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The turbulence has a harmful effect on the beam propagating through the atmosphere. Turbulence will cause the wavefront aberration, resulting in spot drift, irradiance fluctuations and other forms of degradations. The propagation characteristics of partially coherent Bessel beams (PCBBs) in the vertical link are studied by means of wave optics simulation. The aperture averaged scintillation of PCBBs in the vertical link increases with beam order, and the mean signal-to-noise ratio (SNR) of beams used as communication link decreases with beam order. The capability of the PCBBs to resist the influence of the turbulence is compared with that of the partially coherent Gaussian beam (PCGB). The results show that, the PCBBs are more resilient to the perturbations of the turbulence.

In space optical remote sensor, the relative position and orientation of primary mirror and secondary mirror have important influence on high quality imaging. Adjusting the secondary mirror pose is one of the means to improve the imaging quality of space camera. In order to meet the requirements of secondary mirror pose adjustment and improve the performance and economy of the mechanism, the Stewart platform, a typical kind of 6-DOF parallel mechanism, is optimized. Firstly, a parametric model of the Stewart platform is established in ADAMS. Secondly, the optimal objective function of the Stewart platform is established. Thirdly, According to the actual constraint conditions, the constraint function is set up. Finally, the ADAMS simulation software is used to add objective function and constraint functions to optimize the structure parameters and optimized structural parameters are obtained. After parameter checking, all indexes of the optimized secondary mirror positioner meet the requirements.

The object of this work is to improve secrecy capacity of primary user (PU) as well as spectrum utilization. To this end, we present a cognitive relaying enabled efficiency and physical-layer security preserving (CREEP) framework, in which secondary users (SUs) are allowed to perform secondary transmissions. By enabling dual-polarized antenna and leveraging polarization processing at SUs, signals from legitimate users can be separated at the corresponding receivers, while the eavesdropper (EVE) cannot obtain information from PU. We formulate the system model, signaling plan, and key enabling techniques to enhance the spectrum efficiency via allowing SUs' communications, and to preserve PU's physical-layer security via SUs' jamming at EVE. To examine the performance of CREEP, we provide theoretic analysis of a sum capacity maximization under certain rate and power constraints. Numerical results show CREEP not only improves PU's secrecy capacity but also enhances spectrum utilization.

In multichannel cognitive radio networks (CRN), the sequence of sensing order is essential to determine the efficiency of detecting an idle channel and the effective time for spectrum access. Current works on the optimal channel sensing order have to know priori knowledge such as the channel idle probabilities, channel gains and achievable rates of different channels, which dynamically evolve and are difficult to obtain in practice. We are thus motivated to study a model-free sensing order selection scheme for multichannel CRN. In particular, we consider a time-slotted imperfect sensing CRN, where sensing errors impose more challenges to the selection of sensing order. The sensing order selection is modeled as a Q-Learning problem, where secondary users (SU) make intelligent decision on sensing order selection by learning from historical sensing and transmissions. Simulation results show that theq proposed scheme is robust in dynamic environment and can achieve significant throughput improvement compared with random sequential sensing.

The PnP (perspective-n-point) problem is very important in pose estimation technique based on computer vision. Aiming at this issue, an improved iterative solution is proposed. By the means of expressing the 3D point coordinates as a weighted sum of four control points, a system of homogeneous linear equations was established and then the optimized projections on the normalized image plane were obtained. The final estimation result was achieved by a relaxation-based iterative approach. Both simulations and experiments certify that the proposed algorithm can improve the computing accuracy and depress the image noise. Compared with other solutions to the PnP problem, the proposed algorithm can reduce the computational complexity while maintaining high precision with noise depression capability.

Height field and grid data are frequently updated during simulation of large scale ocean water, which reduces the frame rates of whole virtual scene. A CUDA-based framework for unbounded real-time ocean rendering is proposed in this paper. Firstly, height field data is calculated on GPU using FFT method. Then the whole view frustum dependent dynamic grid is created by an adaptive algorithm with continuous LOD, and positions of vertices are updated in CUDA kernels. Finally, normal map is generated using height field data on GPU and blended with perlin noise to shade ocean surface. Data transferred between GPU memory and primary memory is constrained during rendering loop. Experiments show the proposed framework achieves several times of speedup in grid updating efficiency than CPU implementation, and gets real-time and realistic ocean rendering results as well.

This paper considers energy-constrained cognitive radios (CR) which can access a time-slotted licensed spectrum band in hybrid mode. At the beginning of each time slot, CR decides whether to be idle or to transmit. If transmission, CR has to determine which access mode to operate in. If the underlay mode is selected, CR shares spectrum with licensed systems by transmitting with a low power to ensure interference constraints. If the hybrid mode is selected, CR has to carry out spectrum sensing to detect spectrum occupancy. If the sensing outcome indicates idle, CR operates in overlay access mode; otherwise, CR operates in underlay access mode. The objective is to investigate the tradeoff between energy consumption and throughput gain. To this end, CR dynamically determines its operation mode for each time slot, e.g., to be idle or to transmit, and determines the sensing time and access mode if it is to transmit. We formulate this problem as a partially observable Markov decision process (POMDP) and prove important properties of the optimal policy. Simulation results validate our analysis and performance of the proposed dynamic control approach.

In this paper, we study the performance of joint spatial-temporal spectrum sensing based cognitive radio networks (CRNs). The relationship between spatial spectrum sensing and temporal spectrum sensing under the interference constraint of primary user is investigated. Both the spectrum sensing performance and throughput of secondary system is related to sensing duration, and we argue that there exists a tradeoff between them with a given false alarm probability and detection probability, respectively. An optimization problem is formulated to maximize the throughput of secondary system in CRNs. Joint spatial-temporal spectrum sharing outperforms both spatial and temporal spectrum sharing in terms of the throughput in the advantage region.

Joint transmission (JT) is a promising coordinated multipoint (CoMP) transmission scheme for achieving high downlink capacities in wireless cellular networks of next generation. In this paper, an adaptive scheme, named as partial JT CoMP is presented. This scheme improves the network throughput by dynamically adjusting points involving in jointly transmitting according to the instantaneous channel information. The tradeoff between diversity and multiplexing of multiple antennas is exploited to maximize data rate per transmission. Besides, an algorithm to solve the scheduling problem of a cluster for partial JT CoMP is proposed. Results reveal a significant performance improvement in the throughput by the proposed partial JT CoMP strategy.

In a downlink multiuser multiple-input and multiple-output (MU-MIMO) system, a base station (BS) communicates with multiple mobile stations (MS) simultaneously in a given spectrum band. The performance of a MU-MIMO system depends on the choice of user selection, power allocation and precoding schemes. In this paper, we study user selection in Zero-Forcing (ZF) precoding based MU-MIMO systems to maximize the sum rate for high data rate applications. We derive analytical results for simultaneous transmission to two MSs with zero forcing (ZF) precoding, based on which we propose a extended low-complexity algorithm that jointly considers the noise power, the channel gain of the candidate MSs and orthogonality with respect to the selected MSs' channels. Analysis shows that the proposed scheme requires much lower complexity than current schemes. Simulation results demonstrate that the proposed algorithm outperforms existing algorithms in terms of higher throughput.

The general model predictive current control uses a discrete-time model of the system to predict the future current behavior for all the possible voltage vectors generated by the inverter, and then the vector which minimizes a cost function is selected and applied. In this paper, a simplified model predictive current control method for a three-phase voltage-source inverter is presented in order to reduce the amount of calculations in the practical implementation of this method, especially for multilevel ones with large predictions. Utilizing the sector information of the reference voltage space vector, the proposed method just needs a subset of all the available voltage vectors for the prediction and optimization. The computational effort has been greatly reduced while the steady-state performance and dynamic response of the current control are improved. In addition, a reduced switching frequency is obtained. Simulation results are presented to verify the proposed method.

In UMTS cellular networks, Common Pilot Channel (CPICH) signals are broadcast by base stations for channel estimation and cell selection. The strength of CPICH signal determines cell coverage and pilot pollution to neighboring cells; pilot power allocation thus involves a tradeoff between coverage and interference. In this paper, we study the issue of pilot power management in closed-access High-Speed Downlink Packet Access (HSDPA) femtocell network. We minimize the total pilot power in a HSDPA femtocell network subject to instantaneous coverage requirements. We formulate a generalized optimization problem in a femtocell network and propose a suboptimal analytic solution to pilot power allocation implemented in each femtocell. Simulation results show that the proposed algorithm outperforms fixed pilot power schemes in terms of much lower allocated pilot power and interference to macro user equipments.

Multichannel sensing scheduling and interference avoidance are two major challenges for medium access in Cognitive Radio Networks (CRN). In this paper, we propose an adaptive sensing cycle scheme for scheduling multichannel sensing with interference avoidance in CRN. The principle of the proposed adaptive sensing cycle scheme is to prioritize the multichannel sensing and access based on interference estimation from packet statistics. To keep fairness of media access within the CRN, the sensing cycle is formulated under the constrained of constant overall sensing rate for all nodes. Unlike conventional spectrum sensing in physical layer, the interference estimation is based on packet statistics in Media Access Control (MAC) layer and measures the statistical channel availability beyond instantaneous channel availability. Radio performance is studied in a test-bed environment under various interference intensities: the results show that more than 45% throughput improvement is achieved in interference-intensive environments.

A special kind of direct driving Tension Leveller of Electro-galvanized unit in the Cold-Rolling plant of an Iron & Steel Company in China was mad as the research object. Firstly, the structure of the transmission system and its working principle were introduced. Then, to make a target of improving the transmission system's characteristics, the reference roller control strategy was made. In addition, the model of the transmission system which based on two kinds of control strategies whose characteristic were analyzed and compared through the simulation results was found and simulated. Some useful simulation results were verified by the actual tests finally.

A general model for complex electromagnetic environments incorporating multiplicative and additive noise is proposed. The locally optimum detector is derived for weak signal detection and shows that the influence of multiplicative noise in its mean multiplied to the statistics. The finite sample performance is evaluated by Monte-Carlo simulation with the comparison of other detectors and the result indicates that the LO method provides best performance for weak signal detection in non-Gaussian noise.

As a generalization of classical Fourier transformation (FT), the fractional Fourier transform (FrFT) outperforms many other time frequency analysis techniques with its higher time-frequency resolution, but it is quite new to be employed into the biomedical signal processing. In this paper, the FrFT has been investigated for the application of biomedical signal detection, which shows to be effective to nonstationary weak signal detection in additive white Gaussian noise (AWGN). The second-order fractional FT moments and estimation are used for the multi-component chirps and interference situation. The results indicate the great advantage on biomedical signal detection by FrFT.