Results 201 to 210 of about 30,980 (307)
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
A Novel Energy Replenishment Algorithm to Increase the Network Performance of Rechargeable Wireless Sensor Networks. [PDF]
Tariq+4 more
europepmc +1 more source
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz+4 more
wiley +1 more source
A network recovery strategy based on boundary nodes and tetrahedral approximation fermat points in three-dimensional wireless sensor networks. [PDF]
Xu B, Chen H, Cheng Y.
europepmc +1 more source
It is a fact that slippage causes tracking errors in both longitudinal and lateral directions which results to have less travel distance in tracking a reference trajectory. Less travel distance means having energy loss of the battery and carrying loads less than planned.
Gokhan Bayar+2 more
wiley +1 more source
VANET addressing scheme incorporating geographical information in standard IPv6 header [PDF]
Bergs, J+6 more
core +1 more source
Automated design of scaffold-free DNA wireframe nanostructures. [PDF]
Wang W+9 more
europepmc +1 more source
Multi‐UAV systems face challenges in adversarial environments due to limited adaptability and interpretability. This study proposes a self‐organized approach using hierarchical probabilistic graphical models with density‐driven parameter estimation.
Yixin Huang+5 more
wiley +1 more source
RC-LAHR: Road-Side-Unit-Assisted Cloud-Based Location-Aware Hybrid Routing for Software-Defined Vehicular Ad Hoc Networks. [PDF]
Kumar M, Raw RS.
europepmc +1 more source