In Situ Real-Time Measurement for Electron Spin Polarization in Atomic Spin Gyroscopes

iScience, Journal Year: 2025, Volume and Issue: 28(2), P. 111757 - 111757

Published: Jan. 7, 2025

Atomic spin gyroscopes (ASGs) based on spin-exchange relaxation-free (SERF) co-magnetometers represent a new generation of ultra-high-precision inertial sensors. However, their long-term stability is significantly constrained by the electron polarization. Despite its critical importance, current research lacks effective methods for in situ and real-time measurement This paper addresses this gap developing model pump laser propagation within vapor cell proposing an Euler-particle swarm optimization (PSO) algorithm to estimate model's unknown parameters. By utilizing artificial neural networks, we derive output equation polarization, using transmitted power temperature as independent variables. Comparative experiments validate accuracy proposed method, perturbation demonstrate capability. The method polarization lays solid foundation improving closed-loop control enhancing ASGs.

Language: Английский

Mechanical Neural Networks with Explicit and Robust Neurons

Advanced Science, Journal Year: 2024, Volume and Issue: 11(33)

Published: June 19, 2024

Mechanical computing provides an information processing method to realize sensing-analyzing-actuation integrated mechanical intelligence and, when combined with neural networks, can be more efficient for data-rich cognitive tasks. The requirement of solving implicit and usually nonlinear equilibrium equations motion in training networks makes computation challenging costly. Here, explicit neuron is developed which the response directly determined without need equations. A proposed ensure robustness neuron, i.e., insensitivity defects perturbations. explicitness neurons facilitate assembly various network structures. Two exemplified a robust convolutional recurrent long short-term memory capabilities associative learning, are experimentally demonstrated. introduction streamlines design fulfilling robotic matter level intelligence.

Language: Английский

Advances in metamaterials for mechanical computing

Published: April 1, 2025

Mechanical metamaterials are revolutionizing computation by offering a robust and energy-efficient alternative to traditional electronic systems. The field has seen remarkable progress; the structural design functionality of mechanical have advanced significantly, evolving from simple load-bearing enhancements encompass logic information storage through interconnected networks binary ternary units. This progress necessitates comprehensive review clarify complexities computing for broader audience. Review systematically explores evolution computing, ancient mechanisms modern counterparts, highlighting how uniquely address limitations in power consumption, scalability, reliability, especially extreme environments. We analyze fundamental principles metamaterial-based gates units, detailing their underlying mechanisms, strategies, diverse applications. Furthermore, we discuss integration these materials into existing machinery, emphasizing potential programmable enhance create self-powered systems robotics other concludes proposing strategic directions future research innovation this rapidly field.

Language: Английский