Quantization techniques have been studied to reduce the computing and memory requirement of deep neural networks. The full precision floating-point numbers are quantized into …
Compared with the state-of-the-art architectures, using the 3D point cloud as the input of the 2D convolutional neural network without preprocessing will restrict the feature …
Recently, studies on single image super-resolution using Deep Convolutional Neural Networks (DCNN) have been demonstrated to have made outstanding progress over conventional …
Convolutional neural networks (CNNs) have been widely applied in super-resolution (SR) and other image restoration tasks. Recently, Hinton et al. proposed capsule neural networks …
Two-way full-duplex (TWFD) transmissions in cooperative amplify-and-forward (AF) relaying systems provide a promising way of communications with the improved spectral efficiency by …
Rate control plays an important role in video coding and streaming applications with bandwidth constraints. While most researches are proposed to improve the coding efficiency' the …
Convolutional Neural Networks have been widely studied for the super-resolution (SR) and other image restoration tasks. In this paper, we propose an additional error-compensational …
In this paper, we introduce an indoor location estimation method based on the Bluetooth low energy (BLE) beacon. Beacons are advertised at low transmission power level to limit the …
The paper proposes a novel approach to achieve monitoring and control of modern substations integrated with renewable-energy resources using advanced computer communication and …
Most texture compression schemes are not able to accurately represent the object edges, since the number of color candidates usually are not sufficient to reconstruct a smooth …