Results 201 to 210 of about 139,484 (301)

SiOx‐Based Probabilistic Bits Enabling Invertible Logic Gate for Cryptographic Applications

Advanced Intelligent Systems, EarlyView.
To enable lightweight hardware encryption and decryption, a Ti/SiOx/Ti threshold switching device is engineered to generate controllable stochastic oscillations. By tuning the input voltage, the device produces a programmable spike probability governed by intrinsic switching dynamics, enabling probabilistic bits that construct an invertible ...
Jihyun Kim, Hyeonsik Choi, Jiyong Woo
wiley   +1 more source

An Approximate Bayesian Approach to Optimal Input Signal Design for System Identification. [PDF]

Entropy (Basel)
Bania P, Wójcik A.
europepmc   +1 more source

Generalized Task‐Driven Design of Soft Robots via Reduced‐Order Finite Element Method‐Based Surrogate Modeling

Advanced Intelligent Systems, EarlyView.
A unified, reusable modeling pipeline enables task‐driven design of soft robots across actuator families and task scenarios. High‐fidelity simulations are compressed into compact pseudo‐rigid‐body joint surrogates, while a design‐conditioned meta‐model generates new surrogates from geometry parameters without rerunning finite element method.
Yao Yao, David Howard, Perla Maiolino
wiley   +1 more source

NeuroQ: Quantum-Inspired Brain Emulation. [PDF]

Biomimetics (Basel)
Vallverdú J, Rius G.
europepmc   +1 more source

Robinson-Schensted algorithms and quantum stochastic double product integrals [PDF]

This thesis is divided into two parts.\ud \ud In the first part (Chapters 1, 2, 3) various Robinson-Schensted (RS) algorithms are discussed. An introduction to the classical RS algorithm is presented, including the symmetry property, and the result of the algorithm Doob h-transforming the kernel from the Pieri rule of Schur functions h when taking a ...
openaire  

Driver Behavior Modeling with Subjective Risk‐Driven Inverse Reinforcement Learning

Advanced Intelligent Systems, EarlyView.
A subjective risk‐driven inverse reinforcement learning framework is proposed to model driver decision‐making. It infers drivers' risk perception and risk tolerance from driving data. A learnable risk threshold is used to regulate decisions, enabling interpretable and human‐like driving behavior decisions.
Yang Liang, Zichao Wang, Zhiwei He, Qingquan Wei, Yong Zuo, Linzhen Nie, Zhishuai Yin   +6 more
wiley   +1 more source

Twist Fields in Many-Body Physics. [PDF]

Entropy (Basel)
Doyon B.
europepmc   +1 more source

Multi-Objective Grey Wolf Optimizer-Tuned LQR Attitude Control of a Three-DOF Hover System. [PDF]

Biomimetics (Basel)
Çakan A.
europepmc   +1 more source

Dynamic Balance: A Thermodynamic Principle for the Emergence of the Golden Ratio in Open Non-Equilibrium Steady States. [PDF]

Entropy (Basel)
Ruiz A.
europepmc   +1 more source

Advancing Machine Learning Optimization of Chiral Photonic Metasurface: Comparative Study of Neural Network and Genetic Algorithm Approaches

Advanced Physics Research, EarlyView.
Two methods for the optimization of chiral reflection by a metamaterial made of either GaP/Air or PMMA/Air interfaces are compared, showing approaches towards fast design exploration and high‐performance results: a neural‐network pipeline and a genetic algorithm. The structures considered are characterized by a periodic, chiral texturation with a shape
Davide Filippozzi, Alexandre Mayer, Nicolas Roy, Wei Fang, Arash Rahimi‐Iman   +4 more
wiley   +1 more source