A deep transfer learning (DTL) method is proposed for the direction of arrival (DOA) estimation using a single-vector sensor. The method involves training of a convolutional neural network (CNN) with synthetic data in source domain and then adapting the source domain to target domain with available at-sea data. The CNN is fed with the cross-spectrum of acoustical pressure and particle velocity during the training process to learn DOAs of a moving surface ship. For domain adaptation, first convolutional layers of the pre-trained CNN are copied to a target CNN, and the remaining layers of the target CNN are randomly initialized and trained on at-sea data. Numerical tests and real data results suggest that the DTL yields more reliable DOA estimates than a conventional CNN, especially with interfering sources.
A deep transfer learning for underwater source ranging is proposed, which migrates the predictive ability obtained from synthetic environment (source domain) into an experimental sea area (target domain). A deep neural network is first trained on large synthetic datasets generated from historical environmental data, and then part of the neural network is refined on collected data set for source ranging. Its performance is tested on a deep-sea experiment through comparing with convolutional neural networks of different training datasets. Data processing results demonstrate that the ranging accuracy is considerably improved by the proposed method, which can be easily adapted for related areas.
Abstract-MOS Current-Mode Logic (MCML) is usually used for high-speed applications. However, the large static power dissipation of MCML circuit limits its application in portable devices. In this work, we proposed a power-gating (PG) technique to reduce the standby power of the near-threshold MCML. The PG 1-bit full adder and a mod-10 counter are designed and simulated using HSPICE at 45nm CMOS technology with predictive technology model (PTM) model. The simulation results show that the standby power of the PGadder and PG-counter is only 1.0nW and 3.0 nW, respectively. And the performance of the PG MCML circuits does not deteriorate.
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