arXiv subjects

Hui Cao

Publications and source records attributed to Hui Cao.

At least 55 records · Page 3Linked to original sources

arXiv2023

Theory of Stimulated Brillouin Scattering in Fibers for Highly Multimode Excitations

Stimulated Brillouin scattering (SBS) is an important nonlinear optical effect which can both enable and impede optical processes in guided wave systems. Highly multi-mode excitation of fibers has been proposed as a novel route towards efficient suppression of SBS in both active and passive fibers. To study the effects of multimode excitation generally, we develop a theory of SBS for arbitrary input excitations, fiber cross section geometries and refractive index profiles. We derive appropriate nonlinear coupled mode equations for the signal and Stokes modal amplitudes starting from vector optical and tensor acoustic equations. Using applicable approximations, we find an analytical formula for the SBS (Stokes) gain susceptibility, which takes into account the vector nature of both optical and acoustic modes exactly. We show that upon multimode excitation, the SBS power in each Stokes mode grows exponentially with a growth rate that depends parametrically on the distribution of power in the signal modes. Specializing to isotropic fibers we are able to define and calculate an effective SBS gain spectrum for any choice of multimode excitation. The peak value of this gain spectrum determines the SBS threshold, the maximum SBS-limited power that can be sent through the fiber. We show theoretically that peak SBS gain is greatly reduced by highly multimode excitation due to gain broadening and relatively weaker intermodal SBS gain. We demonstrate that equal excitation of the 160 modes of a commercially available, highly multimode circular step index fiber raises the SBS threshold by a factor of 6.5, and find comparable suppression of SBS in similar fibers with a D-shaped cross-section.

physics.optics↗

arXiv2023

Parallel random LiDAR with spatial multiplexing of a many-mode laser

We propose and experimentally demonstrate parallel LiDAR using random intensity fluctuations from a highly multimode laser. We optimize a degenerate cavity to have many spatial modes lasing simultaneously with different frequencies. Their spatio-temporal beating creates ultrafast random intensity fluctuations, which are spatially demultiplexed to generate hundreds of uncorrelated time traces for parallel ranging. The bandwidth of each channel exceeds 10 GHz, leading to a ranging resolution better than 1 cm. Our parallel random LiDAR is robust to cross-channel interference, and will facilitate high-speed 3D sensing and imaging.

physics.optics↗

arXiv2023

Breathing cluster in complex neuron-astrocyte networks

Brain activities are featured by spatially distributed neural clusters of coherent firings and a spontaneous switching of the clusters between the synchrony and asynchrony states. Evidences from {\it in vivo} experiments suggest that astrocytes, a type of glial cell regarded previously as providing only structural and metabolic supports to neurons, participate actively in brain functions and play a crucial role in regulating the neural firing activities, yet the mechanism remains unknown. Introducing astrocyte as a reservoir of the glutamate released from neuron synapses, here we propose the model of complex neuron-astrocyte network and employ it to explore the roles of astrocyte in regulating the synchronization behaviors of networked neurons. It is found that a fraction of neurons on the network can be synchronized as a cluster, while the remaining neurons are kept as desynchronized. Moreover, during the course of network evolution, the cluster is switching between the synchrony and asynchrony states intermittently, henceforth the phenomenon of ``breathing cluster". By the method of symmetry-based analysis, we conduct a theoretical investigation on the stability of the cluster and the mechanism generating the breathing activities. It is revealed that the contents of the cluster are determined by the network symmetry and the breathing activities are due to the interplay between the neural network and the astrocyte. The breathing phenomenon is demonstrated in network models of different structures and neural dynamics. The studies give insights into the cellular mechanism of astrocytes in regulating neural activities, and shed lights onto the spontaneous state switching of the neocortex.

q-bio.NC↗

arXiv2023

Spatio-temporal lasing dynamics in a Limaçon-shaped microcavity

Limaçon-shaped microdisk lasers are promising on-chip light sources with low lasing threshold and unidirectional output. We conduct an experimental study on the lasing dynamics of Limaçon-shaped semiconductor microcavities. The edge emission exhibits intensity fluctuations over a wide range of spatial and temporal scales. They result from multiple dynamic processes with different origins and occurring on different spatiotemporal scales. The dominant process is an alternate oscillation between two output beams with a period as short as a few nanoseconds.

physics.optics↗

arXiv2022

Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey

Since the 2004 DARPA Grand Challenge, the autonomous driving technology has witnessed nearly two decades of rapid development. Particularly, in recent years, with the application of new sensors and deep learning technologies extending to the autonomous field, the development of autonomous driving technology has continued to make breakthroughs. Thus, many carmakers and high-tech giants dedicated to research and system development of autonomous driving. However, as the foundation of autonomous driving, the deep learning technology faces many new security risks. The academic community has proposed deep learning countermeasures against the adversarial examples and AI backdoor, and has introduced them into the autonomous driving field for verification. Deep learning security matters to autonomous driving system security, and then matters to personal safety, which is an issue that deserves attention and research.This paper provides an summary of the concepts, developments and recent research in deep learning security technologies in autonomous driving. Firstly, we briefly introduce the deep learning framework and pipeline in the autonomous driving system, which mainly include the deep learning technologies and algorithms commonly used in this field. Moreover, we focus on the potential security threats of the deep learning based autonomous driving system in each functional layer in turn. We reviews the development of deep learning attack technologies to autonomous driving, investigates the State-of-the-Art algorithms, and reveals the potential risks. At last, we provides an outlook on deep learning security in the autonomous driving field and proposes recommendations for building a safe and trustworthy autonomous driving system.

cs.CR↗

arXiv2022

Anderson localization of electromagnetic waves in three dimensions

Anderson localization marks a halt of diffusive wave propagation in disordered systems. Despite extensive studies over the past 40 years, Anderson localization of light in three dimensions has remained elusive, leading to the question of its very existence. Recent orders-of-magnitude speed-up of finite-difference time-domain calculations allows us to conduct brute-force numerical simulations of light transport in fully disordered 3D systems with unprecedented dimension and refractive index contrast. We demonstrate three-dimensional localization of vector electromagnetic waves in random packings of metallic spheres, in sharp contrast to the absence of localization for dielectric spheres with a refractive index contrast up to 10. Our work opens a wide range of avenues in both fundamental research related to Anderson localization and potential applications using 3D localized light.

physics.optics↗

arXiv2022

Exploiting structural nonlinearity of a reconfigurable multiple-scattering system

Nonlinear optics is a rapidly growing field that has found a wide range of applications. A major limitation, however, is the demand of high power, especially for high-order nonlinearities. Here, by reconfiguring a multiple-scattering system, we introduce 'structural nonlinearity' via a nonlinear mapping between the scattering potential and the output light. Experimentally we demonstrate high-order, tunable nonlinearities at low power. The multiply-scattered light features enhanced intensity fluctuations and long-range spatial correlations. The flexibility, robustness and energy efficiency of our approach provides a versatile platform for exploring structural nonlinearities for various applications.

physics.optics↗

arXiv2022

Tailoring 3D Speckle Statistics

We experimentally generate three-dimensional speckles with customized intensity statistics. By modulating the phase front of a laser beam, far-field speckle patterns maintain the designed intensity probability density function while evolving to different spatial patterns upon axial propagation. We also create speckles with distinct intensity statistics at multiple designated planes. These results open many new possibilities, such as designing volumetric speckle statistics for three-dimensional imaging, sensing, trapping, and manipulation.

physics.optics↗

arXiv2022

Sum rules for energy deposition eigenchannels in scattering systems

In a random-scattering system, the deposition matrix maps the incident wavefront to the internal field distribution across a target volume. The corresponding eigenchannels have been used to enhance the wave energy delivered to the target. Here we find the sum rules for the eigenvalues and eigenchannels of the deposition matrix in any system geometry: including two and three-dimensional scattering systems, as well as narrow waveguides and wide slabs. We derive a number of constraints on the eigenchannel intensity distributions inside the system as well as the corresponding eigenvalues. Our results are general and applicable to random systems of arbitrary scattering strength as well as different types of waves including electromagnetic waves, acoustic waves, and matter waves.

physics.optics↗

arXiv2022

Suppressing transverse mode instability through multimode excitation in a fiber amplifier

High-power fiber laser amplifiers have enabled an increasing range of applications in industry, medicine and defense. The power scaling for narrow-band amplifiers is currently limited by the transverse modal instability. Various techniques have been developed to suppress the instability in a single or few-mode fiber in order to output a clean, collimated beam. Here we propose to use a highly multimode fiber and equal modal excitation to suppress the thermo-optical nonlinearity and instability. Our numerical simulations and theoretical analysis predict a significant reduction of dynamic coupling among the fiber modes with such excitation. When the bandwidth of a coherent seed is narrower than the spectral correlation width of the multimode fiber, the amplified light maintains high spatial coherence and can be transformed to any target pattern or focused to a diffraction-limited spot by a spatial mask at either input or output end of the amplifier. Our method simultaneously achieves high average power, narrow spectral width, and good beam quality, which are desired for fiber amplifiers in many applications.

physics.optics↗

arXiv2022

Sensitive control of broad-area semiconductor lasers by cavity shape

The ray dynamics of optical cavities exhibits bifurcation points: special geometries at which ray trajectories switch abruptly between stable and unstable. A prominent example is the Fabry-Perot cavity with two planar mirrors, which is widely employed for broad-area semiconductor lasers. Such cavities support lasing in a relatively small number of transverse modes, and the laser is highly susceptible to filamentation and irregular pulsations. Here we demonstrate experimentally that a slight deviation from this bifurcation point (planar cavity) dramatically changes the laser performance. In a near-planar cavity with two concave mirrors, the number of transverse lasing modes increases drastically. While the spatial coherence of the laser emission is reduced, the divergence angle of the output beam remains relatively narrow. Moreover, the spatio-temporal lasing dynamics becomes significantly more stable compared to that in a Fabry-Perot cavity. Our near-planar broad-area semiconductor laser has higher brightness, better directionality and hence allows shorter integration times than an incandescent lamp while featuring sufficiently low speckle contrast at the same time, making it a vastly superior light source for speckle-free imaging. Furthermore, our method of controlling spatio-temporal dynamics with extreme sensitivity near a bifurcation point may be applied to other types of high-power lasers and nonlinear dynamic systems.

physics.optics↗

arXiv2022

Coherent enhancement of optical remission in diffusive media

From the earth's crust to the human brain, remitted waves are used for sensing and imaging in a diverse range of diffusive media. Separating the source and detector increases the penetration depth of remitted light, yet rapidly decreases the signal strength, leading to a poor signal-to-noise ratio. Here, we experimentally and numerically show that wavefront shaping a laser beam incident on a diffusive sample enables an order of magnitude remission enhancement, with a penetration depth of up to 10 transport mean free paths. We develop a theoretical model which predicts the maximal-remission enhancement. Our analysis reveals a significant improvement in the sensitivity of remitted waves, to local changes of absorption deep inside diffusive media. This work illustrates the potential of coherent wavefront control for non-invasive diffuse-wave imaging applications, such as diffuse optical tomography and functional near-infrared spectroscopy.

physics.optics↗

arXiv2022

$L^p$ regularity of the Bergman projection on generalizations of the Hartogs triangle in $\mathbb{C}^{n+1}$

In this paper we investigate a class of domains $Ω^{n+1}_k =\{(z,w)\in \mathbb{C}^n\times \mathbb{C}: |z|^k < |w| < 1\}$ for $k \in \mathbb{Z}^+$ which generalizes the Hartogs triangle. we first obtain the new explicit formulas for the Bergman kernel function on these domains and further give a range of $p$ values for which the $L^p$ boundedness of the Bergman projection holds. This range of $p$ is shown to be sharp.

math.CV↗

arXiv2022

Contextualize differential privacy in image database: a lightweight image differential privacy approach based on principle component analysis inverse

Differential privacy (DP) has been the de-facto standard to preserve privacy-sensitive information in database. Nevertheless, there lacks a clear and convincing contextualization of DP in image database, where individual images' indistinguishable contribution to a certain analysis can be achieved and observed when DP is exerted. As a result, the privacy-accuracy trade-off due to integrating DP is insufficiently demonstrated in the context of differentially-private image database. This work aims at contextualizing DP in image database by an explicit and intuitive demonstration of integrating conceptional differential privacy with images. To this end, we design a lightweight approach dedicating to privatizing image database as a whole and preserving the statistical semantics of the image database to an adjustable level, while making individual images' contribution to such statistics indistinguishable. The designed approach leverages principle component analysis (PCA) to reduce the raw image with large amount of attributes to a lower dimensional space whereby DP is performed, so as to decrease the DP load of calculating sensitivity attribute-by-attribute. The DP-exerted image data, which is not visible in its privatized format, is visualized through PCA inverse such that both a human and machine inspector can evaluate the privatization and quantify the privacy-accuracy trade-off in an analysis on the privatized image database. Using the devised approach, we demonstrate the contextualization of DP in images by two use cases based on deep learning models, where we show the indistinguishability of individual images induced by DP and the privatized images' retention of statistical semantics in deep learning tasks, which is elaborated by quantitative analyses on the privacy-accuracy trade-off under different privatization settings.

cs.CR↗

arXiv2022

Research on Dual Channel News Headline Classification Based on ERNIE Pre-training Model

The classification of news headlines is an important direction in the field of NLP, and its data has the characteristics of compactness, uniqueness and various forms. Aiming at the problem that the traditional neural network model cannot adequately capture the underlying feature information of the data and cannot jointly extract key global features and deep local features, a dual-channel network model DC-EBAD based on the ERNIE pre-training model is proposed. Use ERNIE to extract the lexical, semantic and contextual feature information at the bottom of the text, generate dynamic word vector representations fused with context, and then use the BiLSTM-AT network channel to secondary extract the global features of the data and use the attention mechanism to give key parts higher The weight of the DPCNN channel is used to overcome the long-distance text dependence problem and obtain deep local features. The local and global feature vectors are spliced, and finally passed to the fully connected layer, and the final classification result is output through Softmax. The experimental results show that the proposed model improves the accuracy, precision and F1-score of news headline classification compared with the traditional neural network model and the single-channel model under the same conditions. It can be seen that it can perform well in the multi-classification application of news headline text under large data volume.

cs.CL↗

arXiv2021

Roadmap on Wavefront Shaping and deep imaging in complex media

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at depth in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead.

physics.optics↗

arXiv2021

Controlling nonlinear interaction in a many-mode laser by tuning disorder

A many-mode laser with nonlinear modal interaction could serve as a model system to study many-body physics. However, precise and continuous tuning of the interaction strength over a wide range is challenging. Here, we present a unique method for controlling lasing mode structures by introducing random phase fluctuation to a nearly degenerate cavity. We show numerically and experimentally that as the characteristic scale of phase fluctuation decreases by two orders of magnitude, the transverse modes become fragmented and the reduction of their spatial overlap suppresses modal competition for gain, allowing more modes to lase. The tunability, flexibility and robustness of our system provides a powerful platform for investigating many-body phenomena.

physics.optics↗

arXiv2021

SN-Graph: a Minimalist 3D Object Representation for Classification

Using deep learning techniques to process 3D objects has achieved many successes. However, few methods focus on the representation of 3D objects, which could be more effective for specific tasks than traditional representations, such as point clouds, voxels, and multi-view images. In this paper, we propose a Sphere Node Graph (SN-Graph) to represent 3D objects. Specifically, we extract a certain number of internal spheres (as nodes) from the signed distance field (SDF), and then establish connections (as edges) among the sphere nodes to construct a graph, which is seamlessly suitable for 3D analysis using graph neural network (GNN). Experiments conducted on the ModelNet40 dataset show that when there are fewer nodes in the graph or the tested objects are rotated arbitrarily, the classification accuracy of SN-Graph is significantly higher than the state-of-the-art methods.

cs.CV↗