Polar Transformation Research Articles

GeoDTR+: Toward Generic Cross-View Geolocalization via Geometric Disentanglement.

Cross-View Geo-Localization (CVGL) estimates the location of a ground image by matching it to a geo-tagged aerial image in a database. Recent works achieve outstanding progress on CVGL benchmarks. However, existing methods still suffer from poor performance in cross-area evaluation, in which the training and testing data are captured from completely distinct areas. We attribute this deficiency to the lack of ability to extract the geometric layout of visual features and models' overfitting to low-level details. Our preliminary work (Zhang et al. 2022) introduced a Geometric Layout Extractor (GLE) to capture the geometric layout from input features. However, the previous GLE does not fully exploit information in the input feature. In this work, we propose GeoDTR+ with an enhanced GLE module that better models the correlations among visual features. To fully explore the LS techniques from our preliminary work, we further propose Contrastive Hard Samples Generation (CHSG) to facilitate model training. Extensive experiments show that GeoDTR+ achieves state-of-the-art (SOTA) results in cross-area evaluation on CVUSA (Workman et al. 2015), CVACT (Liu and Li, 2019), and VIGOR (Zhu et al. 2021) by a large margin (16.44%, 22.71%, and 13.66% without polar transformation) while keeping the same-area performance comparable to existing SOTA. Moreover, we provide detailed analyses of GeoDTR+.

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Abstract To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building’s refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

RP-Net: A Robust Polar Transformation Network for rotation-invariant face detection

Face detection is challenging in unconstrained environments, where it encounters various challenges such as orientation, pose, and occlusion. Deep convolutional neural networks, particularly cascaded ones, have greatly improved detection performance but still struggle with rotating objects due to limitations in the Cartesian coordinate system. Although data augmentation can mitigate this issue, it also increases computational demands. This paper introduces the Robust Polar Transformation Network (RP-Net) for rotation-invariant face detection. RP-Net converts the complex rotational problem into a simpler translational one to enhance feature extraction and computational efficiency. Additionally, the Advanced Spatial-Channel Restoration (ASCR) module optimizes facial landmark detection within polar domains and restores critical details lost during transformation. Experimental results on benchmark datasets show that RP-Net significantly improves rotation invariance over traditional CNNs and surpasses several state-of-the-art rotation-invariant face detection methods.

On the Equivalence of Phase and Polarization Transformations in Quantum Optics

Relations have been derived to prove that field correlation functions for polarization and phase transformations of radiation states in quantum optics are equivalent in measurements.

Optimizing the polar phase transformation in PVDF-HFP/BaTiO3-activated carbon composite films with the mixture design of experiments

ABSTRACT A salient bottleneck to driving the wider use of polyvinylidene fluoride (PVDF) and its copolymers in smart applications is the requirement for a high-voltage poling process required to activate their β -phase crystal structure before the realisation of the piezoelectricity response. In pursuit of enhancing the β-phase content of PVDF-based films, various approaches have been employed in recent years. Yet, a perfect solution to the challenge remains elusive. Here, we report a preliminary investigation into the production of unpoled poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) films reinforced with barium titanate (BaTiO3) and activated carbon (AC) to improve the piezoelectric property. Employing the mixture design of experiments (MDOE) method, various formulations of the composite’s constituents weight fractions were determined. Afterwards, series of composite films were produced via a toxic-free solution casting method and characterised by Fourier Transform Infrared Spectrum (FTIR) to determine the β-phase content. A non-linear polynomial regression model was established from the experimental FTIR data, and the adequacy of the model was confirmed using ANOVA. Subsequently, an MDOE-enabled optimisation was undertaken, generating a formulation of the composite to obtain the highest β-phase content to be 15 wt.% BaTiO3 and 0.74 wt.% AC. This combination produces a theoretically predicted β-phase content of 72.7 %. A further experimental test employing the optimal formulation produced a β-phase content of 71.90 %, achieved with neither mechanical stretching, thermal annealing, nor electrical poling.

Polarization-Mode Transformation of the Light Field during Diffraction on Amplitude Binary Gratings

In this paper, a comparative analysis and numerical simulation of operation of two types of amplitude binary gratings (conventional and fork), both in the focal plane and near-field diffraction under illumination by mode beams with different polarization states, were performed. The simulation of the field formation in the focal plane was performed using the Richards–Wolf formalism. The diffraction calculation in the near-field diffraction was performed based on the FDTD method, considering the 3D structure of optical elements. The possibility of multiplying the incident beam in different diffraction orders of binary gratings and the polarization transformation associated with spin–orbit interaction at tight focusing were shown. In this case, various polarization transformations were formed in ±1 diffraction orders of the fork grating due to different signs of the introduced vortex-like phase singularity. The obtained results can be useful for the laser processing of materials and surface structuring.

Precisely Tailoring WSe2 Polarity via van der Waals Bismuth-Gold Modulated Contact.

Creating high-quality contacts between high-melting-point metals and delicate two-dimensional (2D) semiconductors poses a critical challenge to polarity control due to inevitable chemical disorder and Fermi-level pinning observed in the contact regions. Here, we report a van der Waals (vdW) integration strategy to precisely tailor the WSe2 polarity by meticulously modulating metal contact compositions. Controlling the low-melting-point bismuth (Bi) thickness effectively modulates the Bi/Au dominant contact with WSe2. This facilitates the precise polarity transformation between n-type, ambipolar, and p-type, with exceptional field-effect mobilities of 200 cm2 V-1 s-1 for electrons and 136 cm2 V-1 s-1 for holes. Within this vdW geometry, we further demonstrate the fundamental electrical components such as diodes and complementary inverters with enhanced rectification ratios and voltage gains. Our results showcase an effective and compatible with mass manufacturing method for precise polarity modulation of 2D semiconductors, providing a promising pathway toward large-scale high-performance 2D electronics and integrated circuits.

Deep neural network-enabled multifunctional switchable terahertz metamaterial devices

Under the support of deep neural networks (DNN), a multifunctional switchable terahertz metamaterial (THz MMs) device is designed and optimized. This device not only achieves ideal ultra-wideband (UWB) absorption in the THz frequency range but enables dual-functional polarization transformation over UWB. When vanadium dioxide (VO2) is in the metallic state, the device as a UWB absorber with an absorption rate exceeding 90% in the 2.43–10 THz range, with a relative bandwidth (RBW) of 145.2%, and its wideband absorption performance is insensitive to polarization. When VO2 is in the insulating state, the device can switch to a polarization converter, achieving conversions from linear to cross polarization and from linear to circular polarization in the ranges of 4.58–10 THz and 4.16–4.43 THz, respectively. Within the 4.58–10 THz range, the polarization conversion ratio approaches 100% with an RBW of 74.3%, the polarization rotation angle is near 90°. Within the 4.16–4.43 THz range, the RBW is 6.29% and the ellipticity ratio approaches 1, Moreover, the effects of incident angle and polarization angle on the operational characteristics are studied. This THz MMs due to its advantages of wide angle, broad bandwidth, and high efficiency, provides valuable references for the research of new multifunctional THz devices. It has great application potential in short-range wireless THz communication, ultrafast optical switches, high-temperature resistant switches, transient spectroscopy, and optical polarization control devices.

Terahertz wide-band wide-angle absorber and polarization converter based on VO2 metamaterial

In this study, a Terahertz wide-band wide-angle absorber and polarization converter based on VO2 metamaterial is proposed. The device is simulated by using full-wave electromagnetic computing software CST. The device functions as a wide-band absorber while VO2 is in the metallic mode. In the 1.82 THz to 4.27 THz band, the absorption efficiency exceeds 90% and the relative bandwidth reaches 81.4%. The absorption mechanism is analyzed bying equivalent circuit theory. Additionally, the absorber can work at large polarization angle and incident angle. While VO2 is in the insulated mode, the design can convert the incident y-polarized terahertz wave into the x-polarized state and realize the transformation of linear polarization. Polarization conversion efficiency (PCR) exceeds 90% in the band of 1.57 THz to 3.82 THz, with a relative bandwidth of 83.5%. The device also supports large incidence angle tolerances. These results show that the disigned metamaterial device has potential applications in absorption, polarization conversion and imaging.

Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net

The performance of existing lesion semantic segmentation models has shown a steady improvement with the introduction of mechanisms like attention, skip connections, and deep supervision. However, these advancements often come at the expense of computational requirements, necessitating powerful graphics processing units with substantial video memory. Consequently, certain models may exhibit poor or non-existent performance on more affordable edge devices, such as smartphones and other point-of-care devices. To tackle this challenge, our paper introduces a lesion segmentation model with a low parameter count and minimal operations. This model incorporates polar transformations to simplify images, facilitating faster training and improved performance. We leverage the characteristics of polar images by directing the model’s focus to areas most likely to contain segmentation information, achieved through the introduction of a learning-efficient polar-based contrast attention (PCA). This design utilizes Hadamard products to implement a lightweight attention mechanism without significantly increasing model parameters and complexities. Furthermore, we present a novel skip cross-channel aggregation (SC2A) approach for sharing cross-channel corrections, introducing Gaussian depthwise convolution to enhance nonlinearity. Extensive experiments on the ISIC 2018 and Kvasir datasets demonstrate that our model surpasses state-of-the-art models while maintaining only about 25K parameters. Additionally, our proposed model exhibits strong generalization to cross-domain data, as confirmed through experiments on the PH2 dataset and CVC-Polyp dataset. In addition, we evaluate the model’s performance in a mobile setting against other lightweight models. Notably, our proposed model outperforms other advanced models in terms of IoU and Dice score, and running time.

DCFF-Net: Deep Context Feature Fusion Network for High-Precision Classification of Hyperspectral Image

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar co-ordinate transformation method was employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) was utilized to classify these feature maps. The model was validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieved excellent classification performance. Experimental results on three public HSI datasets demonstrated that the proposed method accurately recognized different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

Topological Analysis of Image Reconstruction Based on Polar Complex Exponential Transform

Image moments are an important tool used for image reconstruction. An image can be represented in terms of image moments, which is known as image reconstruction from its moments. To construct an image from the moments, a question often arises that how many moments are required for the reconstruction of image. Theoretically, many image moments may be required for accurately reconstructing an image. However, since image moment construction can be computationally challenging, often in practice only a finite number of moments are used for the image reconstruction. The difference in reconstructed and original image for many numbers of images can lead to a satisfying answer. However, accurate reconstruction may not often be possible, and we are often relying on other approaches to find similarity between the original image and reconstructed image. We used a similarity based on a topological data analysis tool known as the persistence diagram, determining the bottleneck distance between the original and the reconstructed image as the measure of similarity. Our investigation was conducted on the common images utilized in image processing tasks. The findings indicate that there is no direct correlation between the number of moments and the quality of image reconstruction. It is necessary to choose an appropriate number of moments, which may be very small, instead of calculating a high number of image moments.

A Power Optimization Approach for Large-scale RM-TB Dual Logic Circuits Based on an Adaptive Multi-Task Intelligent Algorithm

Logic synthesis is a crucial step in integrated circuit design, and power optimization is an indispensable part of this process. However, power optimization for large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits is an NP-hard problem. In this article, we divide Boolean circuits into small-scale circuits based on the idea of divide and conquer using the proposed Dynamic Adaptive Grouping Strategy (DAGS) and the proposed circuit decomposition model (CDM). Each small-scale Boolean circuit is transformed into an MPRM logic circuit by a polarity transformation algorithm. Based on the gate-level integration, we integrate small-scale circuits into an MPRM and Boolean Dual Logic (RBDL) circuit. Furthermore, the power optimization problem of RBDL circuits is a multi-task, multi-extremal, high-dimensional combinatorial optimization problem, for which we propose an Adaptive Multi-task Intelligent Algorithm (AMIA), which includes global task optimization, population reproduction, valuable knowledge transfer (VKT), and local exploration to search for the lowest power for RBDL circuits. Moreover, based on the proposed Fast Power Decomposition Algorithm (FPDA), we proposed a Power Optimization Approach (POA) for an RBDL circuit with the lowest power using the AMIA. Experimental results based on Microelectronics Center of North Carolina (MCNC) Benchmark test circuits demonstrate the effectiveness and superiority of the POA compared to state-of-the-art POAes.

Reaching the efficiency limit of arbitrary polarization transformation with non-orthogonal metasurfaces

Polarization transformation is at the foundation of modern applications in photonics and quantum optics. Notwithstanding their applicative interests, basic theoretical and experimental efforts are still needed to exploit the full potential of polarization optics. Here, we reveal that the coherent superposition of two non-orthogonal eigen-states of Jones matrix can improve drastically the efficiency of arbitrary polarization transformation with respect to classical orthogonal polarization optics. By exploiting metasurface with stacking and twisted configuration, we have implemented a powerful configuration, termed “non-orthogonal metasurfaces”, and have experimentally demonstrated arbitrary input-output polarization modulation reaching nearly 100% transmission efficiency in a broadband and angle-insensitive manner. Additionally, we have proposed a routing methodology to project independent phase holograms with quadruplex circular polarization components. Our results outline a powerful paradigm to achieve extremely efficient polarization optics, and polarization multiplexing for communication and information encryption at microwave and optical frequencies.

All‐Dielectric Meta‐Waveguides for Flexible Polarization Control of Guided Light

AbstractGuided waves are the perfect carriers of electromagnetic signals in planar miniaturized devices due to their high localization and controlled propagation direction. However, it is still a challenge to control the polarization of propagating guided waves. In this work, the broadband polarization TE‐TM degeneracy of highly localized guided waves propagating along an all‐dielectric metasurface and a subwavelength chain of dielectric high‐index cylinders is discovered, both theoretically and experimentally. Using the discovered near‐field polarization degree of freedom, the polarization transformation for guided waves propagating along a subwavelength chain of dielectric cylinders at any frequency within the finite spectral range is demonstrated experimentally. Namely, the simplest planar near‐field polarization device – the quarter‐wave‐retardation (linear‐to‐circular) polarization transformer of guided waves is implemented. The results obtained discover the polarization degree of freedom for guided waves paving the way toward numerous applications in flat optics and planar photonics.

Withaferin A protects against epilepsy by promoting LCN2-mediated astrocyte polarization to stopping neuronal ferroptosis

BackgroundEpilepsy is among the most frequent severe brain diseases, with few treatment options available. Neuronal ferroptosis is an important pathogenic mechanism in epilepsy. As a result, addressing ferroptosis appears to be a promising treatment approach for epilepsy. Withaferin A (WFA) is a C28 steroidal lactone that has a broad range of neuroprotective properties. Nonetheless, the antiepileptic action of WFA and the intrinsic mechanism by which it inhibits ferroptosis following epilepsy remain unknown. PurposeThis study aimed at investigating to the antiepileptic potential of WFA in epilepsy, as well as to propose a potential therapeutic approach for epilepsy therapy. MethodsWe conducted extensive research to examine the impacts of WFA on epilepsy and ferroptosis, using the kainic acid (KA)-treated primary astrocyte as an in vitro model and KA-induced temporal lobe epilepsy mice as an in vivo model. To analyze the neuroprotective effects of WFA on epileptic mice, electroencephalogram (EEG) recording, Nissl staining, and neurological function assessments such as the Morris water maze (MWM) test, Y-maze test, Elevated-plus maze (O-maze) test, and Open field test were used. Furthermore, the mechanism behind the neuroprotective effect of WFA in epilepsy was investigated using the transcriptomics analysis and verified on epileptic patient and epileptic mouse samples using Western blotting (WB) and immunofluorescence (IF) staining. In addition, WB, IF staining and specific antagonists/agonists were used to investigate astrocyte polarization and the regulatory signaling pathways involved. More critically, ferroptosis was assessed utilizing lipocalin-2 (LCN2) overexpression cell lines, siRNA knockdown, JC-1 staining, WB, IF staining, flow cytometry, electron microscopy (TEM), and ferroptosis-related GSH and MDA indicators. ResultsIn this study, we observed that WFA treatment reduced the number of recurrent seizures and time in seizure, and the loss of neurons in the hippocampal area in in epileptic mice, and even improved cognitive and anxiety impairment after epilepsy in a dose depend. Furthermore, WFA treatment was proven to enhance to the transformation of post-epileptic astrocytes from neurotoxic-type A1 to A2 astrocytes in both in vivo and in vitro experiments by inhibiting the phosphoinositide 3-kinase /AKT signaling pathway. At last, transcriptomics analysis in combination with functional experimental validation, it was discovered that WFA promoted astrocyte polarity transformation and then LCN2 in astrocytes, which inhibited neuronal ferroptosis to exert neuroprotective effects after epilepsy. In addition, we discovered significant astrocytic LCN2 expression in human TLE patient hippocampal samples. ConclusionsTaken together, for the first, our findings suggest that WFA has neuroprotective benefits in epilepsy by modulating astrocyte polarization, and that LCN2 may be a novel potential target for the prevention and treatment of ferroptosis after epilepsy.

Generation of terahertz beam with longitudinally varied polarization state via coherent superposition based on metasurface.

Polarization is an important dimension in the research and applications of light waves. However, traditional polarization optics often only focus on the polarization characteristics in the transverse plane. Here, we demonstrate a new scheme for the generation of longitudinally varied polarization state in terahertz beam using all-silicon metasurface. We employ wavefront transformation designs with long-focal-depth for orthogonal circularly polarized terahertz waves, achieving varied amplitude and phase along the propagation direction in opposite spin states. Based on the principle of coherent superposition of polarized waves, different linear and elliptical polarization states are obtained in transverse planes along the propagation path, with variable ellipticity and azimuth angle. Simulation results show that a large-scale evolution of the elliptical polarization azimuth angle from 45° to -60° and ellipticity from 20° to -74° can be observed within a focal depth range of 0.45-0.8 mm. We also intuitively display the helical trajectory of the polarization state from left-hand elliptical ones to right-hand elliptical ones within the focal depth range, using the Poincaré sphere. This work expands the application of metasurface devices for multifunctional polarization devices and can be applied to polarization generation and transformation for optical imaging or terahertz communications.

Forward-backward wavefront manipulation of orthogonal linearly polarized waves based on a metasurface zone plate.

Metasurface zone plates exhibit stronger optical control capabilities than traditional Fresnel zone plates, especially in polarization transformation and multiplexing. However, there are still few studies on metasurface zone plates that can be used for simultaneous control of forward and backward waves. In this work, we propose what is to our knowledge a new scheme that utilizes metasurface zone plates for orthogonal linear polarization separation and wavefront manipulation at the same time. We demonstrate the separation of linearly polarized components and transmission-reflection focusing by using the destructive and constructive interference between different meta-atoms in the super-cell, as well as the phase difference between the super-cells. The metasurface not only needs a simple binary phase design but also shows a working bandwidth more than 30 nm with a central wavelength of 875 nm. This scheme can be extended to other electromagnetic bands such as visible and terahertz ones, providing an important way for the multi-dimensional light field manipulations.

Gaussian quadrature for certain two-dimensional hypersingular integrals

In this paper, the Gaussian quadrature formula is used to approximately calculate two-dimensional hypersingular integrals of the form ∬Sf(x,y)r3dS on the circular domain. The two-dimensional hypersingular integrals are changed into iterated integrals by polar transformation. The inner layer of the integral has singularity and Gaussian quadrature formula is constructed for this part. The outer layer without singularity is calculated by Gauss–Legendre quadrature formula of Riemann integral. The corresponding theoretical analysis is given. Furthermore, this type of two-dimensional hypersingular integral is extended to the form ∬Sf(x,y)rp+1dS(p>2) and an error estimate is also derived. Several numerical examples are presented to verify the effectiveness of the proposed algorithm, no matter the singularity is located at the center or near the boundary of the circular domain.

Optimized pyroelectric behaviors by constructing the nonergodic-ergodic transition state

Bi0.5Na0.5TiO3 (BNT)-based relaxors are considered as potential candidates for pyroelectric applications due to their excellent pyroelectric performances. However, the excellent pyroelectric response in BNT-based relaxors is generally accompanied by a rapid decrease in the depolarization temperature Td, limiting the operation temperature. Herein, we find that the mixed non-ergodic and ergodic relaxor states (a kind of transition system) can promote polarization rotation and remain the reversibility of polarization transformation simultaneously, thereby improving pyroelectric performances and maintaining the Td . The Li+-doped BNKT-based relaxors which possess such a transition state exhibit excellent room temperature (RT) pyroelectric coefficients (1247 μCm−2K−1), and also, the Td can maintain at 69℃. This work provides a strategy for further insight into the enhanced pyroelectric response, which promotes the development of eco-friendly pyroelectric applications (such as infrared detection, temperature sensors, and pyroelectric energy harvesting).