Photonic-Assisted Modulation Format Identification for RF Signals under Low S…
A photonic-assisted approach is proposed for modulation format identification (MFI) on radio frequency (RF) signals under low sampling rate. In this approach, a photonic-assisted interferometer (PA…
Ultra-High Sensitive Fiber Optic Hydrogen Sensor in Air
A compact fiber optic hydrogen sensing system employing self-referenced configuration and controllable light heating technologies, is proposed and experimentally demonstrated with ultra-high sensit…
Second-Order Optimality Conditions for Nonconvex Set-Constrained Optimization…
In this paper, we study second-order optimality conditions for nonconvex set-constrained optimization problems. For a convex set-constrained optimization problem, it is well known that second-order…
Distributed Stochastic Optimization with Large Delays
The recent surge of breakthroughs in machine learning and artificial intelligence has sparked renewed interest in large-scale stochastic optimization problems that are universally considered hard. …
Long-Range High-Spatial-Resolution Distributed Measurement by a Wideband Bril…
Stimulated Brillouin scattering (SBS)-based distributed fiber optic sensing technique has found pervasive applications in measurement of real-world environmental variables. Its sensing capability, …
Photonic-Assisted Multipath Self-Interference Cancellation for Wideband MIMO …
To cancel the multipath self-interference at the receiver of a wideband in-band full-duplex (IBFD) multiple-input multiple-output (MIMO) radio-over-fiber (RoF) transmission system, a photonic-assis…
Efficient Lasing Through Raman-Assisted Four-Wave Mixing With Intrinsic Weak …
Narrow linewidth nonlinear frequency conversion has attracted numerous research attentions. In this manuscript, we demonstrate efficient frequency conversion with weak spectral broadening character…
Heterogeneous Few-Shot Model Rectification With Semantic Mapping
There still involve lots of challenges when applying machine learning algorithms in unknown environments, especially those with limited training data. To handle the data insufficiency and make a fu…
Optimizing Regularized Cholesky Score for Order-Based Learning of Bayesian Ne…
Bayesian networks are a class of popular graphical models that encode causal and conditional independence relations among variables by directed acyclic graphs (DAGs). We propose a novel structure l…
Graph-Based Equilibrium Metrics for Dynamic Supply-Demand Systems With Applic…
How to dynamically measure the local-to-global spatio-temporal coherence between demand and supply networks is a fundamental task for ride-sourcing platforms, such as DiDi. Such coherence measureme…
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