Second order backward stochastic differential equations with quadratic growth [PDF]
Dylan Possamaï, Chao Zhou
openalex +1 more source
Infinite dimensional forward-backward stochastic differential equations and the KPZ equation
Amarjit Budhiraja +1 more
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High‐Speed Altitude Regulation With Neuromorphic Camera and Lightweight Embedded Computation
Neuromorphic cameras deliver rapid, high‐dynamic‐range sensing but overwhelm embedded processors at high speeds. This work presents a lightweight, optimized Lucas–Kanade optical flow method with parallelization, gyroscopic derotation, and adaptive event slicing.
Simon L. Jeger +3 more
wiley +1 more source
Reliable numerical scheme for coupled nonlinear Schrödinger equation under the influence of the multiplicative time noise. [PDF]
Baber MZ +6 more
europepmc +1 more source
A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad +4 more
wiley +1 more source
The FitzHugh-Nagumo equations and quantum noise. [PDF]
Ghose P, Pinotsis DA.
europepmc +1 more source
Numerical Solutions of Backward Stochastic Differential Equations: A Finite Transposition Method [PDF]
Penghui Wang, Xu Zhang
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Modular Electronic Microrobots With Onboard Sensor‐Program‐Steered Locomotion
Modular electronic smartlet microrobots integrate ambient‐light energy harvesting, photodetection, programmable CMOS control, and bubble‐based actuation within a sub‐millimeter fold‐up architecture. A 58‐bit on‐board CMOS chiplet enables sensor–program steered switching between independently addressable actuators, achieving closed‐loop 2D navigation in
Vineeth K. Bandari +6 more
wiley +1 more source
A Bertrand model with Brownian motion and behavioral errors. [PDF]
Gao B, Gao X, He S.
europepmc +1 more source
Deep Reinforcement Learning Approaches for Sensor Data Collection by a Swarm of UAVs
This article presents four decentralized reinforcement learning algorithms for autonomous data harvesting and investigates how collaboration improves collection efficiency. It also presents strategies to minimize training times by improving model flexibility, enabling algorithms to operate with varying number of agents and sensors.
Thiago de Souza Lamenza +2 more
wiley +1 more source

