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Convolutional neural networks (CNNs) excel in various computer vision related tasks but are extremely computationally intensive and power hungry to run on mobile and embedded devices. Recent pruning techniques can reduce the computation and memory requirements of CNNs, but a costly retraining step is needed to restore the classification accuracy of the pruned model. In this paper, we present evidence...
Autonomous underwater vehicles (AUVs) work in complex marine environments, and sensors play an important role in AUV systems. Therefore, research on sensor failure diagnosis technology is important for improving the reliability of AUV systems. In this paper, a new method combining phase space reconstruction and extreme learning machine (ELM) is proposed. This method is applied to predict sensor output...
Future Internet of Things (IoT) systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for security enhancement, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the...
Filter bank-based methods for pixel classification are attractive due to the potential of fast implementation with convolution operations. The design of optimal filter sets, however, is a challenging task given the nonlinear aspects of the problem. This letter extends the well known linear discriminant analysis method into a novel framework for local texture feature discrimination tasks. It proposes...
Flow turbulence intensity is one of the most important indexes in measuring the shear performance of vertical impinging stream reactor (VCISR). The staggered forms of the upper and lower blades and the distances among draft-tube are the significant factors affecting the turbulence intensity in the reactor. In order to explore the law that the structure parameters impact the internal flow field, a...
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