arXiv subjects

Zhanhao Liu

Publications and source records attributed to Zhanhao Liu.

4 recordsLinked to original sources

arXiv2026

Ultralow shot noise limited giant passive resonant gyroscope for Earth rotation measurement

Optical gyroscopes directly measure the Earth's rotation and are promising instruments for real-time geophysical observations and Earth orientation parameter (EOP) determination requiring both high precision and high temporal resolution. Large-scale ring laser gyroscopes (RLGs) currently reach rotational resolutions around $10^{-11}\,\mathrm{(rad/s)/\sqrt{Hz}}$, but their quantum noise limits make it challenging to meet the requirements of future high-temporal-resolution EOP measurements. Passive resonant gyroscopes (PRGs), on the other hand, offer a potentially lower photon shot noise limit and more flexible power scaling, even if their demonstrated rotational resolutions are still about two orders of magnitude below those of leading RLGs. Here we demonstrate a $64\,\mathrm{m^{2}}$ giant passive resonant gyroscope HUST-2, and develop with an extremely low shot noise level. We experimentally obtain a shot noise limited of $5.7(1)\times10^{-13}\,\mathrm{(rad/s)/\sqrt{Hz}}$ at $1\,\mathrm{mW}$ incident optical power, following the characteristic $1/\sqrt{P}$ scaling. Through systematic suppression of dominant technical noise sources, HUST-2 further achieves a measured rotational resolution of $3\times10^{-11}\,\mathrm{(rad/s)/\sqrt{Hz}}$, bringing PRGs into the performance regime of leading large-scale RLGs for the first time. The gap between the present demonstrated rotational resolution and the shot noise limit indicates nearly two orders of magnitude further improvement potential. Reaching this limit would enable high-precision length-of-day (LOD) measurements with $10$-$100\,\mathrm{s}$ temporal resolution and lays the foundation for future large-scale gyroscope networks dedicated to real-time EOP determination.

physics.optics↗

arXiv2026

Beyond Accuracy: Evaluating Posterior Fidelity of Diffusion Inverse Solvers

Uncertainty evaluation is critical in scientific and engineering inverse problems. However, existing benchmarks on Diffusion Inverse Solvers (DIS) primarily focus on reconstruction accuracy but overlook uncertainty and distributional behavior. Since stochastic inverse solvers represent uncertainty through diffusion-based posterior samples, evaluating how well their generated samples capture the target posterior distribution becomes an important aspect of uncertainty quantification. To address this limitation and better understand the distributional behavior of diffusion samplers, we conduct a systematic study to investigate the posterior fidelity of a broad range of existing DIS methods in controlled simulation settings with a known analytical true posterior. Furthermore, to enable posterior-aware evaluation on real-world inverse problems where ground-truth posterior is unavailable, we propose score-based Kernel Stein Discrepancy (score-KSD), a theoretically-grounded and ground-truth-free metric that measures the consistency of the distribution of generated samples from a DIS method with the target posterior score field, induced by the forward model and learned diffusion prior. Through both simulation experiments and real-world inverse problem solving, we validate the effectiveness of the proposed score-KSD and demonstrate that it provides meaningful posterior fidelity diagnostics beyond reconstruction accuracy, revealing that higher reconstruction accuracy does not necessarily imply better posterior consistency.

cs.LG↗

arXiv2021

Windowed total variation denoising and noise variance monitoring

We proposed a real time Total-Variation denosing method with an automatic choice of hyper-parameter $λ$, and the good performance of this method provides a large application field. In this article, we adapt the developed method to the non stationary signal in using the sliding window, and propose a noise variance monitoring method. The simulated results show that our proposition follows well the variation of noise variance.

eess.SP↗

arXiv2020

Revisit 1D Total Variation restoration problem with new real-time algorithms for signal and hyper-parameter estimations

1D Total Variation (TV) denoising, considering the data fidelity and the Total Variation (TV) regularization, proposes a good restored signal preserving shape edges. The main issue is how to choose the weight $λ$ balancing those two terms. In practice, this parameter is selected by assessing a list of candidates (e.g. cross validation), which is inappropriate for the real time application. In this work, we revisit 1D Total Variation restoration algorithm proposed by Tibshirani and Taylor. A heuristic method is integrated for estimating a good choice of $λ$ based on the extremums number of restored signal. We propose an offline version of restoration algorithm in O(n log n) as well as its online implementation in O(n). Combining the rapid algorithm and the automatic choice of $λ$, we propose a real-time automatic denoising algorithm, providing a large application fields. The simulations show that our proposition of $λ$ has a similar performance as the states of the art.

eess.SP↗