Types Of Distortions Research Articles

Blind image quality assessment by pairwise ranking image series

Image quality assessment (IQA) is constantly innovating, but there are still three types of stickers that have not been resolved: the "content sticker" - limitation of training set, the "annotation sticker" - subjective instability in opinion scores and the "distortion sticker" - disordered distortion settings. In this paper, a No-Reference Image Quality Assessment (NR IQA) approach is proposed to deal with the problems. For "content sticker", we introduce the idea of pairwise comparison and generate a largescale ranking set to pre-train the network; For "annotation sticker", the absolute noise-containing subjective scores are transformed into ranking comparison results, and we design an indirect unsupervised regression based on Eigenvalue Decomposition (EVD); For "distortion sticker", we propose a perception-based distortion classification method, which makes the distortion types clear and refined. Experiments have proved that our NR IQA approach Experiments show that the algorithm performs well and has good generalization ability. Furthermore, the proposed perception based distortion classification method would be able to provide insights on how the visual related studies may be developed and to broaden our understanding of human visual system.

Unsupervised underwater image enhancement via content-style representation disentanglement

The absorption and scattering properties of the water medium cause various types of distortion in underwater images, which seriously affects the accuracy and effectiveness of subsequent processing. The application of supervised learning algorithms in underwater image enhancement is limited by the difficulty of obtaining a large number of underwater paired images in practical applications. As a solution, we propose an unsupervised representation disentanglement based underwater image enhancement method (URD-UIE). URD-UIE disentangles content information (e.g., texture, semantics) and style information (e.g., chromatic aberration, blur, noise, and clarity) from underwater images and then employs the disentangled information to generate the target distortion-free image. Our proposed method URD-UIE adopts an unsupervised cycle-consistent adversarial translation architecture and combines multiple loss functions to impose specific constraints on the output results of each module to ensure the structural consistency of underwater images before and after enhancement. The experimental results demonstrate that the URD-UIE technique effectively enhances the quality of underwater images when training with unpaired data, resulting in a significant improvement in the performance of the standard model for underwater object detection and semantic segmentation.

MSCSA-Net: Multi-Scale Channel Spatial Attention Network for Semantic Segmentation of Remote Sensing Images

Although deep learning-based methods for semantic segmentation have achieved prominent performance in the general image domain, semantic segmentation for high-resolution remote sensing images remains highly challenging. One challenge is the large image size. High-resolution remote sensing images can have very high spatial resolution, resulting in images with hundreds of millions of pixels. This makes it difficult for deep learning models to process the images efficiently, as they typically require large amounts of memory and computational resources. Another challenge is the complexity of the objects and scenes in the images. High-resolution remote sensing images often contain a wide variety of objects, such as buildings, roads, trees, and water bodies, with complex shapes and textures. This requires deep learning models to be able to capture a wide range of features and patterns to segment the objects accurately. Moreover, remote sensing images can suffer from various types of noise and distortions, such as atmospheric effects, shadows, and sensor noises, which can also increase difficulty in segmentation tasks. To deal with the aforementioned challenges, we propose a new, mixed deep learning model for semantic segmentation on high-resolution remote sensing images. Our proposed model adopts our newly designed local channel spatial attention, multi-scale attention, and 16-piece local channel spatial attention to effectively extract informative multi-scale features and improve object boundary discrimination. Experimental results with two public benchmark datasets show that our model can indeed improve overall accuracy and compete with several state-of-the-art methods.

Distortion effect on the ligation of imidazoles to water-insoluble iron(III) porphyrin

Axial ligands control the various chemical reactions triggered by heme proteins. Thus, studies on the reaction of iron porphyrin with ligands are important for understanding their functions. Here, a spectroscopic investigation was performed to clarify the distortion effect of the porphyrin ring on the ligation of imidazoles (imidazole (Im), 1-methyl- and 2-methylimidazole (1-MeIm and 2-MeIm)) to iron(III) porphyrin. The overall stability constant ([Formula: see text] was found to have increased, while [Formula: see text] decreased with increasing distortion of the porphyrin core. However, the [Formula: see text] of the tetraphenyl-substituted complex, which is considered the least distorted, was not minimal, while the [Formula: see text] was not maximal. These results suggest that the stability of bis-ligand iron(III) porphyrin complexes depends on the increase in the saddle-type distortion of the porphyrin, the type of distortion, and the steric hindrance at the surrounding substituents. Furthermore, the ligation of 1-MeIm to iron(III) porphyrin hydroxo complexes with the porphyrin ring distortion was investigated. The ligation of 1-MeIm to the iron(III) dodecaphenylporphyrin hydroxo complex (DPPFeOH) suggests that a reaction mechanism different from the general reaction mechanism may occur simultaneously, i.e., a simultaneous reaction. In addition, the [Formula: see text] of DPPFeOH was found to be approximately 15 times smaller than that of the chloride complex. The relationship between the distortion and stability of bis-ligands iron(III) porphyrin complexes obtained in this study is expected to assist in the elucidation of the model reaction of heme proteins.

Proper and improper ferroelastics with perovskite-derived structures

Based on the combined group-theoretical and crystallographic approach, the possible perovskite-derived structures of proper and improper ferroelastics were established. The crystallographic analysis of the Wyckoff position splitting in the low-symmetry proper ferroelastic phases shows the absence of any non-ferroelastic degrees of freedom that reduces the possibilities of structural relaxation. Group-theoretical analysis reveals that tilts of anionic octahedra, as the most common type of structural distortions in perovskites, are closely related to ferroelastic strains, except for the phase with tilt pattern a + a + a +. For such kind of improper ferroelastics, the simplest classification was suggested based on the relationships between the coupled order parameter (tilts of octahedra) and the lattice strain components.

Performance Monitoring of Hybrid All-Optical Fiber/FSO Communication Systems

The demand for network capacity has increased due to the introduction of new digital applications and services, which rely heavily on optical communication networks. While fiber networks serve as the optical networks’ backbone, deploying fiber in certain scenarios is not feasible, making it necessary to use other technologies conjointly. A hybrid all-optical fiber/free space optic (FSO) link is proposed to avoid such a challenge. The all-optical system avoids using electronics that have limited bandwidth. Hence, it supports high-capacity communication. However, the all-optical system comes with challenges arising from fiber and FSO channel impairments. To monitor the amount and type of distortion in the optical channel, machine learning (ML) techniques are exploited. In this work, Gaussian process regression (GPR) is utilized as an ML technique to predict three main channel impairments that arise in the hybrid all-optical fiber/FSO channels, which are turbulence, optical signal-to-noise ratio (OSNR), and chromatic dispersion (CD). The model’s performance is evaluated using boxplot graphs, root mean square error (RMSE) metric, and R-squared metric. The results indicate that the model can predict the various impairments with high accuracy, except under strong amplified spontaneous emission (ASE) noise, where the model demonstrated lower accuracy in predicting light turbulence parameters. The proposed approach provides a self-aware and self-adaptive communication system and can optimize network resources in the future.

Indirect tire pressure monitoring method based on the fusion of time and frequency domain analysis

Monitoring tire pressure conditions and issuing timely warnings of tire deflation are important to ensure driving safety and avoid unnecessary fuel consumption. Although installing pressure sensors is a direct and accurate monitoring method, it may raise system cost and complexity. The indirect monitoring method with no reliance on pressure sensors is attractive for economical cars. This paper proposes a fusion-based indirect tire pressure monitoring method using existing on-board wheel speed sensors. The screening basis of reasonable wheel speed signals was determined by data mining of typical driving scenarios. Three typical signal distortions were effectively corrected to ensure the signal quality. Tire deflation analysis methods based on time and frequency domain signals were designed and fused using fuzzy logic. Validation results of the proposed two analysis methods and their fusion strategy showed that the proposed fusion strategy can achieve more comprehensive tire deflation monitoring with fast response and high accuracy.

Oxide Strontium-Barium Perovskites Ceramics: Examinations of Structural Phase Transitions and Potential Application as Oxygen Carriers

The structural properties of selected (Ba1−xSrx)PbO3 ceramics were examined at 14–1148 K using X-ray powder diffraction (XRD). These materials are attractive due to their variety of applications, such as, for example, high-temperature thermoelectric energy conversion. Attention was paid to this paper as a continuation of the previous examinations of higher Sr2+ concentrations. The type of perovskite distortion and temperatures of the structural phase transitions (SPTs) were determined from the splitting of certain pseudocubic lines. At this point, for example (Ba0.3Sr0.7)PbO3 showed three temperature-induced SPTs. When the amount of Sr increased in the samples, no phase transition was observed, which is contrary to the data previously demonstrated in the literature. The quality of the ceramics was examined by scanning electron microscopy-energy dispersion X-ray spectroscopy (SEM-EDS), demonstrating their homogeneity and uniform elements dispersion. As a result of profound crystal investigations, confirmed by thermogravimetric analysis and quadrupole mass spectroscopy (TGA-QMS), a phase diagram was prepared for the (Ba1−xSrx)PbO3 system based on our former and recent study. Also, the investigation of a new application for the (Ba1−xSrx)PbO3 family is presented in this paper for the first time. The TGA analysis was conducted on Illinois#6 hard coal to evaluate the capability of perovskites to be used in the chemical looping combustion (CLC) process in a range of temperatures 1073–1173 K. Due to its thermal stability and reactivity, Ba0.9Sr0.1PbO3 is the material with the greatest potential to be applied as an oxygen carrier. The combination of strontium and barium offers encouraging results compared to the pure barium and strontium lead oxide perovskites.

Q-angle and postural stability: a review of the relationship and implications for fall risk

Introduction: One of the condition common types of muscular distortion is retrieved Q-angle and postural stability. The function lower extremities with all the structures assist postural stability in most static postures and dynamic movements or activities. The changes in the Q-angle and postural stability contribute to the development risk of falling. This study aimed to examine the relationship and implications of Q-angle and postural stability for fall risk.
 Methods: This study uses a literature review by conducting data through an electronic database such as Pedro, Pubmed, and Google Scholar. They had discussed approximately Q-angle, postural stability, and fall prevention with selection by inclusion and exclusion criteria.
 Results: We discovered three research related articles. The studies examined the relationship and implications of Q-angle and postural stability for fall risk. The results of those studies, Q-angle and postural stability had a correlate and implication for fall risk.
 Conclusion: Q-angle and postural stability are related to the risk of falling. The risk of falling can be decreased by reducing the Q-angle and improving postural stability. Additionally, it can be achieved through exercise or orthotics to helps reduce the risk of falls, which will eventually avoid injuries.

The Depressed Teenage Brain: Chatbot Psychoeducation

ObjectiveTo gain the knowledge needed to develop a cognitive behavioral intervention and reliable psychoeducation applicable for conversational artificial intelligence models, we investigated the underlying constructs of thoughts common to cognitive distortions found in online questions written by adolescents with symptoms of depression. MethodsFrom June 30, 2020, to October 30, 2020, we analyzed a sample of 100 written questions from adolescents about depression posted on an online information service using a qualitative analysis guided by cognitive behavioral theory and informed by the neuroscience of adolescence. ResultsFour types of cognitive distortions (CDs) were found: (1) emotional reasoning, (2) mind reading, (3) catastrophizing, and (4) labeling. Our analysis suggested 3 underlying constructs common to the different CDs: (1) emotional states appearing as reality, (2) experiencing this emotional reality as something others think, and (3) generalizing such beliefs to every relation and the future. These constructs may signify events leading to a ruminative state that seems hard to escape. ConclusionThe 4 different CDs originate from 3 underlying constructs possibly associated with adolescent neurodevelopment. This study indicates a potential to reveal the underlying constructs of thought common to different CDs, thus making CDs more useful as a target point in artificial intelligence–based technological information and intervention tools.

Unravelling transmission ratio distortion across the bovine genome: identification of candidate regions for reproduction defects

BackgroundBiological mechanisms affecting gametogenesis, embryo development and postnatal viability have the potential to alter Mendelian inheritance expectations resulting in observable transmission ratio distortion (TRD). Although the discovery of TRD cases have been around for a long time, the current widespread and growing use of DNA technologies in the livestock industry provides a valuable resource of large genomic data with parent–offspring genotyped trios, enabling the implementation of TRD approach. In this research, the objective is to investigate TRD using SNP-by-SNP and sliding windows approaches on 441,802 genotyped Holstein cattle and 132,991 (or 47,910 phased) autosomal SNPs.ResultsThe TRD was characterized using allelic and genotypic parameterizations. Across the whole genome a total of 604 chromosomal regions showed strong significant TRD. Most (85%) of the regions presented an allelic TRD pattern with an under-representation (reduced viability) of carrier (heterozygous) offspring or with the complete or quasi-complete absence (lethality) for homozygous individuals. On the other hand, the remaining regions with genotypic TRD patterns exhibited the classical recessive inheritance or either an excess or deficiency of heterozygote offspring. Among them, the number of most relevant novel regions with strong allelic and recessive TRD patterns were 10 and 5, respectively. In addition, functional analyses revealed candidate genes regulating key biological processes associated with embryonic development and survival, DNA repair and meiotic processes, among others, providing additional biological evidence of TRD findings.ConclusionsOur results revealed the importance of implementing different TRD parameterizations to capture all types of distortions and to determine the corresponding inheritance pattern. Novel candidate genomic regions containing lethal alleles and genes with functional and biological consequences on fertility and pre- and post-natal viability were also identified, providing opportunities for improving breeding success in cattle.

Quantifying structural distortion manipulation for desired perovskite phase: Part II. Three-step workflow to reveal phase evolution logic

Distortion manipulation emerges as an efficient approach to obtain desired perovskite phases for various applications. In part I of this study, we propose a paradigm to quantify the structural distortion manipulation, which enables us to obtain desired perovskite phases by translating relevant materials research into a single mathematical question. As part II of this continuous study, we construct normalized structures by introducing all possible couplings of dominant distortions into a cubic supercell and then compare them with variously shaped primitive/conventional cells known in the database. The structure comparison demonstrates that distortions are the only cause for phase and property variations. This confirms that our proposed distortion parameters can be directly used to construct phases, providing theoretical support for the paradigm in Part I. Given the limited number of distortion types, we identify that the positional relations involved in distortion arrangements and couplings are the keys to describe numerous phases. Furtherly, a three-step workflow is proposed with core contents related to the positional relation, distortion hierarchy, and distortion-component-generation ordering in spatial dimension, respectively. The definition basis and value changes of distortion/model parameters in this workflow illustration provide guidelines about how to reveal the logic behind the perovskite phase evolution.

Functional materials design: octahedral tilts in hybrid n = 1 Ruddlesden-Popper phases.

The exciting properties offered by hybrid perovskite-related materials have motivated Liu et al. [(2023). IUCrJ, 10, 385-396] to explore the crystallography of hybrid n = 1 Ruddlesden-Popper phases. Their investigation explores the structures (and symmetries) expected to result from typical distortions and gives design strategies to target specific symmetries.

Phase congruency based on derivatives of circular symmetric Gaussian function: an efficient feature map for image quality assessment

Image quality assessment (IQA) has become a hot issue in the area of image processing, which aims to evaluate image quality automatically by a metric being consistent with subjective evaluation. The first stage of conventional IQA model design is the quality-aware feature selection. Taking advantages of early visual feature, Phase congruency (PC) operates in frequency domain to measure local structures such as edges, corners, lines, etc., by computing the local amplitudes and local energies in multiple scales. Conventional local PC features are calculated with log-Gabor-based filtrations in several orientations, and usually combined with other features for IQA model design. Generally, a directional filter is sensitive to the changes on specific direction, and insensitive to other directions. This leads to multi-directional calculation and much time consumption in practical applications. Recently, researchers suggested that spatially circular symmetric filters, such as gradient magnitude (GM) and Laplacian of Gaussian (LoG), are highly efficient quality features and hence have been widely used in various IQA model designs. With the odd-symmetric and even-symmetric properties of GM and LoG operators, the two features are a suitable pair for PC compositions and can be computed uniformly by a Gaussian function with one scale factor. In this regard, we propose to combine GM and LoG signals to construct a new PC model with non-directional property. With ability to catch different types of distortions, the proposed PC feature can be promoted to a full-reference IQA model simply with average pooling or standard deviation pooling, and shows state-of-the-art performance compared with existing methods. Furthermore, our proposed PC algorithm can take the place of conventional PC component in well-known FSIM metric, which achieves improved performance and spends less in computation cost. This study suggests that the proposed circular symmetric PC feature is a highly efficient quality feature and can be exclusively used in IQA model designs.

Waveform distortion of off-axis fishbone instability in the nonlinear magnetohydrodynamic evolution in tokamak plasmas

We have investigated the waveform distortion of energetic particle driven off-axis fishbone mode (OFM) in tokamak plasmas with kinetic magnetohydrodynamic (MHD) hybrid simulations. We extended our previous simulations (Li et al 2022 Nucl. Fusion 62 026013) by considering higher-n harmonics in the MHD fluid, where n is toroidal mode number. The waveform distortion is successfully reproduced in the simulation for both magnetic fluctuations and temperature fluctuations. It is clarified that the waveform distortion arises from the superposition of the n = 2 harmonics on the fundamental n = 1 harmonics of OFM, where the n = 2 harmonics are generated by the MHD nonlinearity from the n = 1 OFM. Two types of waveform distortion can occur depending on the phase relationship between the n = 1 and n = 2 harmonics and the relative amplitude of the n = 2 harmonics to the n = 1 harmonics. Lissajous curve analyses indicate that the wave couplings between the n = 1 and n = 2 harmonics with phase-lock and lead to ‘rising distortion’ and ‘falling distortion’, respectively. The two types of waveform distortion can be attributed to the strong shearing profile of radial MHD velocity with n = 2 around the q = 2 magnetic flux surface. The dependence of waveform distortion on viscosity is investigated. It is found that the viscosity which is needed to reproduce the waveform distortion is larger than that in the experiment.

Quantifying structural distortion manipulation for desired perovskite phase: Part I. Paradigm demonstration in tungsten oxides

Slight distortions can cause dramatic changes in the properties of crystalline perovskite materials and their derivatives. Due to the numerous types of distortions and unclarified distortion-structure relations, a quantitative distortion manipulation for the desired crystalline phase of perovskite materials suitable for various application remains challenging. Here, by establishing parameter sets to systematically describe the types, magnitudes and positional relations involved in distortions, we are able to interpret the structural regulations and manipulation strategies in 7 reported crystal systems. Through the construction of distortion-phase-property functional curves, we further propose a paradigm to quantify the structural distortion manipulation for desired perovskite phases. Using the example of perovskite-like tungsten oxides, we successfully quantify their volume shrinkage and symmetry increase during lithiation. This work verifies that the complicated research and development of perovskite materials can be simplified into a mathematical problem solving process, which will inspire researchers with different backgrounds to participate, especially mathematicians and computer scientists.

Textured Mesh Quality Assessment: Large-scale Dataset and Deep Learning-based Quality Metric

Over the past decade, three-dimensional (3D) graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience, it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of five types of compression-based distortions applied on the geometry, texture mapping, and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3,000 stimuli for which we collected 148,929 quality judgments from over 4,500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes.

A MEMS Mirror-Based Confocal Laser Endomicroscope with Image Distortion Correction

Scanning MEMS mirrors can extend confocal laser microscopy into endoscopic applications, but the practical use of MEMS mirror-based confocal endomicroscopy is hindered partially by various image distortions such as barrel, fan-shaped and nonlinear distortions. In this work, the nonlinear scanning behaviors of an electrothermal MEMS mirror are analyzed and incorporated into an optical scanning model that takes all these three types of distortions into account. The model generates a 2D spatial mapping that can be applied to correct all of the image distortions in one step without a calibration board. To experimentally validate this method, a confocal laser endomicroscope employing a two-axis scanning electrothermal MEMS micromirror is designed and constructed, and confocal fluorescence images of a patterned micro-structure are obtained with the MEMS endomicroscope. The results show that the overall image distortion is reduced by at least one order of magnitude in the length direction.

Generalized relationships between the ionic radii of octahedral cations and the b crystallographic parameter of clays and related minerals

AbstractOver several decades, a wealth of literature has been devoted to correlations between the chemistries of phyllosilicates and their crystallographic unit-cell parameter values. The c parameter is currently used because of its relation to the layer-to-layer distance, characteristic of the various families of phyllosilicates. The b parameter is also of interest because it allows measurement of the layer lateral dimensions and inherent structural adjustments. This unit-cell distance can be extracted from X-ray diffraction traces from the (06ℓ;33ℓ) diffraction region and by attributing the main diffraction peak observed to a 060 reflection, leading to the relationship b = 6.d(060). The aim of this paper is to revisit the relationships between the b value (or equivalent) of the phyllosilicate (i.e. TO, TOT and TOTO) or hydroxide (i.e. hydroxide, oxyhydroxide and layered double hydroxide) families and the layer chemistry based on a mean ionic radius R of octahedral cations, calculated as $R = \mathop \sum \nolimits_{i = 1}^n ( {r_i.x_i} )$, where ri is the ionic radius of the octahedral cation i and xi is its molar fraction over n types of octahedral cations ($\mathop \sum \nolimits_{i = 1}^n ( {x_i} ) = 1$). The data were collected from the literature and involved both natural and synthetic samples with both dioctahedral and trioctahedral structures of the octahedral sheet. The results showed that b values can be linked strongly to R, leading to suitable linear regressions for all of the studied structures. All correlations were found to be applicable irrespective of the di- or trioctahedral nature of the octahedral sheet, and these are discussed in light of (1) the lateral dimension of the octahedral sheet and (2) the dimensional misfit between the tetrahedral and octahedral sheets. For hydroxide families, all data can be gathered on a single b vs R correlation line, and the dimensional properties of the octahedral sheet can be interpreted simply based on an oxygen–cation–oxygen mean distance. For TO structures, two general b vs R correlation trends were reported, and these were assigned to two adjustment mechanisms corresponding to distinct types of tetrahedral and octahedral distortions. For the mica TOT family, two main trends were also reported, whereas the use of the synthetic mica series allowed us to demonstrate that the obtained scattering of data was mainly driven by the presence of multiple limited solid solutions. Such chemical complexity was also noted for smectites, especially regarding the tetrahedral composition and associated variability in layer charge. This variability made it difficult to propose a general regression correlating b to R values for smectites, although the regression obtained for neutral TOT layers can apply as a first-order relation. Finally, a single general b vs R correlation was obtained for chlorites, and the observed slope of the regression was interpreted according to the role played by the isolated hydroxide sheet on the evolution of the lateral dimension of the structures.

Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.

Introduction: Despite continued technological improvements, measurement errors always reduce or distort the information that any real experiment can provide to quantify cellular dynamics. This problem is particularly serious for cell signaling studies to quantify heterogeneity in single-cell gene regulation, where important RNA and protein copy numbers are themselves subject to the inherently random fluctuations of biochemical reactions. Until now, it has not been clear how measurement noise should be managed in addition to other experiment design variables (e.g., sampling size, measurement times, or perturbation levels) to ensure that collected data will provide useful insights on signaling or gene expression mechanisms of interest. Methods: We propose a computational framework that takes explicit consideration of measurement errors to analyze single-cell observations, and we derive Fisher Information Matrix (FIM)-based criteria to quantify the information value of distorted experiments. Results and Discussion: We apply this framework to analyze multiple models in the context of simulated and experimental single-cell data for a reporter gene controlled by an HIV promoter. We show that the proposed approach quantitatively predicts how different types of measurement distortions affect the accuracy and precision of model identification, and we demonstrate that the effects of these distortions can be mitigated through explicit consideration during model inference. We conclude that this reformulation of the FIM could be used effectively to design single-cell experiments to optimally harvest fluctuation information while mitigating the effects of image distortion.