Measurement of the harmonic factor of am signal envelope and of the index of spurious angle modulation with a spectrum analyzer

1988 ◽  
Vol 31 (11) ◽  
pp. 1121-1125 ◽  
Author(s):  
V. A. Belikov ◽  
V. S. Klimashevskii ◽  
P. A. Shpan'on
Keyword(s):  
Spectrum Analyzer ◽  
Signal Envelope ◽  
Harmonic Factor ◽  
Angle Modulation

Minimizing the effect of quasiphase modulation on the harmonic factor of AM signal envelope

1987 ◽  
Vol 30 (9) ◽  
pp. 903-906
Author(s):  
V. A. Belikov ◽  
T. P. Sviridova ◽  
P. A. Shpan'on
Keyword(s):  
Signal Envelope ◽  
Harmonic Factor

scholarly journals Waveguide-Coupled Rydberg Spectrum Analyzer from 0 to 20 GHz

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
David H. Meyer ◽  
Paul D. Kunz ◽  
Kevin C. Cox
Keyword(s):  
Spectrum Analyzer

scholarly journals A Hybridization of Dragonfly Algorithm Optimization and Angle Modulation Mechanism for 0-1 Knapsack Problems

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 598
Author(s):  
Lin Wang ◽  
Ronghua Shi ◽  
Jian Dong
Keyword(s):  
Vertical Displacement ◽  
Continuous Functions ◽  
Convergence Speed ◽  
Knapsack Problems ◽  
Stability And Convergence ◽  
Algorithm Optimization ◽  
Solution Quality ◽  
Dragonfly Algorithm ◽  
Algorithm Stability ◽  
Angle Modulation

The dragonfly algorithm (DA) is a new intelligent algorithm based on the theory of dragonfly foraging and evading predators. DA exhibits excellent performance in solving multimodal continuous functions and engineering problems. To make this algorithm work in the binary space, this paper introduces an angle modulation mechanism on DA (called AMDA) to generate bit strings, that is, to give alternative solutions to binary problems, and uses DA to optimize the coefficients of the trigonometric function. Further, to improve the algorithm stability and convergence speed, an improved AMDA, called IAMDA, is proposed by adding one more coefficient to adjust the vertical displacement of the cosine part of the original generating function. To test the performance of IAMDA and AMDA, 12 zero-one knapsack problems are considered along with 13 classic benchmark functions. Experimental results prove that IAMDA has a superior convergence speed and solution quality as compared to other algorithms.

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