Modulation recognition network of multi-scale analysis with deep threshold noise elimination

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.

A Material Identification Approach Based on Wi-Fi Signal

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.

Study on the variation characteristics of the geoelectric field preceding earthquakes

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.