On the estimation of rational transfer functions from samples of the power spectrum

1993 ◽  
Vol 41 (3) ◽  
pp. 1431-1435
Author(s):  
P.M. Baggenstoss ◽  
R. Kumaresan
Keyword(s):  
Power Spectrum ◽  
Transfer Functions ◽  
Rational Transfer

scholarly journals Design of Cascaded and Shifted Fractional-Order Lead Compensators for Plants with Monotonically Increasing Lags

2020 ◽  
Vol 4 (3) ◽  
pp. 37
Author(s):  
Guido Maione
Keyword(s):  
Fractional Order ◽  
Transfer Functions ◽  
Wide Frequency Range ◽  
Frequency Range ◽  
Robust Controller ◽  
Simulation Experiments ◽  
Rational Transfer ◽  
Serial Connection ◽  
Efficiency Performance ◽  
Two Stages

This paper concerns cascaded, shifted, fractional-order, lead compensators made by the serial connection of two stages introducing their respective phase leads in shifted adjacent frequency ranges. Adding up leads in these intervals gives a flat phase in a wide frequency range. Moreover, the simple elements of the cascade can be easily realized by rational transfer functions. On this basis, a method is proposed in order to design a robust controller for a class of benchmark plants that are difficult to compensate due to monotonically increasing lags. The simulation experiments show the efficiency, performance and robustness of the approach.

scholarly journals SYMMETRIC INTERPOLATORY FRAMELETS AND THEIR ERASURE RECOVERY PROPERTIES

2007 ◽  
Vol 05 (04) ◽  
pp. 541-566 ◽  
Author(s):  
OFER AMRANI ◽  
AMIR AVERBUCH ◽  
TAMIR COHEN ◽  
VALERY A. ZHELUDEV
Keyword(s):  
Transfer Functions ◽  
Error Recovery ◽  
Linear Phase ◽  
Recovery Algorithm ◽  
New Class ◽  
Rational Transfer ◽  
Expansion Coefficients ◽  
Redundant Representation ◽  
Frame Expansions ◽  
Recovery Algorithms

A new class of wavelet-type frames in signal space that uses (anti)symmetric waveforms is presented. The construction employs interpolatory filters with rational transfer functions. These filters have linear phase. They are amenable either to fast cascading or parallel recursive implementation. Robust error recovery algorithms are developed by utilizing the redundancy inherent in frame expansions. Experimental results recover images when (as much as) 60% of the expansion coefficients are either lost or corrupted. The proposed approach inflates the size of the image through framelet expansion and multilevel decomposition thus providing redundant representation of the image. Finally, the frame-based error recovery algorithm is compared with a classical coding approach.

scholarly journals Noise Power Spectrum Scene-Dependency in Simulated Image Capture Systems

2020 ◽  
Vol 2020 (9) ◽  
pp. 345-1-345-7
Author(s):  
Edward W. S. Fry ◽  
Sophie Triantaphillidou ◽  
Robin B. Jenkin ◽  
Ralph E. Jacobson ◽  
John R. Jarvis
Keyword(s):  
Power Spectrum ◽  
Transfer Functions ◽  
Noise Power ◽  
Noise Power Spectrum ◽  
Modulation Transfer ◽  
Simulated Image ◽  
Image Capture ◽  
System Noise ◽  
Relative Standard ◽  
Average System

The Noise Power Spectrum (NPS) is a standard measure for image capture system noise. It is derived traditionally from captured uniform luminance patches that are unrepresentative of pictorial scene signals. Many contemporary capture systems apply nonlinear content-aware signal processing, which renders their noise scene-dependent. For scene-dependent systems, measuring the NPS with respect to uniform patch signals fails to characterize with accuracy: i) system noise concerning a given input scene, ii) the average system noise power in real-world applications. The sceneand- process-dependent NPS (SPD-NPS) framework addresses these limitations by measuring temporally varying system noise with respect to any given input signal. In this paper, we examine the scene-dependency of simulated camera pipelines in-depth by deriving SPD-NPSs from fifty test scenes. The pipelines apply either linear or non-linear denoising and sharpening, tuned to optimize output image quality at various opacity levels and exposures. Further, we present the integrated area under the mean of SPD-NPS curves over a representative scene set as an objective system noise metric, and their relative standard deviation area (RSDA) as a metric for system noise scene-dependency. We close by discussing how these metrics can also be computed using scene-and-processdependent Modulation Transfer Functions (SPD-MTF).

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