Polarization Information Research Articles

Integration of tunable solar spectral directional absorption and switchable near-infrared transmission filters based on phase-change materials

In this work, we propose a one-dimensional layered structure to realize the integration of various applications of solar energy spectrum based on phase-change materials (PCMs). Through the effective combination of PCMs and the designed structure, a tunable solar spectral directional absorption and switchable near-infrared band sensor can be simultaneously obtained on one sample. The research provides creative possibility for the applications of angle and polarization information of solar spectrum and narrowband filtering sensing.

Effect of different defects on the polarization mechanism of (Nb,Ga) codoped TiO2 single crystals

Various defects have a profound impact on the dielectric properties and polarization mechanisms of (Nb,Ga) codoped TiO2 crystals. However, the research conducted in this area remains limited. To address this, we grew (Nb,Ga) codoped TiO2 crystals via the Verneuil method and investigated their dielectric properties under various atmospheric annealing conditions. Annealing in oxygen reduces the number of defects. In this case, the point defects associated with oxygen vacancies is unable to fully form the ideal complex defect clusters which can form effective pegging of free electrons. On the other hand, the defects are mainly in the form of simple defect clusters that causes hopping polarization. Annealing under mixed gas (Ar:H2 = 95%:5%) increases the number of defects and contains more free carriers, which migrate to the interface between the sample and the electrode, leading to interfacial polarization. Both hopping polarization and interfacial polarization are slow polarization, resulting in an increase in dielectric loss and a decrease in frequency stability. Annealing in air or nitrogen atmospheres forms ideal defect dipole clusters, where the electron-pinned defect dipoles (EPDD) are predominantly polarized, resulting in superior dielectric properties. It has been clarified that EPDD as the main polarization form can yield better dielectric properties. By focusing on single crystals as the research subject, we effectively eliminate the influence of grain boundaries, enabling a more accurate assessment of the effects of various crystal defects on dielectric properties. This study holds substantial implications for the advancement of TiO2-based dielectric materials, offering valuable insights into their performance optimization.

Noise-aware infrared polarization image fusion based on salient prior with attention-guided filtering network.

Infrared polarization image fusion integrates intensity and polarization information, producing a fused image that enhances visibility and captures crucial details. However, in complex environments, polarization imaging is susceptible to noise interference. Existing fusion methods typically use the infrared intensity (S0) and degree of linear polarization (DoLP) images for fusion but fail to consider the noise interference, leading to reduced performance. To cope with this problem, we propose a fusion method based on polarization salient prior, which extends DoLP by angle of polarization (AoP) and introduces polarization distance (PD) to obtain salient target features. Moreover, according to the distribution difference between S0 and DoLP features, we construct a fusion network based on attention-guided filtering, utilizing cross-attention to generate filter kernels for fusion. The quantitative and qualitative experimental results validate the effectiveness of our approach. Compared with other fusion methods, our method can effectively suppress noise interference and preserve salient target features.

Polarization-filterless polarization-sensitive polarization-multiplexed phase-shifting incoherent digital holography (P4IDH).

I propose a holography technique that detects both three-dimensional (3D) and polarization information on incoherent light without any polarization filters. Two polarization-sensitive phase-only spatial light modulators are used to generate self-interference holograms with different polarization directions. 3D images for different polarization directions are retrieved from the recorded polarization-multiplexed holograms, exploiting the developed phase-shifting interferometry and numerical refocusing. Moreover, light-use efficiency is improved in general incoherent holography because there is no polarization filter. Its validity is experimentally demonstrated.

Self-calibration algorithm for installation angle deviation of bionic polarization compound eyes.

A self-calibration algorithm based on unsupervised optimization for polarizer installation angle deviation is proposed and used in a multi-aperture bionic polarization compound eye system. To simplify calibration operation, under the condition that the calibration-polarized light information is unknown, this algorithm fully exploits redundancy and random polarization information in the scene, and uses a non-convex multi-objective discrete parameter sorting optimization method to achieve angle self-calibration. Compared with ordinary calibration procedures, the algorithm requires less stringent conditions, achieves online calibration and is more accurate. It also can be applied to camera polarization arrays, division-of-focal-plane polarization cameras, and other polarization devices.

Enhancement method for high colour polarization region under low irradiance condition

Due to the significant impact of low illumination environments on polarization imaging, polarization information is heavily influenced by noise interference. To address this issue, we propose a fusion method to enhance the high colour polarization area in low irradiance conditions. Firstly, we obtain the colour degree of linear polarization (DoLP) image, the colour angle of polarization (AoP) image, and the colour intensity (S0) image by the colour polarization camera. We normalize the S0 image. Subsequently, we map the colour AoP information in combination with the colour DoLP threshold to the H channel. The visual saliency information and brightness information of the colour DoLP image and the colour S0 image are mapped to the S channel and V channel, respectively. Finally, the data is transferred to the RGB space. Experiments show that our method surpasses several existing methods in both visual quality and quantitative measurement.

Automated in-season mapping of winter wheat in China with training data generation and model transfer

Accurate and timely information on winter wheat spatial distribution is essential for food security and environmental sustainability. However, high-quality nation-wide winter wheat products at high resolutions are still scarce around the world, and the approaches for winter wheat mapping are generally constrained by the lack of sufficient and representative training data. In this study, a knowledge-based approach based on spectral and polarization information from critical stages of winter wheat, was proposed to extract high-quality training data of winter wheat, thereby supporting winter wheat mapping with machine learning classifiers. Additionally, classification model trained by the generated training data was transferred across years to achieve the in-season mapping of winter wheat. Two-year classification scenarios based on the automated training data generation (ATDG) or model transfer (MT) were designed to evaluate the quality of automatically generated training data, the performance of model transfer, the contribution of optical and radar data, and the earliest timing for winter wheat mapping over China.With the ATDG and MT, the first 10-m resolution maps of winter wheat over China (ChinaWheat10) were produced for three consecutive years (2020 & 2021 by ATDG; 2021 & 2022 by MT). For ATDG and MT, the combined features of Sentinel-1 and Sentinel-2 yielded the highest overall accuracies with the random forest classifier. Specifically, winter wheat mapping with the ATDG achieved the highest F1-score of 0.94 for both 2020 and 2021. The MT reached a comparable F1-score of 0.94 and 0.93 for 2021 and 2022, and winter wheat maps with the F1-score of 0.93 and 0.92 could be produced as early as April (two months ahead of harvesting). Besides well-delineated winter wheat parcels, the estimated areas of ChinaWheat10 aligned well with the agricultural census data at the provincial (R2 ≥ 0.95) and municipal (R2 ≥ 0.91) levels. These findings suggest the proposed approaches have a great potential for accurate, cost-effective and high-resolution in-season mapping of winter wheat over large regions.

Computational imaging and occluded objects perception method based on polarization camera array.

Traditional optical imaging relies on light intensity information from light reflected or transmitted by an object, while polarization imaging utilizes polarization information of light. Camera array imaging is a potent computational imaging technique that enables computational imaging at any depth. However, conventional imaging methods mainly focus on removing occlusions in the foreground and targeting, with limited attention to imaging and analyzing polarization characteristics at specific depths. Conventional camera arrays cannot be used for polarization layered computational imaging. Thus, to study polarization layered imaging at various depths, we devised a flexible polarization camera array system and proposed a depth-parallax relationship model to achieve computational imaging of polarization arrays and polarization information reconstruction under varying conditions and depths. A series of experiments were conducted under diverse occlusion environments. We analyzed the distinctive characteristics of the imaging results obtained from the polarization array, employing a range of array distribution methods, materials, occlusion density, and depths. Our research successfully achieved computational imaging that incorporates a layered perception of objects. Finally, we evaluated the object region's polarization information using the gray level co-occurrence matrix feature method.

Optimal vector matching fusion method for bionic compound eye polarization compass and inertial sensor integration

The compound eyes of insects can extract the heading by sensing multi-directional polarization information. Inspired by this, the integration with bionic eye polarization compass and low-precision MEMS inertial sensors yields a new choice for autonomous navigation in a GPS-denied environment. However, the occlusion environments, such as jungles and buildings, seriously affects the attitude estimation accuracy by destroying the polarization information. Considering the above problem, this paper proposes a reliable attitude estimation method based on vector matching measurement models for integration with bionic compound eye polarization compass and low-cost MEMS inertial sensors, including the polarization vector, solar vector, and gravity vector. To realize the matching and fusion among these vector models, the vector optimization selection algorithm is designed according to the number of visible polarization sensors in the occlusion environment. Particularly, the vector optimization selection factor is designed according to the error between the measured and theoretical polarization vectors. Furthermore, when using the solar vector estimation model, the degree of the polarization is applied to improve the calculation accuracy of the solar vector. The gravity vector measurement model is employed to enhance the autonomy of the integration. Finally, the effectiveness of the proposed method is verified by simulated and outdoor experiments under tree obscuration.

Improvement of pBRDF Model for Target Surface Based on Diffraction and Transmission Effects

The polarised Bidirectional Reflectance Distribution Function (pBRDF) model relates the properties of target materials to the polarisation information of the incident and reflected light. The Priest–Germer (P-G) model was the first strictly pBRDF model to be officially released; however, some shortcomings remain. In this study, we first analyse the assumption framework of the P-G model, analyse the assumption framework to determine the imperfections in the framework, supplement the boundary conditions of the model for diffraction and transmission effects, and propose and construct a polarised pBTDF model based on the existing P-G model and parameter inversion; the output results of the model are compared with the experimental data through simulation. The results show that the intensity relative error and Degree of Linear Polarisation relative error of the target can be reduced by more than 40%, using the improved model, proving its accuracy and precision.

Computational approaches to developing the implicit media bias dataset: Assessing political orientations of nonpolitical news articles

Research on media bias has been primarily conducted a number of times of news outlets referred on political news articles, but nonpolitical articles can still convey media bias that indicates the political orientation of the news outlet. Using manual human evaluation and computational approaches, we developed and publicly released the Implicit Media Bias Dataset, which contains the political orientations of 24,576 news articles featuring nonpolitical events. News articles published in the information technology and science section of the two most biased Korean news outlets (the most conservative and the most progressive) were collected, and each article was manually evaluated by human annotators in terms of its objectiveness, fairness, unbiasedness, and political orientation. The results revealed significant differences between the articles from the conservative and progressive news outlets in these domains. Next, deep learning models trained with a large corpus of nonpolitical articles were used to identify the political orientations of the first set of articles. They achieved over 98% accuracy in classifying the articles as conservative or progressive. The findings of this study demonstrate the effectiveness of computational methods in identifying and analyzing diverse forms of polarization in society.

Mental models of dynamic systems are different: Adjusting for heterogeneous granularity

This is a methodological contribution to mental model research. It is based on the fact that people emphasize different features of complex situations. Their mental models of the situation are complex because of the situation and of interpersonal diversity. Framed by prior knowledge, they contain elements of distinct detail or granularity levels. Established comparison methods assume that granularity is standardized before elicitation. But unelicited details cannot be analyzed later. However, if elicitation includes details, some of them will be at distinct granularity levels; this leads to unequal distances between some variables. Link-based comparison methods therefore produce exaggerated distance indicators. The method presented here avoids the apparent trade-off between not capturing relevant details and bias from heterogenous granularity. It first selects a subset of variables that are on a comparable level of detail in several mental models, accounting for the frequency of these variables in subgroups. Second, it replaces the sequences of links between each pair of selected variables with a compressed link that maintains the polarity and delay information provided in each mental model. All relevant structural information of the original models is preserved. Such compressed models are constructed for each set of original models to be compared using standard methods without risking to exaggerate distance indicators. Data from a recent study with nine participants illustrates the use.

Two body final states production in electron-positron annihilation and their contributions to (g − 2)μ

In this paper, we study the processes of e+e− annihilation into two body final states, either two pseudoscalar mesons or one meson with a photon. The hadronic vacuum polarization form factors are calculated within the framework of resonance chiral theory in the energy region of E ≲ 2 GeV, with final state interactions taken into account. A joint analysis on the processes of e+e−→ π+π−, K+K−, {K}_L^0{K}_S^0 , π0γ, and ηγ has been performed, and the latest experimental data are included. Based on the vacuum polarization form factors of these processes, we estimate their contributions to the lowest order of anomalous magnetic moment of the muon, (g – 2)μ. Combined with other contributions from hadronic vacuum polarization and other interactions from the standard model, the discrepancy between theoretical prediction and experimental measurement is ∆aμ = (24.1 ± 5.4) × 10−10, i.e., 4.5σ.

A polarimetric projection-based scattering characteristics extraction tool and its application to PolSAR image classification

It is necessary to adequately extract scattering characteristics from polarimetric synthetic aperture radar (PolSAR) data for land-cover classification. Current interpretation technologies, such as target decompositions, polarimetric signatures, and polarimetric coherence patterns, have been widely used to explore polarization information. However, because limited scattering models are used to characterize all ground targets, these methods are occasionally limited in the interpretation of PolSAR data. This may reduce the accuracy of the land-cover classification. In this study, we attempted to extract the scattering characteristics through another novel method. We understand the PolSAR image interpretation as the projection of the scattering model on the measured matrix. Current methods project only a limited series of scattering models onto a measured matrix. To complement current methods of PolSAR image interpretation, we propose a polarimetric projection-based scattering characteristics extraction (PPSCE) method. It projects scattering models consisting of four changing independent polarization parameters onto the measured matrix. These scattering models, associated with the geometric and physical properties of the ground targets, correspond to the entire polarization space. The PPSCE method, meanwhile, extracts polarization information sufficiently. L-band UAVSAR, C-band Radarsat-2, and P-band AIRSAR images of classical experimental areas are used to validate the PPSCE method. The results show that the proposed PPSCE method can present polarization information that is difficult to investigate by the existing methods. In addition, the scattering features derived using the PPSCE method are superior for land-cover classification. Compared to the classification results based on the coherency matrix and Wishart classifier, the proposed method improves the overall classification accuracy by 23.56% for UAVSAR data. Compared to target decompositions and polarization signatures, the overall classification accuracy of the proposed method is increased by 29.32% (RF) and 22.63% (SVM) for UAVSAR data, 7.96% (RF) and 4.36% (RF) for Radarsat-2 and AIRSAR datasets, respectively. Furthermore, the proposed method has significant potential for soil moisture inversion. https://rgdoi.net/10.13140/RG.2.2.14091.26409.

Crop Type Mapping Based on Polarization Information of Time Series Sentinel-1 Images Using Patch-Based Neural Network

Large-scale crop mapping is of fundamental importance to tackle food security problems. SAR remote sensing has lately received great attention for crop type mapping due to its stability in the revisit cycle and is not hindered by cloud cover. However, most SAR image-classification studies focused on the application of backscattering characteristics with machine learning models, while few investigated the potential of the polarization decomposition and deep-learning models. This study investigated whether the radar polarization information mined by polarization decomposition, the patch strategy and the approaches for combining recurrent and convolutional neural networks (Conv2d + LSTM and ConvLSTM2d) could effectively improve the accuracy of crop type mapping. Sentinel-1 SLC and GRD products in 2020 were collected as data sources to extract VH, VV, VH/VV, VV + VH, Entropy, Anisotropy, and Alpha 7-dimensional features for classification. The results showed that the three-dimensional Convolutional Neural Network (Conv3d) was the best classifier with an accuracy and kappa up to 88.9% and 0.875, respectively, and the ConvLSTM2d and Conv2d + LSTM achieved the second and third position. Compared to backscatter coefficients, the polarization decomposition features could provide additional phase information for classification in the time dimension. The optimal patch size was 17, and the patch-based Conv3d outperformed the pixel-based Conv1d by 11.3% in accuracy and 0.128 in kappa. This study demonstrated the value of applying polarization decomposition features to deep-learning models and provided a strong technical support to efficient large-scale crop mapping.

Kinked tracks from Σ+ baryons as a probe of light quark polarizations

Jets initiated by energetic quarks carry in an obvious way approximate information about the original quark energy and momentum. In a less obvious way, they also carry information about the quark polarization. Previous works have shown how the polarization information can be extracted by ATLAS and CMS using events in which the quark hadronizes to an energetic baryon — Λb for b quarks, Λc for c quarks and Λ for s quarks. In this paper we extend these proposals to Σ+ baryons, which can provide sensitivity to polarizations of s and u quarks. We analyze the various aspects of how the kinked track signature of the Σ+→ pπ0 decay can be used for this purpose. We evaluate the feasibility of such measurements in toverline{t} samples.

High Light Efficiency Spectral Polarization Imaging Method Based on Mach–Zehnder Structured Liquid Crystal Tunable Filters and Variable Retarders

Liquid crystal tunable filters (LCTFs) are extensively used in hyperspectral imaging systems to obtain spectral information of target scenes. However, a typical LCTF can only filter linearly polarized light, greatly reducing the transmittance of the system and limiting its application in spectral and polarization imaging. In this paper, a spectropolarimeter using Mach–Zehnder structured LCTFs (MZ-LCTFs) combined with liquid crystal variable retarders (LCVRs) is proposed. The polarized beam splitter (PBS) can make full use of the two polarization components of the incident light to improve the transmittance of the system. Specifically, the results show that the mean pixel intensity (MPI) of spectral images is improved by 93.48% compared to a typical LCTF. Subsequently, the average signal to noise ratio (SNR) of filtered and unfiltered images when simultaneously using polarization S and P channels is increased by 2.59 dB compared to a single channel. In addition, the average Standard Deviations (STDs) of DoLP and DoCP are 0.016 and 0.018, respectively. The proposed method has the potential to be applied to obtain polarization information with high optical efficiency and a full spectrum in a wide band.

Deliberation and polarization: a multi-disciplinary review

In recent years, deliberative democracy has drawn attention as a potential way of fighting polarization. Allowing citizens to exchange arguments and viewpoints on political issues in group, can have strong conflict-mitigating effects: it can foster opinion changes (thereby overcoming idea-based polarization), and improve relations between diametrically opposed groups (thereby tackling affective forms of polarization, such as affective polarization). However, these results conflict with social psychological and communication studies which find that communicative encounters between groups can lead to further polarization and even group think. The question therefore arises under which conditions deliberative interactions between citizens can decrease polarization. Based on a multidisciplinary systematic review of the literature, which includes a wide diversity of communicative encounters ranging from short classroom discussions to multi-weekend citizen assemblies, this paper reports several findings. First, we argue that the effects of communicative encounters on polarization are conditional on how those types of communication were conceptualized across disciplines. More precisely, we find depolarizing effects when group discussions adhere to a deliberative democracy framework, and polarizing effects when they do not. Second we find that the depolarizing effects depend on several design factors that are often implemented in deliberative democracy studies. Finally, our analysis shows that that much more work needs to be done to unravel and test the exact causal mechanism(s) underlying the polarization-reducing effects of deliberation. Many potential causal mechanisms were identified, but few studies were able to adjudicate how deliberation affects polarization.

Polarization image demosaicing and RGB image enhancement for a color polarization sparse focal plane array.

The color division of focal plane (DoFP) polarization sensor structure mostly uses Bayer filter and polarization filter superimposed on each other, which makes the polarization imaging unsatisfactory in terms of photon transmission rate and information fidelity. In order to obtain high-resolution polarization images and high-quality RGB images simultaneously, we simulate a sparse division of focal plane polarization sensor structure, and seek a sweet spot of the simultaneous distribution of the Bayer filter and the polarization filters to obtain both high-resolution polarization images and high-quality RGB images. In addition, From the perspective of sparse polarization sensor imaging, leaving aside the traditional idea of polarization intensity interpolation, we propose a new sparse Stokes vector completion method, in which the network structure avoids the introduction and amplification of noise during polarization information acquisition by mapping the S1 and S2 components directly. The sparsely polarimetric image demosaicing (Sparse-PDM) model is a progressive combined structure of RGB image artifact removal enhancement network and sparsely polarimetric image completion network, which aims to compensate sparsely polarimetric Stokes parameter images with the de-artifacts RGB image as a guide, thus achieving high-quality polarization information and RGB image acquisition. Qualitative and quantitative experimental results on both self-constructed and publicly available datasets prove the superiority of our method over state-of-the-art methods.

BEYONDPLANCK

We present cosmological parameter constraints estimated using the Bayesian BEYONDPLANCK analysis framework. This method supports seamless end-to-end error propagation from raw time-ordered data onto final cosmological parameters. As a first demonstration of the method, we analyzed time-ordered Planck LFI observations, combined with selected external data (WMAP 33–61 GHz, Planck HFI DR4 353 and 857 GHz, and Haslam 408 MHz) in the form of pixelized maps that are used to break critical astrophysical degeneracies. Overall, all the results are generally in good agreement with previously reported values from Planck 2018 and WMAP, with the largest relative difference for any parameter amounting about 1σ when considering only temperature multipoles between 30 ≤ ℓ ≤ 600. In cases where there are differences, we note that the BEYONDPLANCK results are generally slightly closer to the high-ℓ HFI-dominated Planck 2018 results than previous analyses, suggesting slightly less tension between low and high multipoles. Using low-ℓ polarization information from LFI and WMAP, we find a best-fit value of τ = 0.066 ± 0.013, which is higher than the low value of τ = 0.052 ± 0.008 derived from Planck 2018 and slightly lower than the value of 0.069 ± 0.011 derived from the joint analysis of official LFI and WMAP products. Most importantly, however, we find that the uncertainty derived in the BEYONDPLANCK processing is about 30 % greater than when analyzing the official products, after taking into account the different sky coverage. We argue that this uncertainty is due to a marginalization over a more complete model of instrumental and astrophysical parameters, which results in more reliable and more rigorously defined uncertainties. We find that about 2000 Monte Carlo samples are required to achieve a robust convergence for a low-resolution cosmic microwave background (CMB) covariance matrix with 225 independent modes, and producing these samples takes about eight weeks on a modest computing cluster with 256 cores.