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1. An Integrated Communication and Ranging System based on OFDM | |||
HAN Yanhong,ZHANG Yuming | |||
Electrics, Communication and Autocontrol Technology 03 April 2023 | |||
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Abstract:Recent advancements in wireless communication and the miniaturization of equipment with limited resources have given rise to a new enabling technology integrated communication and raning (ICAR). However, the majority of existing researches are theoretical and rely solely on simulation to assess performance, making it difficult to obtain tangible evidence for real-world applications. Thus, in this paper, we build an ICAR platform based on orthogonal frequency division multiplexing (OFDM) for semi-physical verification. Specifically, this platform consists of two software defined radio (SDR) nodes, with hardware components including antennas and universal software radio peripherals (USRPs), while the software component utilizes Matlab. We have designed a duplex mode for the proposed system. In the lab setting, we implement node discovery and link detection, estimate the performance of communication and ranging. The experimental results demonstrate the effectiveness of the proposed system. | |||
TO cite this article:HAN Yanhong,ZHANG Yuming. An Integrated Communication and Ranging System based on OFDM[OL].[ 3 April 2023] http://en.paper.edu.cn/en_releasepaper/content/4760059 |
2. Monaural Speech Enhancement Using Combined Convolution Neural Network In The Time Domain | |||
ZHANG Cheng, JIANG Ting, YU Jia-Cheng | |||
Electrics, Communication and Autocontrol Technology 02 April 2022 | |||
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Abstract:Recent studies have shown that convolution network (CNN) has a good performance on modeling the long-term dependence of speech sequences in time domain. Multi-layer stacked dilated convolution is used to effectively enlarge the receptive field of network. However, the distance between the feature points mapped to the previous layer will become larger with the increase of dilated rate in higher layer, which easily leads to the neglect of the short-range information between the feature points. This paper proposes a plug-and-play inverted residual and linear bottleneck module called combined convolution (CB-Conv) module, aiming to extract the short-range information between feature points. The main part of CB-Conv module designs two parallel convolution blocks, one is common dilated convolution block, the other is aggregation convolution block. The latter mainly aggregates the lost details between adjacent points through pooling layer, and integrates with the output of the common dilated convolution to complete the information extraction. Experimental results on TIMIT datasets show that the proposed module achieves 1.04dB SI-SNR gain based on TasNet framework compared with the baseline Conv-TasNet under the condition of same number of stacted main module. | |||
TO cite this article:ZHANG Cheng, JIANG Ting, YU Jia-Cheng. Monaural Speech Enhancement Using Combined Convolution Neural Network In The Time Domain[OL].[ 2 April 2022] http://en.paper.edu.cn/en_releasepaper/content/4757315 |
3. A Visual SLAM system based on CNN descriptors | |||
CAI Chengying,JIAO Jichao | |||
Electrics, Communication and Autocontrol Technology 28 March 2022 | |||
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Abstract:Simultaneous Localization and Mapping (SLAM) system is very important for autonomous driving. The problem of data association between features becomes the bottleneck limiting the performance of traditional visual SLAM systems, especially in complex environments. In computer vision tasks, Convolutional Neural Networks (CNN) have better performance in complex environments than traditional methods. Therefore, many studies combine SLAM systems with CNN for more reliable data association. In this paper, we design a CNN network for extracting descriptors and combine it with hand-crafted keypoints to construct a visual SLAM system. The experiments in this paper show that CNN-based local descriptors can significantly improve the accuracy and robustness of the SLAM systems. Compared with traditional visual SLAM systems, the SLAM system in this paper is more robust in complex environments, and the localization error of the system in this paper is 24.4% and 33.3% lower than ORB-SLAM2 and VINS-Mono on the evaluated datasets. Meanwhile, CNN local descriptors can be combined with any visual SLAM system, this method has good portability. | |||
TO cite this article:CAI Chengying,JIAO Jichao. A Visual SLAM system based on CNN descriptors[OL].[28 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4757216 |
4. Dynamic Measurement Method for Available Bandwidth of 5G Bearer Network | |||
ZHAO Feng,GAO Weidong,CHUAI Gang | |||
Electrics, Communication and Autocontrol Technology 10 March 2022 | |||
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Abstract:The 5G bearer network is the network between core network and base station. Bearer network, as the information transmission channel between wireless access network and core network, shoulders the important task of transmitting various voice and data services. Therefore, the Qos of bearer network link must be guaranteed. In this paper, the available bandwidth (AB) of end-to-end network of 5G bearer network is dynamically detected to check whether the link has enough network resources. Kalman filter is used to track the effective bandwidth. In addition, this paper also introduces two background traffic modeling methods that are closer to the real network environment. The algorithm is implemented on NS3 simulation platform, and the simulation results show that the algorithm has good performance. | |||
TO cite this article:ZHAO Feng,GAO Weidong,CHUAI Gang. Dynamic Measurement Method for Available Bandwidth of 5G Bearer Network[OL].[10 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4756595 |
5. Nonlinear Statistical Convolutional Neural \\Network for Transmitter Identification \\ Under Noise Conditions | |||
Tianzi Li,Sai Huang | |||
Electrics, Communication and Autocontrol Technology 20 January 2022 | |||
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Abstract:Recently, identifying communication transmitters using deep neural networks under complicated wireless environments has received much attention. Since there is no public dataset to measure the performance of the deep learning algorithm, we construct a transmitter dataset based on various non-linearity of power amplifiers.In traditional methods, handicraft features and parameters need to be estimated to classify the transmitters, which are easily affected by noise conditions.Therefore, a Convolutional Neural Network (CNN) based method called Nonlinear Statistical CNN (NSCNN) that requires no prior knowledge of the transmitter model is utilized. By preprocessing the received signals, the nonlinear statistical properties of different signals within the signal are extracted.Extensive simulation shows the superiority of the proposed method, and the performance is close to the optimal maximum likelihood method. Finally, the results of the received signals using different algorithms and symbol lengths are also discussed. | |||
TO cite this article:Tianzi Li,Sai Huang. Nonlinear Statistical Convolutional Neural \\Network for Transmitter Identification \\ Under Noise Conditions[OL].[20 January 2022] http://en.paper.edu.cn/en_releasepaper/content/4756147 |
6. 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 | |||
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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 |
7. 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 | |||
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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 |
8. Subspace-Based Method for Near-Field Source Localizationin Presence of Complicated Noise | |||
WANG Peiyang,WANG Guangmin | |||
Electrics, Communication and Autocontrol Technology 09 April 2018 | |||
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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 |
9. Research on Parking Space Sensing System Based on Wireless Sensor Networks | |||
Du Nianwei,Wen Zhigang | |||
Electrics, Communication and Autocontrol Technology 11 December 2017 | |||
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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] http://en.paper.edu.cn/en_releasepaper/content/4742569 |
10. Cooperative Energy Detection Of Double Threshold | |||
Huang Mei,Li Shufang | |||
Electrics, Communication and Autocontrol Technology 25 November 2016 | |||
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Abstract:Cognitive radio is one of the primary technology to solve the underutilization problem of the spectrum, in recent time, The State Commission has put up a new wireless system in LTE 230 spectrum. In this paper, we consider an amplify-and-forward relay-based cooperative spectrum sensing using a double threshold energy detector. Each second cognitive user takes a local decision on spectrum occupancy based on two energy detection thresholds, which is according to the channel fading. Cooperative probability of detection, probability of missed detection and probability of false alarm are derived theoretically. Simulation results show that the propose method makes an improvement in spectrum sensing. | |||
TO cite this article:Huang Mei,Li Shufang. Cooperative Energy Detection Of Double Threshold[OL].[25 November 2016] http://en.paper.edu.cn/en_releasepaper/content/4710529 |
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