Spatial Spectrum Research Articles

Optical feedback-induced spatiotemporal patterns with power law spectra in a liquid crystal light valve.

Pattern formation can be induced by coupling electromagnetic fields to a polarizable and lossy medium. Increasing energy injection patterns can exhibit aperiodic behaviors. We investigate the self-organization of unidimensional aperiodic patterns. Based on a liquid crystal light valve (LCLV) with optical feedback, we observed aperiodic one-dimensional patterns with power laws in the temporal and spatial spectrum density of the light intensity, and their pseudo envelope and phase characteristic of spatiotemporal complexity. Theoretically, a local model describes the system close to nascent bistability and spatial instability. Numerical simulations of this model show chaotic spatiotemporal patterns whose temporal and spatial spectra have exponents similar to those observed experimentally.

Active control of Alfvén eigenmodes by external magnetic perturbations with different spatial spectra

Alfvén eigenmodes have been suppressed and excited in tokamak plasmas by (just) modifying the poloidal spectra of externally applied static magnetic perturbations. This effect is observed experimentally when toroidal spectra of n = 2, n = 4 as well as a mixed spectrum of n = 2 and n = 4 is applied. Under the n = 2 magnetic perturbations, the modes are excited or suppressed by modifying the coil phasing between the upper and the lower set of coils. Regardless of the absolute rotation, an even parity for the n = 4 perturbation is observed to reduce the amplitude of the Alfvénic instabilities, while an odd parity amplifies it. To combine the stabilizing (and destabilizing) effect of n = 2 and n = 4, a mixed spectrum is applied, finding similar reduction (and amplification) trends. However, the impact on the mode amplitude is more subtle, due to the reduced coil current required for a mixed spectrum. The signal level on the fast-ion loss detector is sensitive to the applied poloidal spectrum, which is consistent with Hamiltonian full-orbit modelling of an edge resonant transport layer activated by the 3D perturbative fields. An internal redistribution of the fast-ion population is induced, modifying the phase-space gradients driving the Alfvénic instabilities, and ultimately determining their existence. The calculated edge resonant layers for both n = 2 and n = 4 toroidal spectra are consistent with the observed suppressed and excited phases. Moreover, hybrid kinetic-magnetohydrodynamic (MHD) simulations reveal that this edge resonant transport layer overlaps in phase-space with the population responsible for the fast-ion drive. The results presented here may help to control fast-ion driven Alfvénic instabilities in future burning plasmas with a significant fusion born alpha particle population.

Quantitative measurement of spatial distribution of effective refractive index induced by local electron concentration at a nano slit

Abstract Surface plasmon polaritons (SPPs) have found their key applications in high-sensitivity biomolecular detection and integrated photonic devices for optical communication via light manipulation at nanostructures. Despite their broad utility, SPPs are known to be accompanied by other complex near-field propagation modes, such as quasi-cylindrical waves (QCWs) and composite diffracted evanescent waves (CDEWs), whose electromagnetic and quantum propagation effects have not been comprehensively understood especially regarding their mutual interaction with SPPs. In this study, we addressed this complexity by employing a nano groove structure and a high-stability broadband femtosecond laser as a light source, the spatial phase distribution around the nano slit edge was measured with relative stability of a 4.6 × 10−11 at an averaging time of 0.01 s. Through this spatial phase spectrum, we precisely measured the nonlinear distribution of effective refractive index changes with an amplitude of 10−2 refractive index units at the edge of the nano slit–groove structure. These results reveal that the near-field effects on local electron concentration induced by nanostructure’s discontinuity can be quantitatively measured, which can contribute to a deeper understanding of SPP phenomena in nanostructures for the optimal design and utilization of the SPP effects in diverse nano-plasmonic applications.

Super-resolution spatial spectrum reconstruction under unknown near-field interferences

In underwater acoustic signal processing, conventional spatial spectrum estimation and the associated direction-of-arrival estimation are often impaired by strong near-field interferences at the same frequency as the far-field target signal, leading to significant performance degradation and even detection failure. In contrast to prevalent near-field interference suppression algorithms that require prior knowledge of near-field interference positions, this paper proposes a super-resolution spatial spectrum reconstruction algorithm designed for more general scenarios where crucial information about the near-field interference, such as positions and magnitudes, is unknown. The proposed algorithm demonstrates its adaptability in mitigating unknown near-field interference and achieves this by leveraging rank constraint-based relaxation and alternating minimization, resulting in an effective spatial spectrum reconstruction strategy. The efficacy of the proposed spatial spectrum reconstruction method in handling strong near-field interference is confirmed through analysis of simulated and synthetic experimental data. It exhibits superiority over traditional competitors in terms of resolution, denoising capabilities, and estimation accuracy. Moreover, it achieves comparable results to algorithms that utilize prior information about near-field interference positions. The enhanced performance remains consistent even in challenging scenarios such as snapshot deficiency and low signal-to-noise ratios.

Мультиплицирование дифракционных картин от плоских геометрических фрактальных объектов с использованием метода прореживания

An algorithm is described and the features of multiplying diffraction patterns (that is, spatial frequency spectra, both calculated and experimentally obtained) from flat fractal objects using the thinning method are studied. One fractal of the L-system, described in detail and studied by the authors, was used as the main object, which greatly facilitated the interpretation of the results obtained.

Partially coherent Pearcey-Gauss source with hyperbolic sine correlation of the spatial spectrum.

By designing the intricate coherence structure, we are able to create a desired beam profile and trajectory. Our research focus lies on the Fourier plane, specifically emphasizing the coherence of spatial frequencies, and we find it can be seen as a constant system response. A theoretical framework is developed, and experimental studies are conducted to generate a light field of the spatial spectrum with a complex correlation using the pseudo-mode superposition method. We successfully produce partially coherent Pearcey-Gauss beams whose spatial spectrum is hyperbolic sine correlational. Interestingly, these beams maintain the distinctive propagation properties of the Pearcey pattern while exhibiting the remarkable ability to split the mainlobe into two separate lobes.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ∼159 mm, which is greater than the ∼47 mm and ∼44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

Motion-guided large aperture ULA to enhance DOA estimation: An inverse synthetic aperture perspective

Direction-of-arrival (DOA) estimation is a critical issue in array signal processing, especially in radar applications. However, the array aperture of high-frequency radars is constrained by the site and platform, which in turn leads to limited DOA estimation performance. In this paper, inspired by the inverse synthetic aperture radar, we design a novel DOA estimation method based on motion-guided large-aperture uniform linear array (ULA). Referring to the concept of inverse synthetic aperture, the novel method utilizes the relative motion between the radar and the target to construct a larger ULA. We demonstrate that the key condition for reconstructing ULAs is suitable data segmentation and reshaping within the coherent integration time. However, the correct segmentation position is related to the target's motion trajectory. For non-cooperative targets, the uncertain segmentation position is regarded as a mismatch problem between the steering and weighting vectors, such that the spatial spectrum becomes a product of two sinc-like functions. With the analysis of the main lobe and grating lobes, the maximum peak-to-grating-lobe ratio criterion is introduced to find the ambiguity-free DOA. The experimental results demonstrate the performance advantages of the proposed DOA estimation algorithm in terms of noise robustness and resolution.

Spectral analysis of dispersed multiphase flows in the presence of fluid interfaces

Spectral analysis of dispersed two-phase flows is highly desirable to reveal the interplay of the various flow scales, much larger or much smaller than the size of the dispersed bodies. This is a challenging task as the matching conditions at the body interfaces generate singularities in the fields describing the two-phase mixture. The nature of these singularities and their consequences on the spectra are theoretically analyzed for bubble or droplet flows. Results of direct numerical simulations are reported and spatial spectra of the mixture velocity, the flow forces and their power are examined. The regular part of the spectral densities of energy production, dissipation and transfers between scales are separated from their singular part. The resulting spectral energy balance, free of the footprint of the singularities, is found in agreement with coarse-grained simulations where the interfaces are filtered out before solving the Navier–Stokes equations. These results pave the way for the spectral analysis of more complex turbulent dispersed flows.

Improving the Accuracy of Direction of Arrival Estimation with Multiple Signal Inputs Using Deep Learning.

In this paper, an innovative cyclic noise reduction method and an improved CAPON algorithm (also the called minimum variance distortionless response (MVDR) algorithm) are proposed to improve the accuracy and reliability of DOA (direction of arrival) estimation. By processing the eigenvalues obtained from the covariance matrix of the received signal, the signal-to-noise ratio (SNR) can be increased by up to 5 dB by the cyclic noise reduction method, which will improve the DOA estimation accuracy. The improved CAPON algorithm has a convolution neural network (CNN) structure, whose input is the processed covariance matrix of the received signal, and the CAPON spectral value is used as the training label to obtain the estimated spatial spectrum. It retains the advantages of the CAPON algorithm, which can achieve blind source estimation, and simulations show that the proposed algorithm exhibits a better performance than the traditional algorithm in conditions of various SNRs and snapshot numbers. The simulation results show that, based on a certain SNR, the root mean square error (RMSE) of the improved CAPON algorithm can be reduced from 0.86° to 0.8° compared to traditional algorithms, and the angle estimation error can be decreased by up to about 0.3°. With the help of the cyclic noise reduction method, the angle estimation error decreases from 0.04° to 0.02°.

Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization

AbstractTwo‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy.

Low-Complexity 2D-DOD and 2D-DOA Estimation in Bistatic MIMO Radar Systems: A Reduced-Dimension MUSIC Algorithm Approach.

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field.

Analysis of pole pair number impact on single-sided linear induction machine performances

This paper presents a parametric study of linear induction motor for design purpose. The chosen mathematical model uses a 2D formulation with magnetic vector potential A. The implementation of the model is carried out with the finite element method on the free platform Gmsh-GetDP. Circuit model is coupled to FE model so that constant voltage supply mode can be considered. This work aims to highlight the effect of the pole pair number on the characteristics and the performances of the linear induction machine through two numerical models associated to an analytical one. Furthermore, the study shows the effect of the pole pair number on the phase imbalance and the spatial harmonic spectrum of the machine. Reducing this imbalance and higher order harmonics presence will increase machine performances.

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Proposal of Practical Sound Source Localization Method Using Histogram and Frequency Information of Spatial Spectrum for Drone Audition

Spatial signatures of an approaching regime shift in Posidonia oceanica meadows

Determining the proximity of ecosystems to tipping points is a critical yet complex task, heightened by the growing severity of climate change and local anthropogenic stressors on ecosystem integrity. Spatial Early Warning Signals (EWS) have been recognized for their potential in preemptively signaling regime shifts to degraded states, but their performance in natural systems remains uncertain. In this study, we investigated the performance of 'recovery length' — the spatial extent of recovery from a perturbation — and spatial EWS as early warnings of regime shifts in Posidonia oceanica meadows. Our experimental approach involved progressively thinning the P. oceanica canopy, from 0 to 100%, at the edge of a dead-matte area — a structure formed by dead P. oceanica rhizomes and colonized by algal turfs — to promote the propagation of algal turfs. We calculated recovery length as the distance from the dead-matte edge to the point where algal turfs colonized the canopy-thinned region. Our results showed a linear increase in recovery length with canopy thinning, successfully anticipated the degradation of P. oceanica. While spatial skewness decline with increased canopy degradation, other spatial EWS, such as Moran correlation at lag-1, low-frequency spatial spectra, and spatial variance, were ineffective in signaling this degradation. These findings underscore the potential of recovery length as a reliable early warning indicator of regime shifts in marine coastal ecosystems.

Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.

Numerical investigation on vortex-induced vibration of catenary riser conveying fluid under top-end heave excitation

ABSTRACT Due to the catenary shape of steel catenary risers (SCR), the heave motion of the floating platform can generate an equivalent oscillatory flow field and excite the vortex-induced vibration (VIV) of SCR. Internal flow conveyed by SCR further complicates the VIV response. A semi-empirical model is improved to simulate the VIV of SCR with internal flow under top-end heave excitation. VIV characteristics such as displacement temporal–spatial distribution and amplitude spectra spatial distribution are analysed and the fatigue damage is calculated. The effects of heave period, heave amplitude, internal flow volumetric flow rate and internal flow density on VIV of SCR are discussed. The heave excitation with short period or large amplitude tends to excite high-order-mode response and enlarge the fatigue damage. The VIV response becomes less regular and the fatigue damage is reduced when the internal flow volumetric flow rate or density increases.

Research on interference compensation methods for color image sensors based on iterative learning

PurposeFixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.Design/methodology/approachThrough an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.FindingsThe comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).Originality/valueIn this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

Evaluating the Effectiveness of a Gan Fingerprint Removal Approach in Fooling Deepfake Face Detection

Deep neural networks are able to generate stunningly realistic images, making it easy to fool both technology and humans into distinguishing real images from fake ones. Generative Adversarial Networks (GANs) play a significant role in these successes (GANs). Various studies have shown that combining features from different domains can produce effective results. However, the challenges lie in detecting these fake images, especially when modifications or removal of GAN components are involved. In this research, we analyse the high-frequency Fourier modes of real and deep network-generated images and show that Images generated by deep networks share an observable, systematic shortcoming when it comes to reproducing their high-frequency features. We illustrate how eliminating the GAN fingerprint in modified pictures' frequency and spatial spectrum might affect deep-fake detection approaches. In-depth review of the latest research on the GAN-Based Artifacts Detection Method. We empirically assess our approach to the CNN detection model using style GAN structures 140k datasets of Real and Fake Faces. Our method has dramatically reduced the detection rate of fake images by 50%. In our study, we found that adversaries are able to remove the fingerprints of GANs, making it difficult to detect the generated images. This result confirms the lack of robustness of current algorithms and the need for further research in this area.

Direct numerical simulation of a wall source dispersion in a turbulent channel flow

A comprehensive direct numerical simulation (DNS) is conducted to analyze the turbulent mixing process of a surface source dispersion emitting from the bottom wall in a turbulent channel flow. A comparative analysis of ten test cases has been conducted to examine the turbulent mixing process and its relationship with the source strength. The study involves an examination of the dispersion process in both physical and spectral spaces, which includes an evaluation of statistical moments of the concentration field, pre-multiplied spatial spectra of the velocity and concentration fields, as well as an analysis of the coherent turbulent structures in the flow. Upon normalization by friction concentration, the DNS analysis reveals that the first- and second-order statistical moments of the concentration field remain unaffected by variations in the source strength. Conversely, the relative intensity of concentration fluctuations and the thickness of the concentration boundary layer exhibit significant susceptibility to variations in the source strength. It has been observed that the dispersion process within the channel is significantly influenced by the small-scale eddies in cases where the source strength is not particularly intense. When confronted with high source strength scenarios, analysis of the simulation results establishes that both small- and large-scale eddies are crucial players in the dispersion phenomena, as evinced by a comparison of the pre-multiplied spectra of concentration and velocity fields. Furthermore, hairpin vortices have been recognized as the primary source of turbulence in the dispersal of substances from regions of high shear to the center of the channel.