A graphene reflectarray is designed to realize two functions simultaneously at terahertz frequency: generating vortex waves, and steering the waves. Four parts of graphene patches with different values of reflection p...
详细信息
A graphene reflectarray is designed to realize two functions simultaneously at terahertz frequency: generating vortex waves, and steering the waves. Four parts of graphene patches with different values of reflection phase are used to generate vortex waves. On the other hand, generalized Snell's Law is applied to introduce additional reflection phase. Anomalous reflection is realized to steer the Vortex wave by 30 degrees.
We propose a time synchronization modeling and error compensation method for Software-Defined TSN. Experiments verify that the combination method of POE and NNPID can effectively improve time synchronization performan...
Coexistence of cellular networks and wireless local area network (WLAN) in unlicensed spectrum will be a vital application scenario in the future 5G network. Listen-before-talk (LBT) is one of the mechanisms to ensure...
详细信息
Wireless multimedia traffic has increased considerably in recent years, which puts forward higher demand on the capacity of existing network. Multicast, as a transmission strategy with high spectral efficiency, has no...
详细信息
Diffraction loss exists in the corner of L-corridor in *** measurement and simulation show that conventional diffraction propagation model does not fit *** diffraction loss increases linearly with frequency on 0.831 G...
详细信息
Diffraction loss exists in the corner of L-corridor in *** measurement and simulation show that conventional diffraction propagation model does not fit *** diffraction loss increases linearly with frequency on 0.831 GHz.661 GHz,which is around7~14 dB.
Visual saliency detection (VSD) has been attracting increasing attention due to its wide applications in computer visions. In this paper, a visual saliency detection method based on maximum entropy random walk (MEVSD)...
详细信息
In this paper, the problem of data correlation-aware resource management is studied for a network of wireless virtual reality (VR) users communicating over cloud-based small cell networks (SCNs). In the studied model,...
详细信息
In this paper, the problem of data correlation-aware resource management is studied for a network of wireless virtual reality (VR) users communicating over cloud-based small cell networks (SCNs). In the studied model, small base stations (SBSs) with limited computational resources act as VR control centers that collect the tracking information from VR users over the cellular uplink and send them to the VR users over the downlink. In such a setting, VR users may send or request correlated or similar data (panoramic images and tracking data). This potential spatial data correlation can be factored into the resource allocation problem to reduce the traffic load in both uplink and downlink. This VR resource allocation problem is formulated as a noncooperative game that allows jointly optimizing the computational and spectrum resources, while being cognizant of the data correlation. To solve this game, a transfer learning algorithm based on the machine learning framework of echo state networks (ESNs) is proposed. Unlike conventional reinforcement learning algorithms that must be executed each time the environment changes, the proposed algorithm can intelligently transfer information on the learned utility, across time, to rapidly adapt to environmental dynamics due to factors such as changes in the users' content or data correlation. Simulation results show that the proposed algorithm achieves up to 16.7% and 18.2% gains in terms of delay compared to the Q-learning with data correlation and Q-learning without data correlation. The results also show that the proposed algorithm has a faster convergence time than Q-learning and can guarantee low delays.
The Content-Centric network (CCN) is a very important structure in the future network, in which every node has caching ability. Caching strategy has a decisive influence on the performance of the CCN. In this paper, a...
This paper presents a tri-band bandpass filter with high design flexibility in symmetrical structure and a wide stopband by using quad-mode stub loaded resonator. Stub-to-stub coupling is introduced to split two ident...
详细信息
This paper presents a tri-band bandpass filter with high design flexibility in symmetrical structure and a wide stopband by using quad-mode stub loaded resonator. Stub-to-stub coupling is introduced to split two identical modes and produce two transmission zeros. The harmonic passband can be typically eliminated by regularly embedding open-circuited stubs in the feeding structure without affecting the performance of the three passbands. A filter prototype is simulated to validate the proposed design. The results show a desired wide upper stopband at 8.8 GHz with rejection better than 20 dB and three passbands are centered at 2.45 GHz, 3.68 GHz, and 5.19 GHz respectively, which is applicable to WLAN and WiMAX applications.
This paper proposes and improves a model for China's cotton reserves trading market, generates a more accurate price level table in contrast to the widely used China Cotton Association(CCA)'s table, and predic...
This paper proposes and improves a model for China's cotton reserves trading market, generates a more accurate price level table in contrast to the widely used China Cotton Association(CCA)'s table, and predicts the future trading price with this model. Data used is all 29895 trade records of cotton reserves from May, 2016 to Sept, 2016. In this paper, we firstly give a briefly introduction to the data as well as basic knowledge of cotton, especially the CCA's price level table as the target to improve accuracy. We then present a multiple linear regression with dummy variable model, which can reduce the error on predicting the trading price of current month, but still remains some problems such as the result shows a batch of cotton with better quality may get a lower price. So we finally advance the model with multidimensional isotonic regression and more factors taken into consideration. Our last model could be used to predict the trade price of both current month and next month with approximately 4% mean absolute percentage error(MAPE).
暂无评论