Pattern Distortion Research Articles

A Comparative Study of Non-Destructive Testing Techniques: Active Thermography versus Shearography for 3D-Printed Thermoplastic Composites Reinforced with Continuous Carbon Fiber

This study investigates the feasibility and effectiveness of two non-destructive testing methods, active thermography and shearography, on 3D-printed thermoplastic (TP) composites reinforced with continuous carbon fiber. Artificial defects were introduced into the composite plate to benchmark the detection capabilities of these non-destructive testing techniques (NDT). Active thermography produced a thermogram that highlighted defects through variations in surface temperature. Although effective for identifying defects ranging from 3 to 10 mm in size at four different depths, specifically 1 mm, 1.25 mm, 1.5 mm, and 1.75 mm, through the thickness of a 2.8 mm plate, the method encountered some limitations. It faced challenges in detecting deeper defects and accurately determining their shapes. Shearography, which utilizes fringe pattern distortions to detect surface displacement anomalies, successfully identified near-surface defects within the same size range. However, it required more expertise for accurate interpretation and struggled with detecting smaller and deeper defects. The complementary strengths and limitations of these methods suggest that employing both could offer a more comprehensive solution for defect detection in 3D-printed TP composites.

One-shot entorhinal maps enable flexible navigation in novel environments.

Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1-5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.

Heterodyne background-oriented schlieren for the measurement of thermoacoustic oscillations in flames

In aircraft engines, thermoacoustic oscillations in the combustion chamber contribute significantly to noise emissions, which, like all other emissions, must be drastically reduced. Thermoacoustic oscillations are not only a concern, they can also be beneficial in hydrogen combustion. This work demonstrates that thermoacoustic density oscillations with amplitudes at least an order of magnitude smaller than those resulting from density gradients in a turbulent flame can be detected using laser interferometric vibrometry. This improvement was made possible by heterodyning a carrier fringe system in background-oriented schlieren (BOS) recordings, which were subsequently analyzed using techniques commonly used for holographic interferometry. In comparison with other BOS evaluation techniques, the filtering of the individual frames in the Fourier domain offers a more efficient computational approach, as it allows for phase averaging of a high number of single recordings to reduce noise from turbulence. To address fringe pattern distortions and cross talk in the Fourier domain, which both have been observed by other authors, we propose background subtraction methods and an optimized background pattern. Additionally, the procedure provides a visualization tool for marking the high turbulence regions of heat release by the variations in fringe amplitude. Finally, the line-of-sight data are reconstructed using the inverse Abel transform, with the data calibrated by laser interferometric techniques, resulting in local values for density oscillations.Graphical abstract

Radiation Pattern Analysis and Phase Shifter for Base Station Mobile Communication Circular Array Antenna at 900MHz Frequency by Using 4NEC2X Software

Background: This paper presents the utilization of phase shifters to manipulate and control the radiation patterns in Uniform Circular Array (UCA) antennas. UCAs, known for their 360-degree coverage and symmetrical radiation patterns, are enhanced by incorporating phase shifters, enabling dynamic beam steering and pattern shaping. Materials and Methods: A uniform circular antenna array operating at 900 GHz frequency has been intended for use in base stations using 4NEC2X simulation software. Results: A single-element dipole antenna having a 2.14 dBi gain is used as a base for constructing a uniform 0.5 λ spacing circular array. The number of elements gradually increased from 2 to 12, raising the gain to 12.3 dBi. HPBW decreased both in vertical and horizontal plans from 80o to 60o and 360o to 20o respectively this implies that the circular array antenna's directivity is enhanced. The eight-element array beam scanning was performed from 0o to 360o without any distortion in radiation pattern, gain, and directivity, contradictory with the linear array. Conclusions: The studied case and simulation output illustrate the impact of the element numbers and phase shifter configurations on properties of the radiation pattern of the uniform circular array can be obtained when increasing the number of elements, the gain increases too and scanning the main beam without any change and distortion

Design of bi-axial piezoelectric MEMS micro mirror with gimbal actuator for dynamic decoupling

Abstract This study presents a novel structural design of a bi-axial piezoelectric micro-electro-mechanical-systems scanning mirror aimed at preventing the dynamic coupling between the two scanning axes and avoiding distortion of the scanning pattern. In the proposed design, the gimbal actuator, constrained by torsional springs at both ends, not only serves as the torque generation component for the actuation of one axis but also acts as a pivot to prevent structural interference between the two axes during scanning. This approach achieves the advantages of decoupling and compactness simultaneously. Simulations indicate that the gimbal actuator experiences very small displacement during y-axis actuation, thus realizing the dynamic decoupling from the x-axis scanning unit. To evaluate the proposed designs, fabrication processes were adopted on a silicon-on-insulator wafer with a lead zirconium titanate film. The miniaturized scanners, featuring a mirror with a 1.2 mm diameter, were realized on a compact 5 × 5 mm2 chip. Measurements indicate that the resonant frequencies of x-axis and y-axis scanning are 2.69 kHz and 4.29 kHz, respectively, and the optical scanning angles reach 33° and 21° under an 8 Vpp driving voltage. Additionally, measurements show that the displacement ratio between the central mirror and the gimbal actuator can reach up to 17 during y-axis actuation, verifying the concept of dynamic decoupling. The bi-axial Lissajous scanning pattern with straight edges also demonstrates the effective decoupling by the proposed gimbal actuator design.

Spiking representation learning for associative memories.

Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brain's spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effortlessly. However, solving real-world problems with artificial spiking neural networks (SNNs) has proved to be difficult for a variety of reasons. Crucially, scaling SNNs to large networks and processing large-scale real-world datasets have been challenging, especially when compared to their non-spiking deep learning counterparts. The critical operation that is needed of SNNs is the ability to learn distributed representations from data and use these representations for perceptual, cognitive and memory operations. In this work, we introduce a novel SNN that performs unsupervised representation learning and associative memory operations leveraging Hebbian synaptic and activity-dependent structural plasticity coupled with neuron-units modelled as Poisson spike generators with sparse firing (~1 Hz mean and ~100 Hz maximum firing rate). Crucially, the architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories. We evaluated the model on properties relevant for attractor-based associative memories such as pattern completion, perceptual rivalry, distortion resistance, and prototype extraction.

Mechanism of Duration Perception in Artificial Brains Suggests New Model of Attentional Entrainment.

While cognitive theory has advanced several candidate frameworks to explain attentional entrainment, the neural basis for the temporal allocation of attention is unknown. Here we present a new model of attentional entrainment guided by empirical evidence obtained using a cohort of 50 artificial brains. These brains were evolved in silico to perform a duration judgment task similar to one where human subjects perform duration judgments in auditory oddball paradigms. We found that the artificial brains display psychometric characteristics remarkably similar to those of human listeners and exhibit similar patterns of distortions of perception when presented with out-of-rhythm oddballs. A detailed analysis of mechanisms behind the duration distortion suggests that attention peaks at the end of the tone, which is inconsistent with previous attentional entrainment models. Instead, the new model of entrainment emphasizes increased attention to those aspects of the stimulus that the brain expects to be highly informative.

Plant "intelligence" and the misuse of historical sources as evidence.

Proponents of the concepts of plant intelligence and plant neurobiology often use historical sources as "evidence" and argue that eminent past scientists have supported ideas of plant intelligence, memory, learning, decision-making, agency, and consciousness. Historical sources include writings by Charles Darwin, Julius von Sachs, F. W. Went, K. V. Thimann, Barbara McClintock, and J. B. Lamarck. Advocates of plant neurobiology also argue that the ideas of J. C. Bose, an Indian scientist who is considered an important forerunner of plant neurobiology, were suppressed chiefly because of racism. Plant neurobiology has been criticized on scientific grounds, but there has not been close scrutiny of the use of historical sources as a form of evidence. We provide the first in-depth analysis of how historical sources have been used and misused, and conclude that there is a consistent pattern of distortion of these sources. Distortions include the use of erroneous quotations, alteration of quotations, selective quotations without context, and misinterpretation and exaggeration of historical statements. In the case of Bose, we show that there were legitimate scientific reasons for questioning his interpretations of botanical experiments and argue that this context cannot be ignored in evaluating contemporary responses to Bose. Overall, the common practice by proponents of plant intelligence and plant consciousness of uncritically citing the words of eminent scientists of the past, taken out of their historical context to bolster their arguments, should not be confused with scientific evidence supporting these concepts, even when the quotations, themselves, are accurate.

A high-order time-delay difference estimation method for signal enhancement in the distorted towed hydrophone array.

In passive sonar systems, deviations from an ideal linear configuration can significantly impair the beamforming performance of towed hydrophone arrays. This paper presents a method aimed at improving the underwater acoustic signals in the presence of array distortion. The method is centered on a high-order time-delay difference estimation technique utilizing time-frequency autofocus. Initially, a detailed signal model is established that captures the distinctive characteristics of distorted arrays. Subsequently, an algorithm is introduced for high-order time-delay difference estimation to enhance signal fidelity by leveraging phase information within narrowband components originating from incidental acoustic sources. Additionally, a quality metric to evaluate these components is introduced, facilitating the practical implementation of the method. The effectiveness of the proposed approach is validated through both simulation and experimental results, demonstrating its superiority over existing techniques. Importantly, this method does not require prior knowledge of the distortion pattern, making it adaptable to various non-linear array configurations.

ConIQA: A deep learning method for perceptual image quality assessment with limited data

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall’s tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.

Impact of Fe-impurity centers on phonon subsystem of wurtzite-type ZnO

The paper addresses the question of the structural and vibrational properties of Fe-doped ZnO in the wurtzite structure with a lattice without an inversion center. The ZnO crystals containing Fe impurities in different charged states are studied by means of density functional theory (DFT) and potential-based methods. A first-principles simulation is performed to determine the local atomic configurations near the considered defects. The DFT results are compared with those obtained using the shell model. Both approaches give a similar pattern of lattice distortion around the Fe-impurity centers occupying cation sites in the isoelectronic charge state Fe2＋ and single-positive-charge state Fe3＋. The phonon local symmetrized densities of states projected onto the displacement of ions surrounding the defects are calculated by a well-established recursion method. The frequencies of defect vibrations of various symmetry types induced by Fe impurities are determined. The calculations made it possible to interpret the structure of the phonon sideband accompanying the zero-phonon line in polarized emission spectra attributed to the Fe3＋ intracenter transitions, as well as to estimate the role of Fe ions in the formation of observed peaks.

GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases.

Radiation Pattern Correction of Faulty Planar Phased Array using Genetic Algorithm

The probability of antenna array failure or malfunctioning cannot be ruled out, and hardware replacement of faulty elements is not always a viable solution. Therefore, academic and industrial interest in self-healing phased arrays are on the rise. In this work, the phase-only genetic algorithm (GA) optimization flow for the radiation pattern correction of a 4 × 4 phase faulty planar antenna array is proposed. Initially, a reference array pattern at the desired scan angle is generated. Then random phase faults are introduced across the 1 × 4 antenna elements in any one of 4 sub-arrays to produce maximum distortion in the reference radiation pattern of 4 × 4 planar array. The proposed GA re-computes the new excitation weights for the remaining non-faulty 3 sub-arrays to correct the overall radiation pattern of 4 × 4 array. This is achieved by calculating the array output power for reference and GA computed weights. The GA corrected patterns fairly follow the desired array patterns in terms of peak gain and reducing sidelobe levels for the desired scan angle. The efficiency of the optimized radiation patterns was evaluated in full-wave HFSS model and measurements validation. In this way, maintenance cost can be reduced with recovery of acceptable level of radiation pattern using software instead of physically replacing faulty antenna elements in the array.

Surpassing light inhomogeneities in structured-illumination microscopy with FlexSIM.

Super-resolution structured-illumination microscopy (SIM) is a powerful technique that allows one to surpass the diffraction limit by up to a factor two. Yet, its practical use is hampered by its sensitivity to imaging conditions which makes it prone to reconstruction artefacts. In this work, we present FlexSIM, a flexible SIM reconstruction method capable to handle highly challenging data. Specifically, we demonstrate the ability of FlexSIM to deal with the distortion of patterns, the high level of noise encountered in live imaging, as well as out-of-focus fluorescence. Moreover, we show that FlexSIM achieves state-of-the-art performance over a variety of open SIMdatasets.

Applying auxiliary arrays for mainlobe blanking to counter mainlobe repeater jamming

Mainlobe repeater jamming exploits mainlobe gain of radar antenna and signal-processing gain to significantly disrupt the target detection performance of radar. The combination of multiple auxiliary radars to form a distributed array radar improves the radar system’s angle resolution, enabling the realization of traditional spatial jamming cancellation. However, the jamming cancellation method struggle to effectively suppress multiple mainlobe repeater jamming signals when their number exceeds that of auxiliary radars, resulting in pattern distortion and subsequent loss of target energy. To address this issue, the mainlobe blanking method with auxiliary radars is proposed based on the specific characteristics of the jamming signals being distributed in multiple discontinuous peaks at the same angle. This approach leverages the superior angular resolution to accurately differentiate between target and jamming signals based on their distance-angle characteristics. Moreover, specific jamming suppression methods are employed for each jamming signal based on whether it overlaps with the target in the time domain, minimizing the loss of target energy. Furthermore, the proposed method is capable of suppressing jamming even when the target and jamming signals overlap in the time domain, and is not constrained by the number of auxiliary radars available. Simulations and experiments are used to validate the effectiveness and correctness of the proposed method in suppressing mainlobe repeater jamming.

Phase pattern distortions of the medum-wave band antenna placed on the carrier

Phase pattern distortions of the medum-wave band antenna placed on the carrier

The evolution of passive dispersal versus habitat selection have differing emergent consequences in metacommunities.

Dispersal among local communities is fundamental to the metacommunity concept but is only important to the metacommunity structure if dispersal causes distortions of species abundances away from what local ecological conditions favour. We know from much previous work that dispersal can cause such abundance distortions. However, almost all previous theoretical studies have only considered one species alone or two interacting species (e.g. competitors or predator and prey). Moreover, a systematic analysis is needed of whether different dispersal strategies (e.g. passive dispersal versus demographic habitat selection) result in different abundance distortion patterns, how these distortion patterns change with local food web structure, and how the dispersal propensities of the interacting species might evolve in response to one another. In this article, we show using computer simulations and analytical models that abundance distortions occur in simple food webs with both passive dispersal and habitat selection, but habitat selection causes larger distortions. Additionally, patterns in the evolution of dispersal propensity in interacting species are very different for these two dispersal strategies. This study identifies that the dispersal strategies employed by interacting species critically shape how dispersal will influence metacommunity structure. This article is part of the theme issue 'Diversity-dependence of dispersal: interspecific interactions determine spatial dynamics'.

Significantly enhanced upconversion luminescence intensity and tailorable chromaticity of Sn4+-doped NaYF4:Yb3+/Er3+

The internal modification with rare earth ion doping proved to be a very effective strategy for improving the luminescent properties of NaYF4-based upconversion materials. However, greatly enhancing the luminescence efficiency of NaYF4:Yb3+/Er3+ remains a major challenge. Herein, the effects of Sn4+ doping on the structure and luminescent performance of such material were explored. The hydrothermal molten-salt method was applied to synthesize the Sn4+-doped NaYF4:Yb3+/Er3+ upconversion materials, and their crystal structures, morphologies, surface chemical composition and element states, and luminescence performance were characterized. It was found that Sn4+ doping can significantly enhance the luminescence intensity and tailor the chromaticity of NaYF4:Yb3+/Er3+. In particular, the green (G) and red (R) luminescence intensity levels of the 30 mol% Sn4+ doped material were increased by factors of 26.97 and 38.91, respectively. The R/G ratio was incremented from 0.34 for the undoped material to 0.83 for the 40 mol% Sn4+ doped counterpart. The Sn4+ doping led to the change of lattice distortion and crystal growth pattern of NaYF4:Yb3+/Er3+. The mechanism for Sn4+ doping to affect the luminescence properties of the prepared upconversion material was also explored. The changes in luminescence intensity levels and R/G ratios could be attributed to the highly asymmetric distorted lattice and crystal field resulting from Sn4+ doping.

Noncollinear Electric Dipoles in a Polar Chiral Phase of CsSnBr3 Perovskite.

Polar and chiral crystal symmetries confer a variety of potentially useful functionalities upon solids by coupling otherwise noninteracting mechanical, electronic, optical, and magnetic degrees of freedom. We describe two phases of the 3D perovskite, CsSnBr3, which emerge below 85 K due to the formation of Sn(II) lone pairs and their interaction with extant octahedral tilts. Phase II (77 K < T < 85 K, space group P21/m) exhibits ferroaxial order driven by a noncollinear pattern of lone pair-driven distortions within the plane normal to the unique octahedral tilt axis, preserving the inversion symmetry observed at higher temperatures. Phase I (T < 77 K, space group P21) additionally exhibits ferroelectric order due to distortions along the unique tilt axis, breaking both inversion and mirror symmetries. This polar and chiral phase exhibits second harmonic generation from the bulk and pronounced electrostriction and negative thermal expansion along the polar axis (Q22 ≈ 1.1 m4 C-2; αb = -7.8 × 10-5 K-1) through the onset of polarization. The structures of phases I and II were predicted by recursively following harmonic phonon instabilities to generate a tree of candidate structures and subsequently corroborated by synchrotron X-ray powder diffraction and polarized Raman and 81Br nuclear quadrupole resonance spectroscopies. Preliminary attempts to suppress unintentional hole doping to allow for ferroelectric switching are described. Together, the polar symmetry, small band gap, large spin-orbit splitting of Sn 5p orbitals, and predicted strain sensitivity of the symmetry-breaking distortions suggest bulk samples and epitaxial films of CsSnBr3 or its neighboring solid solutions as candidates for bulk Rashba effects.

Lightweight and drift-free magnetically actuated millirobots via asymmetric laser-induced graphene

Millirobots must have low cost, efficient locomotion, and the ability to track target trajectories precisely if they are to be widely deployed. With current materials and fabrication methods, achieving all of these features in one millirobot remains difficult. We develop a series of graphene-based helical millirobots by introducing asymmetric light pattern distortion to a laser-induced polymer-to-graphene conversion process; this distortion resulted in the spontaneous twisting and peeling off of graphene sheets from the polymer substrate. The lightweight nature of graphene in combine with the laser-induced porous microstructure provides a millirobot scaffold with a low density and high surface hydrophobicity. Magnetically driven nickel-coated graphene-based helical millirobots with rapid locomotion, excellent trajectory tracking, and precise drug delivery ability were fabricated from the scaffold. Importantly, such high-performance millirobots are fabricated at a speed of 77 scaffolds per second, demonstrating their potential in high-throughput and large-scale production. By using drug delivery for gastric cancer treatment as an example, we demonstrate the advantages of the graphene-based helical millirobots in terms of their long-distance locomotion and drug transport in a physiological environment. This study demonstrates the potential of the graphene-based helical millirobots to meet performance, versatility, scalability, and cost-effectiveness requirements simultaneously.