Analysis and Design of Optimized Fractional Order Low-Pass Bessel Filter

2020 ◽  
pp. 2150035 ◽  
Author(s):  
Ashu Soni ◽  
Maneesha Gupta
Keyword(s):  
Sensitivity Analysis ◽  
Fractional Order ◽  
Search Algorithm ◽  
Percentage Error ◽  
Spice Simulation ◽  
Analysis And Design ◽  
First Case ◽  
Low Pass ◽  
Fractional Filter ◽  
Different Order

This paper approximates the behavior of low-pass Bessel filter in fractional domain using the Interior search algorithm (ISA). A detailed analysis has been done with sensitivity analysis, W-plane stability and absolute percentage error (gain and cut-off frequency) to study the low-pass fractional order filter. The work focuses on the analysis and design of fractional filter with two cases. In the first case, the design and simulation of [Formula: see text] order low-pass Bessel filter has been done using MATLAB for [Formula: see text] value ranging from 0.1 to 0.9. SPICE simulation of 1.2, 1.5 and 1.9 order low-pass Bessel filter using fractional order capacitor of order [Formula: see text] with Tow–Thomas biquad have been performed. In the second case, low-pass ([Formula: see text] order Bessel filter has been simulated and designed using MATLAB, where [Formula: see text] and [Formula: see text] are two fractional order elements of a different order. SPICE simulation for different values of [Formula: see text] and [Formula: see text] have been done using two fractional order capacitors of [Formula: see text] and [Formula: see text] order with Tow–Thomas biquad.

scholarly journals Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Markus J. Ankenbrand ◽  
Liliia Shainberg ◽  
Michael Hock ◽  
David Lohr ◽  
Laura M. Schreiber
Keyword(s):  
Neural Networks ◽  
Sensitivity Analysis ◽  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Cardiac Mri ◽  
Magnetic Resonance Images ◽  
Model Interpretation ◽  
First Case ◽  
Segmentation Models

Abstract Background Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance. Results We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model. Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

Optimal design of fractional order low pass Butterworth filter with accurate magnitude response

2018 ◽  
Vol 72 ◽  
pp. 96-114 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Optimal Design ◽  
Fractional Order ◽  
Magnitude Response ◽  
Butterworth Filter ◽  
Low Pass

scholarly journals Fractional-Order Modelling and Optimal Control of Cholera Transmission

2021 ◽  
Vol 5 (4) ◽  
pp. 261
Author(s):  
Silvério Rosa ◽  
Delfim F. M. Torres
Keyword(s):  
Mathematical Model ◽  
Optimal Control ◽  
Sensitivity Analysis ◽  
Fractional Order ◽  
Control Measures ◽  
Time Interval ◽  
Variable Order ◽  
Effectiveness Analysis ◽  
The Cost ◽  
Derivative Order

A Caputo-type fractional-order mathematical model for “metapopulation cholera transmission” was recently proposed in [Chaos Solitons Fractals 117 (2018), 37–49]. A sensitivity analysis of that model is done here to show the accuracy relevance of parameter estimation. Then, a fractional optimal control (FOC) problem is formulated and numerically solved. A cost-effectiveness analysis is performed to assess the relevance of studied control measures. Moreover, such analysis allows us to assess the cost and effectiveness of the control measures during intervention. We conclude that the FOC system is more effective only in part of the time interval. For this reason, we propose a system where the derivative order varies along the time interval, being fractional or classical when more advantageous. Such variable-order fractional model, that we call a FractInt system, shows to be the most effective in the control of the disease.

Gravitational search algorithm for synthesis of selectively thinned concentric ring array antenna with minimum sidelobe level and with fixed and variable first null beamwidth

2014 ◽  
Vol 7 (6) ◽  
pp. 775-781 ◽  
Author(s):  
Anirban Chatterjee ◽  
Gautam Kumar Mahanti ◽  
Narendra Nath Pathak
Keyword(s):  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Synthesis Method ◽  
Computational Time ◽  
Sidelobe Level ◽  
Concentric Ring ◽  
Pattern Synthesis ◽  
First Case ◽  
Gravitational Search ◽  
First Null Beamwidth

Thinning a large concentric ring array by an evolutionary algorithm needs to handle a large amount of variables. The computational time to find out the optimum elements set increases with the increase of array size. Moreover, thinning significantly reduces the directivity of the array. In this paper, the authors propose a pattern synthesis method to reduce the peak sidelobe level (peak SLL) while keeping first null beamwidth (FNBW) of the array fixed by thinning the outermost rings of the array based on Gravitational Search Algorithm (GSA). Two different cases have been studied. In the first case only the outermost ring of the array is thinned and in the second case the two outermost rings are thinned. The FNBW of the optimized array is kept equal to or less than that of a fully populated, uniformly excited and 0.5 λ spaced concentric ring array of same number of elements and rings. The directivity of the optimized array for the above two cases are compared with an array optimized by thinning all the rings, while keeping the design criteria same as the above two cases. The optimized array by thinning the outermost rings gives higher directivity over the optimized array by thinning all the rings. Time required for computing the optimum elements state for the above two cases using GSA are shown lesser compared to the optimized array by thinning all the rings using the same algorithm. The peak SLL and the FNBW of the optimized array for the above two cases are also compared with the optimized array by thinning all the rings.

Improved Electronically-Tunable Current-Mode Square-Root-Domain First-Order Multifunction Filter

2021 ◽  
pp. 2150190
Author(s):  
Manoj Kumar Jain
Keyword(s):  
Transient Response ◽  
Current Mode ◽  
Square Root ◽  
Spice Simulation ◽  
First Order ◽  
Low Pass ◽  
Multifunction Filter ◽  
Simulation Results ◽  
Electronically Tunable ◽  
High Pass

Some time back, Kircay reported an electronically-tunable current-mode square-root-domain first-order filter capable of realizing low-pass (LP), high-pass (HP) and all-pass (AP) filter functions. When simulated in SPICE, Kircay’s circuit has been found to exhibit DC offsets in case of LP and AP responses and incorrect transient response in case of HP response. In this paper, an improved circuit overcoming these difficulties/deficiencies has been suggested and its workability of the improved circuit as well as its capability in meeting the intended objectives has been demonstrated by SPICE simulation results.

An experimental analog circuit realization of Matsuda’s approximate fractional-order integral operators for industrial electronics

2021 ◽  
Author(s):  
Murat Koseoglu ◽  
Furkan Nur Deniz ◽  
Baris Baykant Alagoz ◽  
Ali Yuce ◽  
Nusret Tan
Keyword(s):  
Fractional Order ◽  
Analog Circuit ◽  
Low Cost ◽  
Electric Circuit ◽  
Partial Fraction ◽  
Circuit Realization ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Performance Improvements ◽  
Low Pass

Abstract Analog circuit realization of fractional order (FO) elements is a significant step for the industrialization of FO control systems because of enabling a low-cost, electric circuit realization by means of standard industrial electronics components. This study demonstrates an effective operational amplifier-based analog circuit realization of approximate FO integral elements for industrial electronics. To this end, approximate transfer function models of FO integral elements, which are calculated by using Matsuda’s approximation method, are decomposed into the sum of low-pass filter forms according to the partial fraction expansion. Each partial fraction term is implemented by using low-pass filters and amplifier circuits, and these circuits are combined with a summing amplifier to compose the approximate FO integral circuits. Widely used low-cost industrial electronics components, which are LF347N opamps, resistor and capacitor components, are used to achieve a discrete, easy-to-build analog realization of the approximate FO integral elements. The performance of designed circuit is compared with performance of Krishna’s FO circuit design and performance improvements are shown. The study presents design, performance validation and experimental verification of this straightforward approximate FO integral realization method.

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