arXiv subjects

Mingjian Cheng

Publications and source records attributed to Mingjian Cheng.

7 recordsLinked to original sources

arXiv2026

Seeing full vectorial structures of light fields with a single-shot holographic multiplexed detector

The vectorial structure of light, amplitude, phase, and polarization, encodes essential information for applications ranging from super-resolution microscopy to high-capacity communications and quantum information processing. However, existing characterization methods either rely on multiple sequential measurements or require bulky polarization splitting optics in the signal path. Here we propose and experimentally demonstrate a single shot holographic multiplexed detector that retrieves the full vectorial information from a single intensity recording. Two orthogonally polarized reference beams with distinct off axis carriers interfere with the unknown vectorial light field, encoding both polarization channels into one off axis hologram. Digital holographic reconstruction combined with a self calibrated global phase retrieval recovers the complex wavefronts in the two channels without any additional measurements. We validate our approach by characterizing the polarization structures and concurrence of various vectorial structured light beams on a higher order Poincare sphere (l=2). This compact, efficient detector may open new routes for real time vectorial metrology in light matter interaction, chiral sensing, vectorial adaptive optics, and dynamic structured light applications.

physics.optics↗

arXiv2026

Fractional optical skyrmions

Optical topologies in the form of Skyrmions have attracted significant interest of late, where their integer Skyrmion number has been shown to be robust to complex media. Here we create the first fractional Skyrmions by structuring light as a vectorial superposition of non-integer orbital angular momentum. We unravel the map structure to reveal a new phenomenon, the abrupt transition jumps in skyrmion number, which serves to reinforce the integer nature of skyrmion topologies. Our experimental demonstration agrees well with simulation, opening a new spectrum of optical topologies to explore, with exciting possibilities in optical communication and sensing.

physics.optics↗

arXiv2025

Spatially-Resolved Atmospheric Turbulence Sensing with Two-Dimensional Orbital Angular Momentum Spectroscopy

Atmospheric turbulence characterization is crucial for technologies like free-space optical communications. Existing methods using a spatially-integrated one-dimensional (1D) orbital angular momentum (OAM) spectrum, P(m), obscure the heterogeneous nature of atmospheric distortions. This study introduces a two-dimensional (2D) OAM spectroscopy, P(m, n), which resolves the OAM spectrum (topological charge m) across discrete radial annuli (index n). Integrating this high-dimensional spectral analysis with a Support Vector Machine (SVM) classifier significantly improves the accuracy of atmospheric turbulence parameter inversion. The full potential of complex probe beams, such as multi-ringed Bessel-Gaussian beams, is realized with this radially-resolved 2D analysis. Through a co-design of the probe beam's spatial structure and the OAM spectral analysis dimensionality, a median classification accuracy of 85.47% was achieved across 20 turbulence conditions, a 23% absolute improvement over 1D techniques. The radial index also mitigates insufficient OAM spectral range, and a targeted feature-selection protocol addresses noise from low signal-to-noise ratio outer radial regions. This framework emphasizes co-design of the optical probe field and its OAM spectral analysis for enhanced fidelity in turbulence characterization.

physics.optics↗

arXiv2025

Machine learning assisted speckle and OAM spectrum analysis for enhanced turbulence characterisation

Atmospheric turbulence degrades the performance of free-space optical (FSO) communication and remote sensing systems by introducing phase and intensity distortions. While a majority of research focuses on mitigating these effects to ensure robust signal transmission, an underexplored alternative is to leverage the transformation of structured light to characterize the turbulent medium itself. Here, we introduce a deep learning framework that fuses post-propagation intensity speckle patterns and orbital angular momentum (OAM) spectral data for atmospheric turbulence parameter inference. Our architecture, based on a modified InceptionNet backbone, is optimized to extract and integrate multi-scale features from these distinct optical modalities. This multimodal approach achieves validation accuracies exceeding 80%, substantially outperforming conventional single-modality baselines. The framework demonstrates high inference accuracy and enhanced training stability across a broad range of simulated turbulent conditions, quantified by varying Fried parameters (r0) and Reynolds numbers (Re). This work presents a scalable and data-efficient method for turbulence characterization, offering a pathway toward robust environmental sensing and the optimization of dynamic FSO systems.

physics.optics↗

arXiv2024

General Scintillation for Gaussian Beam Propagating through Oceanic Turbulence and UWOC System Performance Evaluation

In this paper, we derive a general and exact closed-form expression of scintillation index (SI) for a Gaussian beam propagating through weak oceanic turbulence, based on the general oceanic turbulence optical power spectrum (OTOPS) and the Rytov theory. Our universal expression not only includes existing Rytov variances but also accounts for actual cases where the Kolmogorov microscale is non-zero. The correctness and accuracy of our derivation are verified through comparison with the published work under identical conditions. By utilizing our derived expressions, we analyze the impact of various beam, propagation and oceanic turbulence parameters on both SI and bit error rate (BER) performance of underwater wireless optical communication (UWOC) systems. Numerical results demonstrate that the relationship between the Kolmogorov microscale and SI is nonlinear. Additionally, considering that certain oceanic turbulence parameters are related to depth, we use temperature and salinity data from Argo buoy deployed in real oceans to investigate the dependence of SI on depth. Our findings will contribute to the design and optimization of UWOC systems.

physics.optics↗

arXiv2024

Environmental monitoring using orbital angular momentum mode decomposition enhanced machine learning

Atmospheric interaction with light has been an area of fascination for many researchers over the last century. Environmental conditions, such as temperature and wind speed, heavily influence the complex and rapidly varying optical distortions propagating optical fields experience. The continuous random phase fluctuations commonly make deciphering the exact origins of specific optical aberrations challenging. The generation of eddies is a major contributor to atmospheric turbulence, similar in geometric structure to optical vortices that sit at the centre of OAM beams. Decomposing the received optical fields into OAM provides a unique spatial similarity that can be used to analyse turbulent channels. In this work, we present a novel mode decomposition assisted machine learning approach that reveals trainable features in the distortions of vortex beams that allow for effective environmental monitoring. This novel technique can be used reliably with Support Vector Machine regression models to measure temperature variations of 0.49C and wind speed variations of 0.029 m/s over a 36m experimental turbulent free-space channels with controllable and verifiable temperature and wind speed with short 3s measurement. The predictable nature of these findings could indicate the presence of an underlying physical relationship between environmental conditions that lead to specific eddy formation and the OAM spiral spectra.

physics.optics↗

arXiv2020

Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units

Recurrent neural network (RNN) has been widely studied in sequence learning tasks, while the mainstream models (e.g., LSTM and GRU) rely on the gating mechanism (in control of how information flows between hidden states). However, the vanilla gates in RNN (e.g., the input gate in LSTM) suffer from the problem of gate undertraining, which can be caused by various factors, such as the saturating activation functions, the gate layouts (e.g., the gate number and gating functions), or even the suboptimal memory state etc.. Those may result in failures of learning gating switch roles and thus the weak performance. In this paper, we propose a new gating mechanism within general gated recurrent neural networks to handle this issue. Specifically, the proposed gates directly short connect the extracted input features to the outputs of vanilla gates, denoted as refined gates. The refining mechanism allows enhancing gradient back-propagation as well as extending the gating activation scope, which can guide RNN to reach possibly deeper minima. We verify the proposed gating mechanism on three popular types of gated RNNs including LSTM, GRU and MGU. Extensive experiments on 3 synthetic tasks, 3 language modeling tasks and 5 scene text recognition benchmarks demonstrate the effectiveness of our method.

cs.CV↗