In this letter, we study the performance of multi-antenna relay networks with limited feedback beamforming in decode-and-forward (DF) relaying. Closed-form expression for both outage probability and symbol error rate are derived by using the moment generation function (MGF) of the combined signal-to-noise ratio (SNR) at the destination. Subjected to a total power constraint, we also explore adaptive power allocation between source and relay to optimize the performance. Simulations are given to verify the correctness of our theoretical derivations. Results show that the proposed adaptive power allocation solution significantly outperforms the uniform power allocation method.

Transfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multi-similarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the “negative transfer” and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.

In this paper, we present a new approach which is spanning tree-based genetic algorithm for solving a multi-objective transportation problem. The transportation problem as a special type of the network optimization problems has the special data structure in solution characterized as a transportation graph. In encoding transportation problem, we introduce one of node encodings based on a spanning tree which is adopted as it is capable of equally and uniquely representing all possible basic solutions. The crossover and mutation were designed based on this encoding. Also we designed the criterion that chromosome has always feasibility converted to a transportation tree. In the evolutionary process, the mixed strategy with (µ+λ)-selection and roulette wheel selection is used. Numerical experiments show the effectiveness and efficiency of the proposed algorithm.

In this Letter, we propose a novel method of low-level global motion feature description based on Vector Quantization (VQ) index histograms of motion feature vectors (MFVVQIH) for the purpose of video shot retrieval. The contribution lies in three aspects: first, we use VQ to eliminate singular points in the motion feature vector space; second, we utilize the global motion feature vector index histogram of a video shot as the global motion signature; third, video shot retrieval based on index histograms instead of original motion feature vectors guarantees the low computation complexity, and thus assures a real-time video shot retrieval. Experimental results show that the proposed scheme has high accuracy and low computation complexity.

A cross-coupled charge pump with internal pumping capacitor, which is advantageous from a point of minimizing TFT-LCD driver IC module, is newly proposed in this paper. By using NMOS and PMOS diodes connected to boosting nodes from VIN nodes, the pumping node is precharged to the same value at the pumping node in starting pumping. Since the first-stage charge pump is designed differently from the other stage pumps, a back current of pumped charge from charge pumping node to input stage is prevented. As a pumping clock driver is located in front of pumping capacitor, the driving capacity is improved by reducing a voltage drop of the pumping clock line from parasitic resistor. Finally, a layout area is decreased more compared with the conventional cross-coupled charge pump by using a stack-MIM capacitor. A proposed charge pump for TFT-LCD driver IC is designed with 0.13 µm triple-well DDI process, fabricated, and tested.

An FFT-based full-search block matching algorithm (BMA) is described that uses the sum of squared differences (SSD) criterion. The proposed method does not have to extend a real signal into complex one. This reduces the computational load of FFT approaches. In addition, if two macroblocks share the same search window, they can be matched at the same time. In a simulation of motion estimation, the proposed method achieved the same performance as a direct SSD full search and its processing speed is faster than other FFT-based BMAs.

Hyperspectral images (HSIs) are generally susceptible to various noise, such as Gaussian and stripe noise. Recently, numerous denoising algorithms have been proposed to recover the HSIs. However, those approaches cannot use spectral information efficiently and suffer from the weakness of stripe noise removal. Here, we propose a tensor decomposition method with two different constraints to remove the mixed noise from HSIs. For a HSI cube, we first employ the tensor singular value decomposition (t-SVD) to effectively preserve the low-rank information of HSIs. Considering the continuity property of HSIs spectra, we design a simple smoothness constraint by using Tikhonov regularization for tensor decomposition to enhance the denoising performance. Moreover, we also design a new unidirectional total variation (TV) constraint to filter the stripe noise from HSIs. This strategy will achieve better performance for preserving images details than original TV models. The developed method is evaluated on both synthetic and real noisy HSIs, and shows the favorable results.

Existing moving object's trajectory prediction algorithms suffer from the data sparsity problem, which affects the accuracy of the trajectory prediction. Aiming to the problem, we present an Entropy-based Sparse Trajectories Prediction method enhanced by Matrix Factorization (ESTP-MF). Firstly, we do trajectory synthesis based on trajectory entropy and put synthesized trajectories into the trajectory space. It can resolve the sparse problem of trajectory data and make the new trajectory space more reliable. Secondly, under the new trajectory space, we introduce matrix factorization into Markov models to improve the sparse trajectory prediction. It uses matrix factorization to infer transition probabilities of the missing regions in terms of corresponding existing elements in the transition probability matrix. It aims to further solve the problem of data sparsity. Experiments with a real trajectory dataset show that ESTP-MF generally improves prediction accuracy by as much as 6% and 4% compared to the SubSyn algorithm and STP-EE algorithm respectively.

Software-defined networking (SDN) has emerged as a promising approach to enable network innovation, which can provide network virtualization through a hypervisor plane to share the same cloud datacenter network among multiple virtual networks. While, this attractive approach may bring some new problem that leads to more susceptible to the failure of network component because of the separated control and forwarding planes. The centralized control and virtual network sharing the same physical network are becoming fragile and prone to failure if the topology of virtual network and the control path is not properly designed. Thus, how to map virtual network into physical datacenter network in virtualized SDN while guaranteeing the survivability against the failure of physical component is extremely important and should fully consider more influence factors on the survivability of virtual network. In this paper, combining VN with SDN, a topology-aware survivable virtual network embedding approach is proposed to improve the survivability of virtual network by an enhanced virtual controller embedding strategy to optimize the placement selection of virtual network without using any backup resources. The strategy explicitly takes account of the network delay and the number of disjoint path between virtual controller and virtual switch to minimize the expected percentage of control path loss with survivable factor. Extensive experimental evaluations have been conducted and the results verify that the proposed technology has improved the survivability and network delay while keeping the other within reasonable bounds.

In recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.

Community detection is an important task in the social network analysis field. Many detection methods have been developed; however, they provide little semantic interpretation for the discovered communities. We develop a framework based on joint matrix factorization to integrate network topology and node content information, such that the communities and their semantic labels are derived simultaneously. Moreover, to improve the detection accuracy, we attempt to make the community relationships derived from two types of information consistent. Experimental results on real-world networks show the superior performance of the proposed method and demonstrate its ability to semantically annotate communities.

Polarimetric coherence strongly relates to the types and orientations of local scatterers. An optimization scheme is proposed to optimize the coherence between two polarimetric channels for polarimetric SAR (PolSAR) data. The coherence magnitude (correlation coefficient) is maximized by rotating a polarimetric coherence matrix in the rotation domain around the radar line of sight. L-band E-SAR and X-band Pi-SAR PolSAR data sets are used for demonstration and validation. The coherence of oriented manmade targets is significantly enhanced while that of forests remains relatively low. Therefore, the proposed technique can effectively discriminate these two land covers which are easily misinterpreted by the conventional model-based decomposition. Moreover, based on an optimized polarimetric coherence parameter and the total backscattered power, a simple manmade target extraction scheme is developed for application demonstration. This approach is applied with the Pi-SAR data. The experimental results validate the effectiveness of the proposed method.

In this paper, the statistical characteristics of rain attenuation in the equatorial zone are investigated. A more reasonable LMS channel model incorporating weather impairments is proposed and compared to the weather-affected Ka-band land mobile satellite (LMS) channel model suggested by Loo. The proposed LMS model uses Lutz's LMS channel model as its basis. The PDF of the received signal and BER performance derived from Loo's model and the proposed channel model are quantified and compared to verify the effectiveness of the proposed model. Finally, the influence of weather impairments on the BER performance is evaluated under various weather conditions, which clearly shows the superiority of the proposed model.