Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Fuzzy adaptive nonlinear MIMO control for rigid coupled multibody robots using reinforcement learning model. [PDF]
Duan C, Wang L, Li S.
europepmc +1 more source
Data‐Driven High‐Throughput Volume Fraction Estimation From X‐Ray Diffraction Patterns
Long exposure times and the need for manual evaluation limit the use of X‐ray diffraction in high‐throughput applications. This study presents a data‐driven approach addressing both issues. HiVE (a method for High‐throughput Volume fraction Estimation) performs composition estimation for high‐noise XRD patterns produced using polychromatic emission ...
Hawo H. Höfer +6 more
wiley +1 more source
Galaxy Evolution with Manifold Learning. [PDF]
Takeuchi TT, Cooray S, Kano RR.
europepmc +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Design and Simulation of Suspension Leveling System for Small Agricultural Machinery in Hilly and Mountainous Areas. [PDF]
Huang P +5 more
europepmc +1 more source
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
A Causality-Informed Correlation-Aware Health-State Assessment for Complex Equipment. [PDF]
Li W, Feng Z, Sun Y, Zhang X.
europepmc +1 more source
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu +11 more
wiley +1 more source
Mitigating Neural Habituation in Insect Bio-Bots: A Dual-Timescale Adaptive Control Approach. [PDF]
Triet LM, Truong Thinh N.
europepmc +1 more source

