arXiv subjects

Xinzheng Zhang

Publications and source records attributed to Xinzheng Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Electrically tunable nonrigid moire exciton polariton supersolids at room temperature

A supersolid is a macroscopic quantum state which sustains superfluid and crystallizing structure together after breaking the U(1) symmetry and translational symmetry. On the other hand, a moire pattern can form by superimposing two periodic structures along a particular direction. Up to now, supersolids and moire states are disconnected from each other. In this work we show that exciton polariton supersolids can form moire states in a double degenerate parametric scattering process which creates two constituted supersolids with different periods in a liquid crystal microcavity. In addition, we demonstrate the nonrigidity of the moire exciton polariton supersolids by electrically tuning the wavevector and period of one supersolid component with another one being fixed. Our work finds a simple way to link moire states and supersolids, which offers to study nontrivial physics emerging from the combination of moire lattices and supersolids which can be electrically tuned at room temperature.

physics.optics↗

arXiv2025

Observation of condensed moire exciton polaritons in twisted photonic lattices at room temperature

Moire lattices attract significant attention in double-layer graphene and TMD layer heterostructures as well as in photonic crystals due to the interesting exotic physics that emerges within these structures. However, direct measurement of the moiré ground, excited states and Bloch bands in twisted photonic lattices is still illusive. In this work we report strong coupling between excitons in CsPbBr3 microplates and moire photonic modes at room temperature. Depending on the coupling strength between the nearest potential sites, we observe staggered moire polariton ground states, excited states and moire polariton bands. Phase locked moire zero (in-phase) states and moire pi (antiphase) states with different spatial distributions are measured. The moire polariton distribution can be tuned into the shape of a parallelogram by controlling the depth and width of the potential in one photonic lattice with another superimposed one fixed. In addition, moire polaritons in twisted 2D honeycomb lattices are also observed. Increasing the pumping density, we realize exciton polariton condensation in the moire potential sites of the 1D/2D twisted lattices with the coherence time of around 1.4 ps. Our work lays the foundation to study coherent moire polariton condensation in twisted photonic lattices at room temperature.

physics.optics↗

arXiv2024

Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data

Recently, an intriguing research trend for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery has arisen: using simulated data to train ATR models is a feasible solution to the issue of inadequate measured data. To close the domain gap that exists between the real and simulated data, the unsupervised domain adaptation (UDA) techniques are frequently exploited to construct ATR models. However, for UDA, the target domain lacks labeled data to direct the model training, posing a great challenge to ATR performance. To address the above problem, a semi-supervised domain adaptation (SSDA) framework has been proposed adopting progressive multi-level alignments for simulated data-aided SAR ATR. First, a progressive wavelet transform data augmentation (PWTDA) is presented by analyzing the discrepancies of wavelet decomposition sub-bands of two domain images, obtaining the domain-level alignment. Specifically, the domain gap is narrowed by mixing the wavelet transform high-frequency sub-band components. Second, we develop an asymptotic instance-prototype alignment (AIPA) strategy to push the source domain instances close to the corresponding target prototypes, aiming to achieve category-level alignment. Moreover, the consistency alignment is implemented by excavating the strong-weak augmentation consistency of both individual samples and the multi-sample relationship, enhancing the generalization capability of the model. Extensive experiments on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset, indicate that our approach obtains recognition accuracies of 99.63% and 98.91% in two common experimental settings with only one labeled sample per class of the target domain, outperforming the most advanced SSDA techniques.

cs.CV↗

arXiv2024

Energy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition

Automatic target recognition (ATR) is an important use case for synthetic aperture radar (SAR) image interpretation. Recent years have seen significant advancements in SAR ATR technology based on semi-supervised learning. However, existing semi-supervised SAR ATR algorithms show low recognition accuracy in the case of class imbalance. This work offers a non-balanced semi-supervised SAR target recognition approach using dynamic energy scores and adaptive loss. First, an energy score-based method is developed to dynamically select unlabeled samples near to the training distribution as pseudo-labels during training, assuring pseudo-label reliability in long-tailed distribution circumstances. Secondly, loss functions suitable for class imbalances are proposed, including adaptive margin perception loss and adaptive hard triplet loss, the former offsets inter-class confusion of classifiers, alleviating the imbalance issue inherent in pseudo-label generation. The latter effectively tackles the model's preference for the majority class by focusing on complex difficult samples during training. Experimental results on extremely imbalanced SAR datasets demonstrate that the proposed method performs well under the dual constraints of scarce labels and data imbalance, effectively overcoming the model bias caused by data imbalance and achieving high-precision target recognition.

cs.CV↗

arXiv2024

Soft-Matter-Based Topological Vertical Cavity Surface Emitting Lasers

Polarized topological vertical cavity surface-emitting lasers (VCSELs), as stable and efficient on-chip light sources, play an important role in the next generation of optical storage and optical communications. However, most current topological lasers demand complex design and expensive fabrication processes, and their semiconductor-based structures pose challenges for flexible device applications. By use of an analogy with two-dimensional Semenov insulators in synthetic parametric space, we design and realize a one-dimensional optical superlattice (stacked polymerized cholesteric liquid crystal films and Mylar films), thereby we demonstrate a flexible, low threshold, circularly polarized topological VCSEL with high slope efficiency. We show that such a laser maintains a good single-mode property under low pump power and inherits the transverse spatial profile of the pump laser. Thanks to the soft-matter-based flexibility, our topological VCSEL can be "attached" to substrates of various shapes, enabling desired laser properties and robust beam steering even after undergoing hundreds of bends. Our results may find applications in consumer electronics, laser scanning and displays, as well as wearable devices.

physics.optics↗

arXiv2023

Photocurrent imaging of hybrid polaritons in graphene based heterostructures

Photocurrent is arising as a powerful tool for detecting in-plane collective excitations in hybrid polariton systems. In this paper, based on the intrinsic optoelectric response of graphene, photocurrent imaging of in-plane plasmons from each graphene layer is presented in a hybrid graphene-graphene heterostructure. In combination with near-field optical signals which detect plasmons above the sample, three dimensional detection of hybrid plasmons is demonstrated. Especially, only an electronic boundary is necessary for the electrical detection of hybrid plasmons, which acts as both the photocurrent junction and plasmon reflector. Our studies would promote electrical studies of polariton related physical phenomena and pave the way towards all-electrical nano-optical processing.

physics.optics↗

arXiv2022

Light-induced dynamics of liquid-crystalline droplets on the surface of iron-doped lithium niobate crystals

We investigated the effect of a photovoltaic field generated on the surface of iron-doped lithium niobate crystals on droplets of a ferroelectric nematic liquid crystalline and a standard nematic liquid crystalline material deposited on this surface. When such assembly is illuminated with a laser beam, a wide range of dynamic phenomena are initiated. Droplets located outside the laser spot are dragged in the direction of the illuminated area, while droplets located inside the illuminated region tend to bridge each other and rearrange into tendril-like structures. In the ferroelectric nematic phase (NF) these processes take place via the formation of conical spikes evolving into jet streams, similar to the behavior of droplets of conventional dielectric liquids exposed to overcritical electric fields. However, in contrast to conventional liquids, the jet streams of the NF phase exhibit profound branching. In the nematic phase (N) of both the ferroelectric nematic and the standard nematic material, dynamic processes occur via smooth-edged continuous features typical for conventional liquids subjected to under-critical fields. The difference in dynamic behavior is attributed to the large increase of dielectric permittivity in the ferroelectric nematic phase with respect to the dielectric permittivity of the nematic phase.

cond-mat.soft↗

arXiv2022

Topological Interface-State Lasing in a Polymer-Cholesteric Liquid Crystal Superlattice

The advance of topological photonics has heralded a revolution for manipulating light as well as for the development of novel photonic devices such as topological insulator lasers. Here, we demonstrate topological lasing of circular polarization in a polymer-cholesteric liquid crystal (P-CLC) superlattice, tunable in the visible wavelength regime. By use of the femtosecond-laser direct-writing and self-assembling techniques, we establish the P-CLC superlattice with a controlled mini-band structure and a topological interface defect, thereby achieving a low threshold for robust topological lasing at about 0.4 uJ. Thanks to the chiral liquid crystal, not only the emission wavelength is thermally tuned, but the circularly polarized lasing is readily achieved. Our results bring about the possibility to realize compact and integrated topological photonic devices at low cost, as well as to engineer an ideal platform for exploring topological physics that involves light-matter interaction in soft-matter environments.

physics.optics↗

arXiv2021

Topologically tuned terahertz confinement in a nonlinear photonic chip

Compact terahertz (THz) functional devices are greatly sought after for high-speed wireless communication, biochemical sensing, and non-destructive inspection. However, conventional devices to generate and guide THz waves are afflicted with diffraction loss and disorder due to inevitable fabrication defects. Here, based on the topological protection of electromagnetic waves, we demonstrate nonlinear generation and topologically tuned confinement of THz waves in a judiciously-patterned lithium niobate chip forming a wedge-shaped Su-Schrieffer-Heeger lattice. Experimentally measured band structures provide direct visualization of the generated THz waves in momentum space, and their robustness to chiral perturbation is also analyzed and compared between topologically trivial and nontrivial regimes. Such chip-scale control of THz waves may bring about new possibilities for THz integrated topological circuits, promising for advanced photonic applications.

physics.optics↗

arXiv2021

Infrared full-Stokes polarimetry by parametric up-conversion

Polarimetry aims to measure polarization information of an optical field, providing a new freedom to enhance performance of optical metrology. Furthermore, the polarimetry in infrared (IR) range holds a promise for a wide range of academic and industrial applications because the IR light relates to unique spectral signatures of various complex chemicals. However, a primary challenge in IR applications remains lacking efficient detectors. Motivated by such a constraint, we present in this paper a nonlinear up-conversion full-Stokes IR Retrieval Polarimetry (IRP) that is able to decipher the IR polarization using a well-commercial and high-performance visible light detector. Assisted by a nonlinear sum-frequency generation (SFG) process in a lithium niobate thin film, the polarization states of the IR light are encoded into the visible SF wave. Based on a Stokes-Mueller formalism developed here, the IR polarization is successfully retrieved from SF light with high precision, and polarization imaging over the targets with either uniform or non-uniform polarization distributions are demonstrated. Our results form a fresh perspective for the design of novel advanced up-conversion polarimeter for a wide range of IR metrological applications.

physics.optics↗

arXiv2021

Active plasmon interference control on ultrafast time scales by free electrons

Interference between light waves is one of the widely known phenomena in physics, which is widely used in modern optics, ranging from precise detection at the nanoscale to gravitational-wave observation. Akin to light, both classical and quantum interferences between surface plasmon polaritons (SPPs) have been demonstrated. However, to actively control the SPP interference within subcycle in time (usually less than several femtoseconds in the visible range) is still missing, which hinders the ultimate manipulation of SPP interference on ultrafast time scale. In this paper, the interference between SPPs launched by a hole dimer, which was excited by a grazing incident free electron beam without direct contact, was manipulated through both propagation and initial phase difference control. Particularly, using cathodoluminescence spectroscopy, the appearance of higher-order interference orders was obtained through propagation phase control by increasing separation distances of the dimer. Meanwhile, the peak-valley-peak evolution at a certain wavelength through changing the accelerating voltages was observed, which originates from the initial phase difference control of hole launched SPPs. In particular, the time resolution of this kind of control is shown to be in the ultrafast attosecond (as) region. Our work suggests that fast electron beams can be an efficient tool to control polarition interference in subcycle temporal scale, which can be potentially used in ultrafast optical processing or sensing.

physics.optics↗

arXiv2020

Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning

Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detection from multi-temporal SAR images using deep learning. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.

cs.CV↗

arXiv2020

A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet

In this research, a novel robust change detection approach is presented for imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep learning. Our main contribution is to develop a novel method for generating difference image and a parallel fuzzy c-means (FCM) clustering method. The main steps of our proposed approach are as follows: 1) Inspired by convolution and pooling in deep learning, a deep difference image (DDI) is obtained based on parameterized pooling leading to better speckle suppression and feature enhancement than traditional difference images. 2) Two different parameter Sigmoid nonlinear mapping are applied to the DDI to get two mapped DDIs. Parallel FCM are utilized on these two mapped DDIs to obtain three types of pseudo-label pixels, namely, changed pixels, unchanged pixels, and intermediate pixels. 3) A PCANet with support vector machine (SVM) are trained to classify intermediate pixels to be changed or unchanged. Three imbalanced multi-temporal SAR image sets are used for change detection experiments. The experimental results demonstrate that the proposed approach is effective and robust for imbalanced SAR data, and achieve up to 99.52% change detection accuracy superior to most state-of-the-art methods.

cs.CV↗

arXiv2020

Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection

Change detection is one of the fundamental applications of synthetic aperture radar (SAR) images. However, speckle noise presented in SAR images has a much negative effect on change detection. In this research, a novel two-phase object-based deep learning approach is proposed for multi-temporal SAR image change detection. Compared with traditional methods, the proposed approach brings two main innovations. One is to classify all pixels into three categories rather than two categories: unchanged pixels, changed pixels caused by strong speckle (false changes), and changed pixels formed by real terrain variation (real changes). The other is to group neighboring pixels into segmented into superpixel objects (from pixels) such as to exploit local spatial context. Two phases are designed in the methodology: 1) Generate objects based on the simple linear iterative clustering algorithm, and discriminate these objects into changed and unchanged classes using fuzzy c-means (FCM) clustering and a deep PCANet. The prediction of this Phase is the set of changed and unchanged superpixels. 2) Deep learning on the pixel sets over the changed superpixels only, obtained in the first phase, to discriminate real changes from false changes. SLIC is employed again to achieve new superpixels in the second phase. Low rank and sparse decomposition are applied to these new superpixels to suppress speckle noise significantly. A further clustering step is applied to these new superpixels via FCM. A new PCANet is then trained to classify two kinds of changed superpixels to achieve the final change maps. Numerical experiments demonstrate that, compared with benchmark methods, the proposed approach can distinguish real changes from false changes effectively with significantly reduced false alarm rates, and achieve up to 99.71% change detection accuracy using multi-temporal SAR imagery.

cs.CV↗

arXiv2018

AFA-PredNet: The action modulation within predictive coding

The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception. Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner. Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals. In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition. In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction. Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way. In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment.

cs.RO↗

arXiv2016

A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates and even regulates the sensorimotor behaviours. These regulation can be depicted in the Bayesian framework, that is why cognitive agents are able to generate behaviours with subtle differences according to their emotion or recognize the emotion by perception. A novel recurrent neural network called recurrent neural network with parametric bias units (RNNPB) runs in three modes, constructing a two-level emotion regulated learning model, was further applied to testify this theory in two different cases.

cs.RO↗

arXiv2016

Tailoring reflection of graphene plasmons by focused ion beams

Graphene plasmons are of remarkable features that make graphene plasmon elements promising for applications to integrated photonic devices. The fabrication of graphene plasmon components and control over plasmon propagating are of fundamental important. Through near-field plasmon imaging, we demonstrate controllable modifying of the reflection of graphene plasmon at boundaries etched by ion beams. Moreover, by varying ion dose at a proper value, nature like reflection boundary can be obtained. We also investigate the influence of ion beam incident angle on plasmon reflection. To illustrate the application of ion beam etching, a simple graphene wedge-shape plasmon structure is fabricated and performs excellently, proving this technology as a simple and efficient tool for controlling graphene plasmons.

physics.optics↗

arXiv2016

Directional Generation of Graphene Plasmons by Near Field Interference

The highly unidirectional excitation of graphene plasmons (GPs) through near-field interference of orthogonally polarized dipoles is investigated. The preferred excitation direction of GPs by a single circularly polarized dipole can be simply understood with the angular momentum conservation law. Moreover, the propagation direction of GPs can be switched not only by changing the phase difference between dipoles, but also by placing the z-polarized dipole to its image position, whereas the handedness of the background field remains the same. The unidirectional excitation of GPs can be extended directly into arc graphene surface as well. Furthermore, our proposal on directional generation of GPs can be realized in a semiconductor nanowire/graphene system, where a semiconductor nanowire can mimic the circularly polarized dipole when illuminated by two orthogonally polarized plane waves.

cond-mat.mes-hall↗