Results 241 to 250 of about 7,858,197 (288)

Exploring the Potential of Microwave Annealing for Enhancing Si‐based GeSn Lasers

Advanced Materials Technologies, EarlyView.
We explore low‐thermal‐budget microwave annealing to enhance the performance of group‐IV GeSn lasers on Si. Microwave annealing under optimal conditions can simultaneously relax unwanted compressive strain and enhance the material quality of the GeSn active layer, thereby reducing the threshold and increasing the laser operating temperature.
Yue‐Tong Jheng, Cheng‐Feng Liu, Ruei‐Jhang Wang, Yu Cheng, Lung‐Yi Hsu, Yonder Berencén, Shengqiang Zhou, Hanh Thi‐Thu Tran, Guo‐En Chang   +8 more
wiley   +1 more source

Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization

Advanced Materials Technologies, EarlyView.
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue, Yuan Gao, Fanping Sui, Jason Wang, Megan Teng, Peggy Tsao, Huicong Deng, Yande Peng, Liwei Lin   +8 more
wiley   +1 more source

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Deep Learning Seismic Random Noise Attenuation via Improved Residual Convolutional Neural Network

IEEE Transactions on Geoscience and Remote Sensing, 2021
Because a high signal-to-noise ratio (SNR) is beneficial to the subsequent processing procedures, the noise attenuation is important. We propose an adaptive random noise attenuation framework based on convolutional neural networks (CNNs).
Liu Yang, Wei Chen, Hang Wang, Yangkang Zhang   +3 more
semanticscholar   +3 more sources

Random noise

Electronics and Power, 1973
  +19 more sources

Random noise

Electronics and Power, 1974
  +18 more sources

Mathematical analysis of random noise

Bell System Technical Journal, 1944
S. Rice
semanticscholar   +3 more sources

A fully unsupervised and highly generalized deep learning approach for random noise suppression

Geophysical Prospecting, 2021
In this study, we proposed a deep learning algorithm (PATCHUNET) to suppress random noise and preserve the coherent seismic signal. The input data are divided into several patches, and each patch is encoded to extract the meaningful features.
O. Saad, Yangkang Zhang
semanticscholar   +1 more source

Seismic Exploration Random Noise on Land: Modeling and Application to Noise Suppression

IEEE Transactions on Geoscience and Remote Sensing, 2017
Guanghui Li, Yue Li, Baojun Yang
semanticscholar   +3 more sources

An Unsupervised Deep Learning Method for Denoising Prestack Random Noise

IEEE Geoscience and Remote Sensing Letters, 2022
Deep-learning-based methods have been successfully applied to seismic data random noise attenuation. Among them, the supervised deep-learning-based methods dominate the unsupervised ones.
Dawei Liu, Zheyuan Deng, Cheng Wang, Xiaokai Wang, Wenchao Chen   +4 more
semanticscholar   +1 more source

Seismic Random Noise Attenuation Using Synchrosqueezed Wavelet Transform and Low-Rank Signal Matrix Approximation

IEEE Transactions on Geoscience and Remote Sensing, 2017
R. Anvari, Mohammad Amir Nazari Siahsar, Saman Gholtashi, Amin Roshandel Kahoo, M. Mohammadi   +4 more
semanticscholar   +3 more sources