76 research outputs found
Quantum correlated photons via a passive nonlinear microcavity
Photons, by nature, typically do not exhibit interactions with each other.
Creating photon-photon interactions holds immense importance in both
fundamental physics and quantum technologies. Currently, such interactions have
only been achieved indirectly as mediated by atomic-like quantum emitters with
resonant photon-atom interactions. However, the use of these indirect
interactions presents substantial fundamental challenges that impede scaling
and practical applications. Here we demonstrate creation of non-classical
photon correlations, including photon anti-bunching, via a passive InGaP
photonic integrated circuit. Our approach employs the quantum interference
between uncorrelated light and the two-photon bound state, the latter of which
arises from the -mediated photon interaction. Our work opens a new
route in controlling quantum light by harnessing highly-engineerable bulk
optical nonlinearities, which has significant implications for nonlinear
optical quantum information processing and quantum networking.Comment: 26 pages, 15 figures, 2 table
Optimization for Variable Height Wind Farm Layout Model
The optimization of wind farm layouts is very important for the effective utilization of wind resources. A fixed wind turbine hub height in the layout of wind farms leads to a low wind energy utilization and a higher LCOE (levelized cost of electricity). WOMH (Wind Farm Layout Optimization Model Considering Multiple Hub Heights) is proposed in this paper to tackle the above problem. This model is different from the traditional fixed hub height model, as it uses a variable height wind turbine. In WOMH, the Jensen wake and Weibull distribution are used to describe the wake effect on the wind turbines and wind speed distribution, respectively. An algorithm called DEGM (differential evolution and greedy method with multiple strategies) is proposed to solve WOMH, which is NP hard. In the DEGM, seven strategies are designed to adjust the distribution coordinates of wind turbines so that the height of the wind turbines will be arranged from low to high in the wind direction. This layout reduces the Jensen wake effect, thus reducing the value of the LCOE. The experimental results show that in the DEGM, when the number of wind turbines is 5, 10, 20, 30 and 50, the WOMH reduces the LCOE by 13.96%, 12.54%, 8.22%, 6.14% and 7.77% compared with the fixed hub height model, respectively. In addition, the quality of the solution of the DEGM is more satisfactory than that of the three-dimensional greedy algorithm and the DEEM (differential evolution with a new encoding mechanism) algorithm. In the case of five different numbers of wind turbines, the LCOE of DEGM is at least 3.67% lower than that of DEEM, and an average of 6.83% lower than that of three-dimensional greedy. The model and algorithm in this paper provide an effective solution for the field of wind farm layout optimization
Minimizing Age of Information in UAV-Assisted Data Collection With Limited Charging Facilities
Age of Information (AoI) has become a new metric for data freshness in Unmanned Aerial Vehicle (UAV)-assisted data collection. Whereas, the UAVs are usually energy-limited. When they leave to be charged, the AoI may increase additionally. Current studies of optimizing the AoI usually consider the situation where charging facilities are sufficient, which is not true in many scenarios. For the situation where only one UAV can be charged at one time, we propose a multi-agent cooperative Deep Q-Network algorithm with delayed reward (DR-MADQN) to minimize the AoI. Simulation results show DR-MADQN can effectively decrease the AoI and charge waiting time
Telemedicine and digital management in repair and regeneration after nerve injury and in nervous system diseases
3-D Deformation Measurement Based on Three GB-MIMO Radar Systems: Experimental Verification and Accuracy Analysis
Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets
Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets
An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR
Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method
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