第一篇：
Accession number: 20104313316802
Title: Detecting and extracting vibration disturb in IMU testing in field
Authors: Gang, Liu1 ; Jie, Yang1 ; Lixin, Wang1 ; Jiang, Gao2 ; Xiaomei, Wang2
Author affiliation: 1 Xi'an Hongqing Research Institute of Hi-Tech, Xi'an, Shaanxi Province, China
2 PLA Sergeant College of the Second Artillery, Qinzhou, China
Corresponding author: Gang, L. (yangjieflying@126.com)
Source title: ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings
Abbreviated source title: ICCET - Int. Conf. Comput. Eng. Technol., Proc.
Volume: 2
Monograph title: ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings
Issue date: 2010
Publication year: 2010
Pages: V2516-V2518
Article number: 5485585
Language: English
ISBN-13: 9781424463503
Document type: Conference article (CA)
Conference name: 2010 2nd International Conference on Computer Engineering and Technology, ICCET 2010
Conference date: April 16, 2010 - April 18, 2010
Conference location: Chengdu, China
Conference code: 81865
Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
Abstract: IMU was often interrupted by the vibration of ground when it was calibrated in the field, the vibration disturb would cause great influence to the calibrated precision of IMU. This paper first established the mathematic model of vibration signal, then analyzed the output signal of IMU by using wavelet modulus maximum method, finally deduced a arithmetic to restrain the noise disturb and distill the vibration. Simulation shows that this arithmetic had very good detection ability. © 2010 IEEE.
Number of references: 6
Main heading: Vibration analysis
Controlled terms: Calibration - Signal detection
Uncontrolled terms: Detection ability - IMU calibration - In-field - Mathematic model - Modulus maxima - Output signal - Vibration disturb - Vibration signal - Wavelet - Wavelet modulus maxima
Classification code: 716.1 Information Theory and Signal Processing - 941 Acoustical and Optical Measuring Instruments - 942 Electric and Electronic Measuring Instruments - 943 Mechanical and Miscellaneous Measuring Instruments - 943.2 Mechanical Variables Measurements - 944 Moisture, Pressure and Temperature, and Radiation Measuring Instruments
DOI: 10.1109/ICCET.2010.5485585
Database: Compendex
Compilation and indexing terms, © 2010 Elsevier Inc.
第二篇：
Accession number: 20104313316796
Title: Modulation recognition of communication signal based on wavelet RBF neural network
Authors: He, Bing1 ; Liu, Gang1 ; Cun, Ge2 ; Jiang, Gao2
Author affiliation: 1 Xi'an Hongqing Research Institute of Hi-Tech, Xi'an, Shaanxi Province, China
2 Second Artillery Petty Officer School, QingZhou, Shandong Province, China
Corresponding author: He, B. (hb830513@126.com)
Source title: ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings
Abbreviated source title: ICCET - Int. Conf. Comput. Eng. Technol., Proc.
Volume: 2
Monograph title: ICCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings
Issue date: 2010
Publication year: 2010
Pages: V2490-V2492
Article number: 5485567
Language: English
ISBN-13: 9781424463503
Document type: Conference article (CA)
Conference name: 2010 2nd International Conference on Computer Engineering and Technology, ICCET 2010
Conference date: April 16, 2010 - April 18, 2010
Conference location: Chengdu, China
Conference code: 81865
Publisher: IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
Abstract: Modulation recognition of communication signal is to confirm the modulation style of communication signal in the condition with much noise. Wavelet transformation has a good localization characteristic in time-frequency domain, while the neural network has characteristics of self-studying, self-adaptation, and high stabilization and can improve the autoimmunization and intelligence of recognition. We adopted the ideal of combination of wavelet and neural network in the paper, firstly, we used the wavelet to decompose the signal, and then abstracted the characteristic through the wavelet coefficient, lastly we adopted the RBF(Radial Basis Funtion) nerual network to recognize 4 kinds of common digital communication signal. The simulation results indicate that the presented method performs well. © 2010 IEEE.
Number of references: 8
Main heading: Neural networks
Controlled terms: Amplitude modulation - Communication - Digital communication systems - Radial basis function networks - Signal processing - Wavelet transforms
Uncontrolled terms: Communication signals - Digital communication signals - Modulation recognition - Radial basis - RBF Neural Network - Self adaptation - Simulation result - Time frequency domain - Wavelet coefficients - Wavelet transformations
Classification code: 716 Telecommunication; Radar, Radio and Television - 716.1 Information Theory and Signal Processing - 723.4 Artificial Intelligence - 921.3 Mathematical Transformations
DOI: 10.1109/ICCET.2010.5485567
Database: Compendex
Compilation and indexing terms, © 2010 Elsevier Inc.
