An equilibrium demodulation method used in the mobile communication system includes: carrying on amplitude limitation process for them after sampling data of I (phase inversion) and Q (quadrature) ofbaseband are received by the mobile terminal and further carrying on rotation removel treatment and converting the received multiple data to real number, utilizing the training series kept the storage to carry on related calculation for estimating impact response of channel, conforming the position for the training serial in input series, calculating the mensuration initial value, and then carrying on iteration calculation for obtaining state transfer diagram and utilizing the state transfer diagram to obtain decoding output series.
The invention discloses a pantograph carbon contact strip abrasion detection method based on deep learning target detection. The method comprises the following steps of making an original image data set, and manually calibrating the position of an original image carbon contact strip; clustering the marked bounding boxes in the data set by using an unsupervised learningalgorithm k-means to obtain Anchor Box of which the size and shape meet requirements, and training a Yolo model under a deep learning daknetk framework to obtain a pantograph carbon contact strip positioning model; determining a rectangular region completely including the pantograph carbon contact strip by using the pantograph carbon contact strip positioning model, and intercepting the coordinates of the rectangular region in an original image; and extracting the image edges by using an adaptive threshold edge detectionalgorithm, determining the minimum distance between the upper and lower boundaries of the carbon contact strip by using a projection method, and calculating the thickness of the carbon contact strip. The method can adapt to a complex environment, the positioning rate is improved, and the robustness and the accuracy of a pantograph carbon contact strip abrasion detection algorithm are improved.
The invention discloses an online protocol format inference method based on multiple sequence alignment. Content of a known part of a protocol is marked; for online traffic, captured traffic is grouped according to certain number in an increment analysis mode; for each group, extracting a format of the protocol through progressive multiple sequence alignment; adjacent group results are analyzed; and if analysis results are different, all groups in two continuous groups are analyzed as results, until the analysis is finished. According to the method, an online protocol analysis time demand canbe satisfied, and a protocol analysis effect also can be ensured.