In wireless communications, a smart antenna system utilizes an antenna array to acquire the spatial signatures of transmitted signals. This system uses the difference in the spatial signatures or the direction of arrival (DOA) of signals to correctly obtain the desired signal. This can reduce co-channel interference, mitigate the fading phenomenon caused by multipath transmissions, improve the communication quality and increase the system capacity. The purpose of this paper is to evaluate the performance of smart antennas using four beamforming algorithms applied to a wideband code division multiple access (W-CDMA) system. The simulation results show that, based on the same power consumption at the transceiver and using a Rake receiver, a W-CDMA system with a smart antenna can operate at a lower bit error rate at the specific signal to noise ratio (SNR). Moreover, the smart antenna system accommodates more users at the specific signal to interference ratio (SIR), even though a certain angle difference exists between the actual DOAs and the estimated DOAs.

In this work, a simple method for extracting MOSFET threshold voltage, effective channel length and channel mobility by using S-parameter measurement is presented. In the new method, the dependence between the channel conductivity and applied gate voltage of the MOSFET device is cleverly utilized to extract the threshold voltage, while biasing the drain node of the device at zero voltage during measurement. Moreover, the effective channel length and channel mobility can also be obtained with the same measurement. Furthermore, all the physical parameters can be extracted directly on the modeling devices without relying on specifically designed test devices. Most important of all, only one S-parameter measurement is required for each device under test (DUT), making the proposed extraction method promising for automatic measurement applications.

In recent years, the increasing complexity of deep network structures has hindered their application in small resource constrained hardware. Therefore, we urgently need to compress and accelerate deep network models. Channel pruning is an effective method to compress deep neural networks. However, most existing channel pruning methods are prone to falling into local optima. In this paper, we propose a channel pruning method via Improved Grey Wolf Optimizer Pruner which called IGWO-Pruner to prune redundant channels of convolutional neural networks. It identifies pruning ratio of each layer by using Improved Grey Wolf algorithm, and then fine-tuning the new pruned network model. In experimental section, we evaluate the proposed method in CIFAR datasets and ILSVRC-2012 with several classical networks, including VGGNet, GoogLeNet and ResNet-18/34/56/152, and experimental results demonstrate the proposed method is able to prune a large number of redundant channels and parameters with rare performance loss.

A robust method is presented for 3D face landmarking with facial pose and expression variations. This method is based on Multi-level Partition of Unity (MPU) Implicits without relying on texture, pose, orientation and expression information. The MPU Implicits reconstruct 3D face surface in a hierarchical way. From lower to higher reconstruction levels, the local shapes can be reconstructed gradually according to their significance. For 3D faces, three landmarks, nose, left eyehole and right eyehole, can be detected uniquely with the analysis of curvature features at lower levels. Experimental results on GavabDB database show that this method is invariant to pose, holes, noise and expression. The overall performance of 98.59% is achieved under pose and expression variations.