Results 121 to 130 of about 3,038,089 (290)
Recent advances in nanophotonics‐based chiral biosensing approaches are comprehensively reviewed, highlighting key trends, advantages, and limitations of each technology. Special attention is given to emerging strategies that exploit magneto‐optical and quantum plasmonic phenomena to enhance sensitivity down to the level of a few molecules, or even a ...
Jorge Ricardo Mejía‐Salazar
wiley +1 more source
Flexible Sensor‐Based Human–Machine Interfaces with AI Integration for Medical Robotics
This review explores how flexible sensing technology and artificial intelligence (AI) significantly enhance human–machine interfaces in medical robotics. It highlights key sensing mechanisms, AI‐driven advancements, and applications in prosthetics, exoskeletons, and surgical robotics.
Yuxiao Wang +5 more
wiley +1 more source
Sparse Coding-Enabled Low-Fluence Multi-Parametric Photoacoustic Microscopy. [PDF]
Wang Z, Zhou Y, Hu S.
europepmc +1 more source
Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source
Inference via sparse coding in a hierarchical vision model. [PDF]
Bowren J, Sanchez-Giraldo L, Schwartz O.
europepmc +1 more source
CD207+ dendritic cells (DCs) drive emphysema by promoting CD8⁺ T cell cytotoxicity via Birbeck granule‐dependent MHC‐I antigen presentation. This DC subset is expanded by cigarette smoke‐induced oxidative stress, which triggers granulocyte‐macrophage colony‐stimulating factor (GM‐CSF) release from airway epithelium.
Shurui Xuan +10 more
wiley +1 more source
Remote sensing image super-resolution using multi-scale convolutional sparse coding network. [PDF]
Cheng R, Wang H, Luo P.
europepmc +1 more source
Non-negative sparse coding is a method for decomposing multivariate data into non-negative sparse components. In this paper we briefly describe the motivation behind this type of data representation and its relation to standard sparse coding and non ...
Hoyer, Patrik O.
core +2 more sources
Multi‐Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects
The study proposes graph convolution network with sparse pooling to learn the hierarchical features of brain graph for MDD classification. Experiment is done on multi‐site fMRI samples (3335 subjects, the largest functional dataset of MDD to date) and transfer learning is applied, achieving an average accuracy of 70.14%.
Jianpo Su +14 more
wiley +1 more source
Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding. [PDF]
Wu J +10 more
europepmc +1 more source

