Mixed analog/digital, FIR/IIR realization of a linear-phase lowpass filter

1996 ◽  
Vol 31 (9) ◽  
pp. 1347-1350 ◽  
Author(s):  
Qiuting Huang
Keyword(s):  
Linear Phase ◽  
Lowpass Filter

A DESIGN METHODOLOGY FOR TWO-DIMENSIONAL ANALOG FILTERS WITH LINEAR PHASE

1991 ◽  
Vol 01 (01) ◽  
pp. 75-91
Author(s):  
G. GU ◽  
P. MISRA ◽  
B. A. SHENOI
Keyword(s):  
Fourier Series ◽  
Design Methodology ◽  
Analog Filters ◽  
Two Dimensional ◽  
Linear Phase ◽  
Design Technique ◽  
Lowpass Filter ◽  
Analog Filter

A new methodology is proposed for the synthesis of two-dimensional analog filters with linear phase. The Fourier series theory is employed to design a noncausal Laguerre type filter and Hankel approximation machinery is then used to synthesize the causal, stable two-dimensional analog filter with linear phase. The proposed design technique is illustrated by design of a lowpass filter.

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

On strategy for determining displacement solely from perturbed phase of self mixing interferometry

2020 ◽  
Author(s):  
Junaid Khan
Keyword(s):  
Coupling Factor ◽  
Moving Target ◽  
Linear Phase ◽  
Spectral Filtering ◽  
Signal Shape ◽  
Phase Equation ◽  
Sensing Applications ◽  
Computationally Expensive ◽  
Fixed Point Methods ◽  
Factor C

While self mixing interferometry(SMI) has proven to be suitable for displacement measurement and other sensing applications,its characteristic self mixing signal shape is strongly governed by the non-linear phase equation which forms relation between perturbed and unperturbed phase of self mixing laser.Therefore, while it is desirable for robust estimation of displacement of moving target, the algorithms to achieve this must have an objective strategy which can be achieved by understanding the characteristic of extracting knowledge of perturbed phase from unperturbed phase. Therefore, it has been proved and shown that such strategy must not involve sole methods where perturbed phase is continuous function of unperturbed phase (e.g:Taylor series or fixed point methods) or through successive displacements (e.g: variations of Gauss Seidal method). Subset of this strategy is to perform spectral filtering of perturbed phase followed by perturbative or homotopic deformation. A less computationally expensive approach of this strategy is adopted to achieve displacement with mean error of 62.2nm covering all feedback regimes, when coupling factor 'C' is unknown.<br>

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