{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T17:23:57Z","timestamp":1771003437066,"version":"3.50.1"},"reference-count":18,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,12,19]]},"abstract":"<jats:p>In order to improve the accuracy, efficiency and network throughput of multi-channel weak signal synchronous acquisition in the network, a multi-channel weak signal synchronous acquisition method in remote communication network is designed. Firstly, by analyzing the multi-channel structure of remote communication network, the interference factors of multi-channel weak signal acquisition are determined. The parameter model method is used to estimate the bispectrum of weak signals, complete the multi-channel weak signal extraction of remote communication network, and preprocess the multi-channel weak signals of remote communication network by average filtering method. On this basis, the characteristics of multi-channel weak signals in the remote communication network are judged, and their characteristics are changed through the short time window function in the time domain, and the multi-channel weak signal synchronous catcher in the remote communication network is constructed to realize the synchronous acquisition of multi-channel weak signals in the remote communication network. The experimental results show that this method has high accuracy, short time-consuming and good network throughput. The acquisition accuracy of this method is always maintained at more than 90%.<\/jats:p>","DOI":"10.3233\/jcm-226397","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T11:29:59Z","timestamp":1662118199000},"page":"2135-2148","source":"Crossref","is-referenced-by-count":0,"title":["Synchronous capture method of multi-channel weak signal in long-distance communication network"],"prefix":"10.1177","volume":"22","author":[{"given":"Yuanyuan","family":"Wang","sequence":"first","affiliation":[]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/JCM-226397_ref1","doi-asserted-by":"crossref","first-page":"3571","DOI":"10.1007\/s11227-020-03410-y","article-title":"Network intrusion detection using multi-architectural modular deep neural network","volume":"77","author":"Atefinia","year":"2021","journal-title":"J Supercomput."},{"issue":"67","key":"10.3233\/JCM-226397_ref2","first-page":"36","article-title":"Power information network intrusion detection 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