Results 201 to 210 of about 254,560 (354)
An Impedimetric Sensing Probe Based on Printed Circuit Board Technology for Monitoring in Cryobiology Applications. [PDF]
Shamkhalichenar H, Tiersch TR, Choi JW.
europepmc +1 more source
Physical Intelligence in Small‐Scale Robots and Machines
“Physical intelligence” (PI) empowers biological organisms and artificial machines, especially at the small scales, to perceive, adapt, and even reshape their complex, dynamic, and unstructured operation environments. This review summarizes recent milestones and future directions of PI in small‐scale robots and machines.
Huyue Chen, Metin Sitti
wiley +1 more source
GESC-YOLO: Improved Lightweight Printed Circuit Board Defect Detection Based Algorithm. [PDF]
Kong X, Liu G, Gao Y.
europepmc +1 more source
Biocompatibility Study of a Commercial Printed Circuit Board for Biomedical Applications: Lab-on-PCB for Organotypic Retina Cultures. [PDF]
Urbano-Gámez JD +8 more
europepmc +1 more source
Screen gate‐based transistors are presented, enabling tunable analog sigmoid and Gaussian activations. The SA‐transistor improves MRI classification accuracy, while the GA‐transistor supports precise Gaussian kernel tuning for forecasting. Both functions are implemented in a single device, offering compact, energy‐efficient analog AI processing ...
Junhyung Cho +9 more
wiley +1 more source
Nanoscale monitoring of the initial stage of water condensation on a printed circuit board. [PDF]
Romanenko A +3 more
europepmc +1 more source
Hydrogels demonstrate material properties that mimic the mechanical and chemical environments of biological tissues. Yet, they face challenges during their integration into 3D interfaces. By identifying a class of thermoplastic hydrogels, a strategy is developed to pattern hydrogels in thermally drawn fibers.
Changhoon Sung +13 more
wiley +1 more source
Printed circuit board substrates derived from lignocellulose nanofibrils for sustainable electronics applications. [PDF]
Dudnyk Y +4 more
europepmc +1 more source
Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning. [PDF]
Gang S, Fabrice N, Chung D, Lee J.
europepmc +1 more source

