To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning...To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.展开更多
Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to fi...Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.展开更多
Earthquake events occurred in Nantong, Jiangsu Province on Nov. 3, and Dec. 25, 2001 in which the biggest mag-nitudes were ML=3.8 and ML=4.1, respectively. This paper firstly explains the principle of the eliminating ...Earthquake events occurred in Nantong, Jiangsu Province on Nov. 3, and Dec. 25, 2001 in which the biggest mag-nitudes were ML=3.8 and ML=4.1, respectively. This paper firstly explains the principle of the eliminating noise method by the multi-dipole observation system of geoelectric field. Then based on the observation data of the multi-dipole observation system obtained by ZD9A telluric current monitors installed in Chongming and Nanjing stations, we study the abnormal variation of the geoelectric field preceding the earthquakes. The study shows that: a) Eliminating noise method of multi-dipole observation is an excellent method by which many kinds of geoelec-tric field noises can be eliminated successfully and the geoelectric precursor information can be recognized; b) The geoelectric precursor signals for the events were recorded on the NS and NE dipoles in Chongming station 42 days, 20 days and 2 days before the earthquakes respectively, in which the station is near the epicenter, and the longest time of persisting period was 9 days. The abnormal variation signals of geoelectric field observed in Nanjing sta-tion are all the noises but not the seismic electric signals, in which the station is not near the epicenter; c) Dipole distribution method of common electrode is not good in the multi-dipole observation system of the geoelectric field.展开更多
Rafael C. Gnzalez has mentioned an algorithm on adaptive local noise elimination filter in the book named Digital Image Processing. This paper points out the algorithm＇s deficiency and presents an improved harmonic m...Rafael C. Gnzalez has mentioned an algorithm on adaptive local noise elimination filter in the book named Digital Image Processing. This paper points out the algorithm＇s deficiency and presents an improved harmonic mean filter algorithm which makes mean square error emse cutting quarter but SNR, SNPm and PSNR increasing a tenth more than original algorithm. This filter algorithm is verified to be effective by simulation experiment.展开更多
基金Project supported by the National Key R&D Program of China(No.2020YFF01015000ZL)the Fundamental Research Funds for the Central Universities,China(No.3072022CF0806)。
文摘To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.
基金This work supports in part by National Key R&D Program of China(No.2018YFB2100400)National Science Foundation of China(No.61872100)+2 种基金Industrial Internet Innovation and Development Project of China(2019)PCL Future Regional Network Facilities for Large-scale Experiments and Applications(PCL2018KP001)Guangdong Higher Education Innovation Team(NO.2020KCXTD007).
文摘Material identification is a technology that can help to identify the type of target material.Existing approaches depend on expensive instruments,complicated pre-treatments and professional users.It is difficult to find a substantial yet effective material identification method to meet the daily use demands.In this paper,we introduce a Wi-Fi-signal based material identification approach by measuring the amplitude ratio and phase difference as the key features in the material classifier,which can significantly reduce the cost and guarantee a high level accuracy.In practical measurement of WiFi based material identification,these two features are commonly interrupted by the software/hardware noise of the channel state information(CSI).To eliminate the inherent noise of CSI,we design a denoising method based on the antenna array of the commercial off-the-shelf(COTS)Wi-Fi device.After that,the amplitude ratios and phase differences can be more stably utilized to classify the materials.We implement our system and evaluate its ability to identify materials in indoor environment.The result shows that our system can identify 10 commonly seen liquids with an average accuracy of 98.8%.It can also identify similar liquids with an overall accuracy higher than 95%,such as various concentrations of salt water.
基金Chinese Joint Seismological Science Foundation (102080) and State Science and Technology Target Key Project (2001B A601B01-04-02) during theTenth Five-year Plan.
文摘Earthquake events occurred in Nantong, Jiangsu Province on Nov. 3, and Dec. 25, 2001 in which the biggest mag-nitudes were ML=3.8 and ML=4.1, respectively. This paper firstly explains the principle of the eliminating noise method by the multi-dipole observation system of geoelectric field. Then based on the observation data of the multi-dipole observation system obtained by ZD9A telluric current monitors installed in Chongming and Nanjing stations, we study the abnormal variation of the geoelectric field preceding the earthquakes. The study shows that: a) Eliminating noise method of multi-dipole observation is an excellent method by which many kinds of geoelec-tric field noises can be eliminated successfully and the geoelectric precursor information can be recognized; b) The geoelectric precursor signals for the events were recorded on the NS and NE dipoles in Chongming station 42 days, 20 days and 2 days before the earthquakes respectively, in which the station is near the epicenter, and the longest time of persisting period was 9 days. The abnormal variation signals of geoelectric field observed in Nanjing sta-tion are all the noises but not the seismic electric signals, in which the station is not near the epicenter; c) Dipole distribution method of common electrode is not good in the multi-dipole observation system of the geoelectric field.
基金This project is supported by National Natural Science Foundation of China （60473024） and the Natural Science Foundation of Zhejiang Province（603009）..
文摘Rafael C. Gnzalez has mentioned an algorithm on adaptive local noise elimination filter in the book named Digital Image Processing. This paper points out the algorithm＇s deficiency and presents an improved harmonic mean filter algorithm which makes mean square error emse cutting quarter but SNR, SNPm and PSNR increasing a tenth more than original algorithm. This filter algorithm is verified to be effective by simulation experiment.
