Xidian University

About: Xidian University is a education organization based out in Xi'an, China. It is known for research contribution in the topics: Antenna (radio) & Computer science. The organization has 32099 authors who have published 38961 publications receiving 431820 citations. The organization is also known as: University of Electronic Science and Technology at Xi'an & Xīān Diànzǐ Kējì Dàxué.

An Efficient Deep Learning Model to Predict Cloud Workload for Industry Informatics

Abstract: Deep learning, as the most important architecture of current computational intelligence, achieves super performance to predict the cloud workload for industry informatics. However, it is a nontrivial task to train a deep learning model efficiently since the deep learning model often includes a great number of parameters. In this paper, an efficient deep learning model based on the canonical polyadic decomposition is proposed to predict the cloud workload for industry informatics. In the proposed model, the parameters are compressed significantly by converting the weight matrices to the canonical polyadic format. Furthermore, an efficient learning algorithm is designed to train the parameters. Finally, the proposed efficient deep learning model is applied to the workload prediction of virtual machines on cloud. Experiments are conducted on the datasets collected from PlanetLab to validate the performance of the proposed model by comparing with other machine-learning-based approaches for workload prediction of virtual machines. Results indicate that the proposed model achieves a higher training efficiency and workload prediction accuracy than state-of-the-art machine-learning-based approaches, proving the potential of the proposed model to provide predictive services for industry informatics.

Containment control of heterogeneous multi-agent systems

Abstract: In this paper, we consider the containment control problem for a group of autonomous agents modelled by heterogeneous dynamics. The communication networks among the leaders and the followers are directed graphs. When the leaders are first-order integrator agents, we present a linear protocol for heterogeneous multi-agent systems such that the second-order integrator agents converge to the convex hull spanned by the first-order integrator agents if and only if the directed graph contains a directed spanning forest. If the leaders are second-order integrator agents, we propose a nonlinear protocol and obtain a necessary and sufficient condition that the heterogeneous multi-agent system solves the containment control problem in finite time. Simulation examples are also provided to illustrate the effectiveness of the theoretical results.

Device-to-device communications for enhancing quality of experience in software defined multi-tier LTE-A networks

Abstract: 3GPP has been developing LTE-A standard to significantly enhance the performance of cellular networks by utilizing various radio access techniques to provide ubiquitous and seamless broadband access to a rich diversity of mobile connected devices, which usually demand high QoE in spite of of access location or time. Consequently, it imposes unprecedented stringent requirements on centralized network management at the operator side, which has to overcome formidable complexity in designing, managing, and configuring network architectures, protocols, and algorithms before being able to fully support all LTE-A techniques. It is noticed that the software defined wireless network concept appears to be a promising direction to address such complexity, by decoupling control logic from all network elements and then providing fine-grained control and measurement in LTE-A networks. Toward this end, we propose in this article a device-to-device communication-based algorithm to enhance the QoE of users in software defined multi-tier LTE-A networks. Besides discussing research issues that deserve further study, we also present numerical results to illustrate the performance gains that can be achieved by applying the proposed algorithm to a typical 3GPP network scenario.

Fuzzy C-means clustering algorithm based on kernel method

Abstract: In this paper, we propose a fuzzy kernel C-means clustering algorithm (FKCM) which is based on conventional fuzzy C-means clustering algorithm (FCM). This new FKCM algorithm integrates FCM with Mercer kernel function and deals with some issues in fuzzy clustering. The properties of the new algorithms are illustrated the FKCM algorithm is not only suitable for clusters with the spherical shape, but also other non-spherical shapes such as annular ring shape effectively.

GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues.

Abstract: As the most accurate model for simulating light propagation in heterogeneous tissues, Monte Carlo (MC) method has been widely used in the field of optical molecular imaging. However, MC method is time-consuming due to the calculations of a large number of photons propagation in tissues. The structural complexity of the heterogeneous tissues further increases the computational time. In this paper we present a parallel implementation for MC simulation of light propagation in heterogeneous tissues whose surfaces are constructed by different number of triangle meshes. On the basis of graphics processing units (GPU), the code is implemented with compute unified device architecture (CUDA) platform and optimized to reduce the access latency as much as possible by making full use of the constant memory and texture memory on GPU. We test the implementation in the homogeneous and heterogeneous mouse models with a NVIDIA GTX 260 card and a 2.40GHz Intel Xeon CPU. The experimental results demonstrate the feasibility and efficiency of the parallel MC simulation on GPU.