By tilting a plasma jet and rotating 360°,a large-area can be scanned and sterilized in a short time.Compared with the previous array device,this pipe has the significant advantages of high sterilization uniformi...By tilting a plasma jet and rotating 360°,a large-area can be scanned and sterilized in a short time.Compared with the previous array device,this pipe has the significant advantages of high sterilization uniformity and low gas consumption.Firstly,a rotatable plasma jet device,which can control the swing and rotation of a jet pipe,is designed,and a corresponding theoretical model is established to guide the experiment.Secondly,with Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli)as the target bacteria,the device achieves a short sterilization time of 158 s—the minimum sterilization flow of S.aureus and E.coli is 0.8 slm and 0.6 slm,respectively.The device is compared with an array plasma sterilization device in terms of sterilization speed and gas consumption.The results show that the device is not only better than an array plasma sterilization device with respect to scanning uniformity,but also far less than the array plasma sterilization device in gas consumption of 5 slm.Therefore,the device has great potential in applications involving efficient,large-area sterilization.展开更多
In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replac...In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.展开更多
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,an...We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.展开更多
基金partially supported by National Natural Science Foundation of China(Nos.61864001 and 62163009)Key Projects of Guangxi Natural Science Foundation(No.2021JJD170019)+1 种基金the Foundation of Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(Guilin University of Electronic Technology)(Nos.YQ21111 and YQ21204)Innovation Project of Guang Xi Graduate Education(No.YCSW2021181)。
文摘By tilting a plasma jet and rotating 360°,a large-area can be scanned and sterilized in a short time.Compared with the previous array device,this pipe has the significant advantages of high sterilization uniformity and low gas consumption.Firstly,a rotatable plasma jet device,which can control the swing and rotation of a jet pipe,is designed,and a corresponding theoretical model is established to guide the experiment.Secondly,with Staphylococcus aureus(S.aureus)and Escherichia coli(E.coli)as the target bacteria,the device achieves a short sterilization time of 158 s—the minimum sterilization flow of S.aureus and E.coli is 0.8 slm and 0.6 slm,respectively.The device is compared with an array plasma sterilization device in terms of sterilization speed and gas consumption.The results show that the device is not only better than an array plasma sterilization device with respect to scanning uniformity,but also far less than the array plasma sterilization device in gas consumption of 5 slm.Therefore,the device has great potential in applications involving efficient,large-area sterilization.
基金supported in part by the National Natural Science Foundation of China No.62001220the Natural Science Foundation of Jiangsu Province BK20200440the Fundamental Research Funds for the Central Universities No.1004-YAH20016,No.NT2020009。
文摘In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.
文摘We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.