國立虎尾科技大學 |

Machine Learning Techniques for Automatic Modulation Classification.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning Techniques for Automatic Modulation Classification./
作者:	Choubey, Isha.
面頁冊數:	1 online resource (102 pages)
附註:	Source: Masters Abstracts International, Volume: 57-02.
Contained By:	Masters Abstracts International57-02(E).
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9780355540987

Machine Learning Techniques for Automatic Modulation Classification.
Choubey, Isha.

Machine Learning Techniques for Automatic Modulation Classification. - 1 online resource (102 pages)

Source: Masters Abstracts International, Volume: 57-02.

Thesis (M.S.)--San Diego State University, 2017.

Includes bibliographical references

Automatic Modulation Classification (AMC) is concerned with automatically identifying the modulation type of communication signals. AMC is the fundamental component of signal recovery systems and is also employed in jammers in military electronic warfare. Its potential to solve serious issues such as spectral congestion encourages one to develop systems that can quickly and efficiently identify the modulation class of intercepted signals.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355540987Subjects--Topical Terms:

596380
Electrical engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning Techniques for Automatic Modulation Classification.
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100 1 $a Choubey, Isha. $3 1194238
245 1 0 $a Machine Learning Techniques for Automatic Modulation Classification.
264 0 $c 2017
300 $a 1 online resource (102 pages)
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500 $a Source: Masters Abstracts International, Volume: 57-02.
500 $a Adviser: Duy Nguyen.
502 $a Thesis (M.S.)--San Diego State University, 2017.
504 $a Includes bibliographical references
520 $a Automatic Modulation Classification (AMC) is concerned with automatically identifying the modulation type of communication signals. AMC is the fundamental component of signal recovery systems and is also employed in jammers in military electronic warfare. Its potential to solve serious issues such as spectral congestion encourages one to develop systems that can quickly and efficiently identify the modulation class of intercepted signals.
520 $a This thesis is dedicated to classifying digital signals into one of the eight classes: 8-Pulse shift keying (8-PSK), Binary pulse shift keying (BPSK), Continuous-phase frequency-shift keying (CPFSK), Gaussian frequency-shift keying (GFSK), 4-Pulse amplitude modulation (4-PAM), 16-Quadrature amplitude modulation (16-QAM), 64-QAM and Quadrature phase shift keying (QPSK). The classification task has been accomplished via machine learning techniques. The objective is to study and compare various classifiers for identifying the class of a digitally modulated signal.
520 $a Machine learning classifiers k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forests and Artificial Neural Networks were implemented. The classifiers were trained to perform the task of AMC and their performances were examined and compared with each other. Manual feature engineering was done to train the classifiers. An alternate solution to feature engineering was presented in the form of feature learning from raw data.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a San Diego State University. $b Electrical Engineering. $3 1182874
773 0 $t Masters Abstracts International $g 57-02(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10689637 $z click for full text (PQDT)