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Convolutional neural networks(CNNs) have been widely applied in various applications. However, the computation-intensive convolutional layers and memory-intensive fully connected layers have brought many challenges to the implementation of CNN on embedded platforms. To overcome this problem, this work proposes a power-efficient accelerator for CNNs, and different methods are applied to optimize the...
Today, artificial neural networks (ANNs) are widely used in a variety of applications, including speech recognition, face detection, disease diagnosis, etc. And as the emerging field of ANNs, Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) which contains complex computational logic. To achieve high accuracy, researchers always build large-scale LSTM networks which are time-consuming...
The brain-actuated vehicle model is of great value to develop a brain-actuated assistive driving. Furthermore, this study helps to develop other BCI systems such as mobile robots, wheelchairs and unmanned aerial vehicle. In this paper, we propose a control model of motor-imagery based brain-actuated vehicle. This model includes a direction control and speed control combined driver model, a motor imagery...
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