GitHub

[ICCV2025] FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems

️Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general ...
IEEE

Antinoise Bearing Fault Diagnosis Using Time-Reassigned Multisynchrosqueezing Transform and Complex Sparse Learning Dictionary

Abstract: Bearings are core components of rotating machinery, and their operating status monitoring and fault diagnosis are crucial for equipment health management. Accurately identifying fault ...
GitHub

JAPS — Jacobian-Aware Posterior Sampling for Inverse Problems

Diffusion models provide powerful generative priors for solving inverse problems by sampling from a posterior distribution conditioned on corrupted measurements. Existing methods primarily follow two ...
IEEE

Using Clarke Transform to Create a Framework on the Manifold: From Sampling via Trajectory Generation to Control

Abstract: We present a framework based on Clarke coordinates for spatial displacement-actuated continuum robots with an arbitrary number of joints. This framework consists of three modular components, ...