and a sensitivity of 0.704×10−3deg/Gs in the linear region is achieved. Moreover, the stability and repeatability of the system are verified, leading to a promising method for magnetic field measurements.

Multi-Domain Joint Synthetic Aperture Radar Ship Detection Method Integrating Complex Information with Deep Learning

With the flourishing development of deep learning, synthetic aperture radar (SAR) ship detection based on this method has been widely applied across various domains. However, most deep-learning-based detection methods currently only use the amplitude information from SAR images. In fact, phase information and time-frequency features can also play a role in ship detection. Additionally, the background noise and the small size of ships also pose challenges to detection. Finally, satellite-based detection requires the model to be lightweight and capable of real-time processing. To address these difficulties, we propose a multi-domain joint SAR ship detection method that integrates complex information with deep learning. Based on the imaging mechanism of line-by-line scanning, we can first confirm the presence of ships within echo returns in the eigen-subspace domain, which can reduce detection time. Benefiting from the complex information of single-look complex (SLC) SAR images, we transform the echo returns containing ships into the time-frequency domain. In the time-frequency domain, ships exhibit distinctive features that are different from noise, without the limitation of size, which is highly advantageous for detection. Therefore, we constructed a time-frequency SAR image dataset (TFSID) using the images in the time-frequency domain, and utilizing the advantages of this dataset, we combined space-to-depth convolution (SPDConv) and Inception depthwise convolution (InceptionDWConv) to propose Efficient SPD-InceptionDWConv (ESIDConv). Using this module as the core, we proposed a lightweight SAR ship detector (LSDet) based on YOLOv5n. The detector achieves a detection accuracy of 99.5 with only 0.3 M parameters and 1.2 G operations on the dataset. Extensive experiments on different datasets demonstrated the superiority and effectiveness of our proposed method.

Computational vibration mitigation using phase interpolation in digital holographic microscopy for overlay metrology

Digital holographic microscopy retrieves amplitude and phase information of an image which allows us to computationally correct for imperfections in the imaging optics. However, digital holographic microscopy is an interferometric technique that is inherently sensitive to undesired phase variations between object and reference beam. These phase variations lower the fringe contrast if they are integrated over a finite exposure time which leads to a reduced amplitude of the retrieved image. This results in significant errors in applications that rely on a stable and accurate amplitude measurement, such as optical overlay metrology in the semiconductor industry. We present experimental results on a computational vibration mitigation method for the application of overlay metrology using phase interpolation between a sequence of measured holograms and demonstrate its capability to improve metrology precision in an overlay metrology application that uses digital holographic microscopy.

Modular test rig for a rotor supported by gas foil bearings

AbstractFoil bearings for high speed turbomachinery are and have been studied in terms of load capacity and lift‐off speed, stability, thermal behaviour and manufacturing. This paper focuses on a modular rotor‐bearing test rig, with which design changes can easily be experimentally investigated. An exemplary design change is undertaken and its effects are studied. Furthermore, a simple procedure is introduced, with which rigid body modes of the rotor can be extracted from phase information derived from experimentally obtained time series data.

Regularization by denoising diffusion process meets deep relaxation in phase

Fourier phase retrieval is one of the representative inverse problems where a signal needs to be recovered using only the measured magnitude of its Fourier transform. Deep learning-based algorithms for solving Fourier phase retrieval have been widely studied. These methods provide better reconstruction than the conventional algorithms, such as alternating projection approaches and convex relaxation methods. However, it is difficult to recover the phase information of 256 × 256 images accurately, and they often cannot provide fine details and textures. Recently, diffusion models have been used to solve Fourier phase retrieval problems. They offer realistic reconstruction results, but due to the nature of generative models, they often create non-existent features in the actual images. To address these issues, we introduced a novel algorithm inspired by regularization by denoising diffusion, a variational diffusion sampling for reconstructing the images from the measurements. In particular, the optimization problem in the convex relaxation approach for phase retrieval is interpreted as an additional constraint during the variational sampling process to estimate the phase from the given Fourier magnitude measurement. The proposed method stands out by leveraging not only pre-trained diffusion models as image priors but also the classical optimization approach as the regularization. This novel combination ensures not just accurate phase reconstruction, but also performance guarantees. Our experiments demonstrate that the proposed algorithm consistently provides state-of-the-art performance across various datasets of 256 × 256 images. We further showed the effectiveness of the new regularization for the performance gain in the phase estimation.

Robust quantitative X-ray phase diagnostic for carbon composite characterisation in the context of lightning induced risk

Getting complementary physical information from a single image acquisition is particularly valuable for materials analysis. Grating based X-ray Phase Contrast Imaging (XPCI) methods allow decoupling attenuation, phase and scattering information. However, the phase and scattering extraction processes can easily suffer from artefacts, which is detrimental to implement this imaging technique in societal applications. In this paper, we demonstrate that grating based XPCI can provide a robust phase measurement in complex materials such as damaged composites. The technique allows the phase to be analysed using a self-assessment method that first identifies the artefacts from the imaging setup, and then can be used as an indicator to interpret the signal from a material. We focus on carbon fibre reinforced polymers which we subjected to laboratory-controlled lightning strikes. We evidence that the combined information from phase and attenuation allow identifying the type of defect induced by the lightning current. The phase information is converted into relative mass density variation within the sample and depicts areas with a loss in density up to 40%. We ensure that these results are valid by comparing them with an X-ray attenuation contrast tomographic reconstruction.