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	1. Design of Continuous Inverse Class-F Mode Doherty Power Amplifier With Complex Back-off Load
	ZHANG Weikai,LIU Yuanan
	Electrics, Communication and Autocontrol Technology 15 March 2019
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (1203K B)

	Abstract:This paper presents an improved continuous inverse Class-F broadband Doherty power amplifier (DPA) design methodology to enhance the bandwidth and back-off efficiency. By analyzing and modeling the reactance effect of the auxiliary power amplifier (APA) in the cut-off state, the complex impedance is determined as the load impedance of the main power amplifier at the back-off point. Further, the main power amplifer (MPA) is designed working in continuous inverse Class-F mode. While the performance of the band edge shows good consistency, it also maintains high efficiency at 8 dB back-off level. To verify the theory, gallium nitride (GaN) power amplifiers CGH40025F and CG2H40010F from Wolfspeed were used. The simulation results show that the DPA's working band is 1.8 - 2.7 GHz, while the saturated output power ranges from 44.6 dbm to 45.8 dBm, the efficiency is between 52\% and 64\% at 8 dB back-off region with a gain of 8-9 dB.
	TO cite this article:ZHANG Weikai,LIU Yuanan. Design of Continuous Inverse Class-F Mode Doherty Power Amplifier With Complex Back-off Load[OL].[15 March 2019] http://en.paper.edu.cn/en_releasepaper/content/4747885


	2. Object Tracking Algorithm Based on and L2-regularization Least Square and Convolutional Networks
	ZHOU Fei,XUE Bin,AN Kangning,GAO Jianjun
	Electrics, Communication and Autocontrol Technology 09 April 2018
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (431K B)

	Abstract:Object tracking is a hot and difficult research topic in computer vision. In this paper, we propose a object tracking algorithm based on L2 regularization least squares method and convolution network under the particle filter framework. Firstly, the extent of occlusion can be evaluated by L2 tracker. Secondly, convolutional networks is used to locate the target object if the extent of occlusion satisfies two inequality constraints. In order to make convolutional networks suitable for tracking tasks with high real-time requirements, this thesis uses a simple two-layer convolutional networks to represent the targets robustly. Finally, most of the insignificant samples are removed before applying convolutional networks, which reduces the complexity of the algorithm. The experimental results on numerous challenging image sequences show that the proposed method is more robust and stable than L2 tracker when the target object undergoes dramatic appearance changes such as pose variation or rotation and is superior in accuracy to other classical tracking algorithms.
	TO cite this article:ZHOU Fei,XUE Bin,AN Kangning, et al. Object Tracking Algorithm Based on and L2-regularization Least Square and Convolutional Networks[OL].[ 9 April 2018] http://en.paper.edu.cn/en_releasepaper/content/4744496


	3. Subspace-Based Method for Near-Field Source Localizationin Presence of Complicated Noise
	WANG Peiyang,WANG Guangmin
	Electrics, Communication and Autocontrol Technology 09 April 2018
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (131K B)

	Abstract:In this paper, we consider the problem of estimatingthe directions-of-arrival (DOAs) and ranges of multiple nearfieldnarrowband signals impinging on a symmetric uniformlinear array (ULA) in presence of complicated noise (e.g., in nonuniform noise) in practical applications.By forming a Toeplitz-like correlation matrix from the antidiagonalelements of the array covariance matrix, a new subspace-basedlocalization method is proposed, where the null space is obtainedthrough eigendecomposition of the resultant Toeplitz-like matrix,and the MUSIC method is used to estimate the locationparameters. Finally,the effectiveness of the proposed method is verified throughnumerical examples.
	TO cite this article:WANG Peiyang,WANG Guangmin. Subspace-Based Method for Near-Field Source Localizationin Presence of Complicated Noise[OL].[ 9 April 2018] http://en.paper.edu.cn/en_releasepaper/content/4744472


	4. Research on Parking Space Sensing System Based on Wireless Sensor Networks
	Du Nianwei,Wen Zhigang
	Electrics, Communication and Autocontrol Technology 11 December 2017
	Show/Hide Abstract \| Cite this paper︱Full-text: PDF (406K B)

	Abstract:This paper firstly discusses the current ways of parking lot detecting. Although intelligent parking has playing an increasingly important role in the field of smart transportation, however, in the currently commercialized parking space detecting system, the forms of scheme are various, but there have some disadvantages such as low accuracy, poor anti-jamming ability and so on. Then we consider a parking lot sensing system based on Ultra-Wideband (UWB) Radar and studying the parking sensing algorithm. The primal work is studying and introducing UWB Radar into the detecting system, selecting (signal strength-distance) two-dimensional data as the determination of the parking situation data sets, and using typical SVM classification algorithm and K-means clustering algorithm of machine learning filed to process the data. A parking space sensing system is designed and implemented. Also, the test cases are carried out to demonstrate the performance of the proposed schemes for the system.
	TO cite this article:Du Nianwei,Wen Zhigang. Research on Parking Space Sensing System Based on Wireless Sensor Networks[OL].[11 December 2017]

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