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1.
Opt Express ; 29(22): 34913-34925, 2021 Oct 25.
Artículo en Inglés | MEDLINE | ID: mdl-34808940

RESUMEN

We present γ-ray radiation detection in a neutron radiation environment using a monolithic active pixel sensor (MAPS) camera without conversion or shielding layers. The measured output signal is the sum of the pedestal value, noise, and real radiation response signal. The sensor response shows that the MAPS camera is sensitive to neutrons and can capture a single photon. The number of pixels with a signal exceeding 100 exhibits a strong dependence on the dose rate and is the best indicator of this value. Therefore, a MAPS camera can be efficiently used as a radiation detection sensor in a robotic system, further limiting human errors in performing radiation detection in complex nuclear radiation environments.

2.
Sci Rep ; 14(1): 15185, 2024 Jul 02.
Artículo en Inglés | MEDLINE | ID: mdl-38956263

RESUMEN

As the node positioning of underwater wireless sensor networks is easily affected by tidal motion, ocean current motion and multipath effect, the node positioning accuracy is low. In order to better improve the positioning accuracy of moving nodes of underwater wireless sensor networks, a method of locating mobile nodes of underwater wireless sensor based on tidal motion model is proposed. Firstly, the Time Difference of Arrival (TDOA) localization optimized by niche genetic algorithm is used to initialize each node. The integration of niche technology can effectively find multiple excellent solutions in the solution space, thus providing more abundant solution choices. This algorithm has excellent performance in multi-modal optimization problems, and can avoid the algorithm falling into local optimal solutions, so as to obtain more comprehensive optimization results. The simulation results show that the proposed algorithm has better positioning accuracy than the traditional Chan algorithm and Taylor algorithm. Then, each node is updated in real time by the optimized tidal movement model formula predicted by Kalman filter algorithm. The prediction algorithm is used to compare the real-time predicted update position of the node with the actual position. The positioning distance error of the prediction algorithm is also enough to meet the practical application requirements.

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