arXiv subjects

Yijing Li

Publications and source records attributed to Yijing Li.

8 recordsLinked to original sources

arXiv2026

Mid-infrared single-photon computational temporal ghost imaging

The capture of transient optical waveforms is critical to reveal dynamical phenomena in various fields. However, fast and sensitive mid-infrared (MIR) measurements are typically limited by processing bandwidth and detection sensitivity of conventional infrared detectors. Here, we propose and implement a computational temporal ghost imaging system, which favors high-speed and high-sensitivity characterization of MIR temporal objects. The core process relies on high-fidelity nonlinear optical transduction for facilitating both the programmable structured illumination and frequency upconversion detection based on the high-performance near-infrared light modulator and detector, respectively. Consequently, the correlation between the recorded integral upconversion intensity and the designated encoding patterns allows one to reconstruct the MIR profiles with a temporal resolution of 80 ps, well beyond the intrinsic bandwidth or timing jitter of the involved detectors. Moreover, a record-high detection sensitivity is manifested by recovering single-photon MIR waveforms with an incident flux below 0.1 photon/bit. Additionally, faithful reconstructions at sub-Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which can reduce the data acquisition time by over 90\%. The presented paradigm features high timing precision, single-photon sensitivity, and efficient data sampling, which could be extended into far-infrared or terahertz regions to address pressing demands in fast and sensitive sensing.

physics.optics↗

arXiv2026

Wide-field mid-infrared cavity-enhanced upconversion imaging

Mid-infrared (MIR) spectral imaging enables precise target identification and analysis by capturing rich chemical fingerprints, which calls for high-sensitivity broadband MIR imagers at room temperature. Here, we devise and implement a continuous-wave pumping MIR upconversion imaging system based on external-cavity enhancement, which favors a large field of view, a low cavity loss, and a high spectral resolution. The involved optical cavity is constructed in an integrated fashion by utilizing one crystal facet as a cavity mirror, which allows a 43-fold power enhancement for the single-longitudinal-mode pump at 1064 nm. In combination with the chirped-poled crystal design, high-fidelity and wide-field spectral imaging mapping is permitted to facilitate an acceptance angle up to 28.5$^\circ$ over a spectral coverage of 2.5--5 $\mu$m. Moreover, a thermal locking approach is used to stabilize the cavity at the high-power operation, eliminating active feedback and ensuring long-term stability. A proof-of-principle demonstration is presented to showcase real-time observation of CO$_{2}$ gas injection dynamics. The implemented MIR upconversion imager features wide-field operation, high detection sensitivity, and compact footprint, which would benefit subsequent applications including environment monitoring, gas leakage inspection, and medical diagnostics.

physics.optics↗

arXiv2025

London Blue Light Collaboration Evaluation: A Comparative Analysis of Spatio temporal Patterns on Emergency Services by London Ambulance Service and London Fire Brigade

With rising demand for emergency services, the London Ambulance Service, LAS, and the London Fire Brigade, LFB, face growing challenges in resource coordination. This study investigates the temporal and spatial similarities in their service demands to assess potential for routine cross-agency collaboration. Time series analysis revealed aligned demand peaks in summer, on Fridays, during daytime hours, and were highly sensitive to high temperature weather conditions. Bivariate mapping and Moran I indicated significant spatial overlaps in central London and Hillingdon. Geographically Weighted Regression, GWR, examined the influence of socioeconomic factors, while Comap analysis uncovered spatiotemporal heterogeneity across fire service types. The findings highlight opportunities for targeted collaboration in high-overlap areas and peak periods, offering practical insights to enhance emergency service resilience and efficiency.

cs.CY↗

arXiv2025

A scalable Bayesian double machine learning framework, with application to racial disproportionality assessment

Racial disproportionality in stop and search practices elicits substantial concerns about its societal and behavioral impacts. In London, Black individuals are about four times more likely to be stopped and searched than White individuals. Using data on stop and search events in London from January 2019 to December 2023, this paper aims to investigate disproportionality in the volume of stops for expressive crimes involving Black individuals compared to other ethnicities. We employ a semi-parametric partially linear structural regression method and introduce a Bayesian empirical likelihood procedure combined with double machine learning techniques to control for high-dimensional confounding and to accommodate strong prior assumptions. In addition, we show that the proposed procedure yields a valid posterior in terms of coverage. Applying this approach to the stop and search dataset, we find that racial disproportionality aimed at the Black community may be influenced by the borough racial composition when focusing on expressive crimes.

stat.AP↗

arXiv2022

Safer Traffic Recovery from the Pandemic in London -- Spatiotemporal Data Mining of Car Crashes

In the aim to support London's safer recovery from the pandemic by improving road safety intelligently, this study investigated the spatiotemporal patterns of age-involved car crashes and affecting factors, upon answering two main research questions: (1)"What are the spatial and temporal patterns of car crashes as well as their changes in two typical years, 2019 and 2020, in London, and how the influential factors work?"; (2)"What are the spatiotemporal patterns of casualty by age groups, and how people's daily activities affect the patterns pre- and para- the pandemic"? Three approaches, i.e., spatial analysis (network Kernel Density Estimation, NetKDE), factor analysis, and spatiotemporal data mining (tensor decomposition), had been implemented to identify the temporal patterns of car crashes on weekly and daily basis respectively, detect the crashes' hot spots, and to gain better understanding the effect from citizens' daily activity on crashes' patterns pre- and para- the pandemic. It had been found from the study that car crashes mainly clustered in the central part of London, especially busier areas around denser hubs of point-of-interest (POIs); the POIs, as a reflector for citizens' daily activities and travel behaviours, can be of help to gain a better understanding of the crashes' patterns, upon further assessment on interactions through the geographical detector; the crashes' casualty patterns varied by age group, with distinctive relationships between POIs and crashes' pattern for corresponding age group categorised. In all, the paper provided an in-depth exploratory analysis of car crashes and their casualty patterns in London to facilitate deployment policies towards post-pandemic safer recovery upon COVID-19.

cs.SI↗

arXiv2020

UNOC: Understanding Occlusion for Embodied Presence in Virtual Reality

Tracking body and hand motions in the 3D space is essential for social and self-presence in augmented and virtual environments. Unlike the popular 3D pose estimation setting, the problem is often formulated as inside-out tracking based on embodied perception (e.g., egocentric cameras, handheld sensors). In this paper, we propose a new data-driven framework for inside-out body tracking, targeting challenges of omnipresent occlusions in optimization-based methods (e.g., inverse kinematics solvers). We first collect a large-scale motion capture dataset with both body and finger motions using optical markers and inertial sensors. This dataset focuses on social scenarios and captures ground truth poses under self-occlusions and body-hand interactions. We then simulate the occlusion patterns in head-mounted camera views on the captured ground truth using a ray casting algorithm and learn a deep neural network to infer the occluded body parts. In the experiments, we show that our method is able to generate high-fidelity embodied poses by applying the proposed method on the task of real-time inside-out body tracking, finger motion synthesis, and 3-point inverse kinematics.

cs.CV↗

arXiv2014

Binary Stereo Matching

In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem. The cost volume is constructed through bitwise operations on a series of binary strings. Then this approach is combined with traditional winner-take-all strategy, resulting in a new local stereo matching algorithm called binary stereo matching (BSM). Since core algorithm of BSM is based on binary and integer computations, it has a higher computational efficiency than previous methods. Experimental results on Middlebury benchmark show that BSM has comparable performance with state-of-the-art local stereo methods in terms of both quality and speed. Furthermore, experiments on images with radiometric differences demonstrate that BSM is more robust than previous methods under these changes, which is common under real illumination.

cs.CV↗

arXiv2012

Solving Hybrid Influence Diagrams with Deterministic Variables

We describe a framework and an algorithm for solving hybrid influence diagrams with discrete, continuous, and deterministic chance variables, and discrete and continuous decision variables. A continuous chance variable in an influence diagram is said to be deterministic if its conditional distributions have zero variances. The solution algorithm is an extension of Shenoy's fusion algorithm for discrete influence diagrams. We describe an extended Shenoy-Shafer architecture for propagation of discrete, continuous, and utility potentials in hybrid influence diagrams that include deterministic chance variables. The algorithm and framework are illustrated by solving two small examples.

cs.AI↗