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Hyperbolic Tangent Filter in Signal ProcessingTRANSCRIPT
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 1 of 10
Hyperbolic Tangent Filter in Signal Processing
CLAUDE ZIAD BAYEH1, 2
1Faculty of Engineering II, Lebanese University 2EGRDI transaction on mathematics (2012)
LEBANON Email: [email protected]
Abstract: - The Hyperbolic tangent Filter is an original method introduced by the author in signal processing in 2012; it is a filter type in the Amplitude-oriented design of filters. It is more similar to the Butterworth filter but it uses the function Hyperbolic Tangent for frequencies instead of the polynomial โwโ frequency. It has many advantages over the traditional filters such as -It has no ripples, similar to the Butterworth filter. -It attenuates faster than other existing filters for the same order (compared to Butterworth, Chebyshev, Elliptic Filterโฆ). -It is more flexible than other filters by varying some parameters. -It is an excellent attenuator for unneeded frequencies and excellent conservator for the needed frequencies. Briefly, a comparative study is presented in this paper to compare the traditional filters such as Butterworth and Chebyshev with the new proposed filter Hyperbolic Tangent Filter. Key-words:- Filters, Analog filters, Digital filters, Signal processing, Mathematics, Amplitude oriented design.
1. Introduction In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature [1-3]. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal [6-7]. Most often, this means removing some frequencies and not others in order to suppress interfering signals and reduce background noise. However, filters do not exclusively act in the frequency domain; especially in the field of image processing many other targets for filtering exist [10-12]. The drawback of filtering is the loss of information associated with it. Signal combination in Fourier space is an alternative approach for removal of certain frequencies from the recorded signal. There are many different bases of classifying filters and these overlap in many different ways; there is no simple hierarchical classification. Filters may be: โข analog or digital โข discrete-time (sampled) or continuous-time โข linear or non-linear โข Time-invariant or time-variant, also known as shift invariance. If the filter operates in a spatial
domain then the characterization is space invariance. โข passive or active type of continuous-time filter โข Infinite impulse response (IIR) or finite impulse response (FIR) type of discrete-time or digital
filter.
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 2 of 10
In fact, many scientists introduced many filters in order to filter the signal, keep the needed frequencies and attenuate the unneeded frequencies. The more important thing is the implementation of these mathematical expressions in electronics and the design of analog and digital circuits. The important filters are Butterworth, Chebyshev 1, Chebyshev 2, and Elliptic filter. In this paper, the author introduced a new filter design (amplitude oriented design) in signal processing in order to increase the efficiency of filtering signals. The name of this filter is โHyperbolic Tangent Filterโ. This filter has many advantages over previous ones, and these advantages are discussed in this paper. In the second section, a brief review of ideal filter is presented. In section 3, Amplitude oriented design is discussed with some examples of filters such as Butterworth and Chebyshev filters. In section 4, the author proposed a new filter based on Hyperbolic Tangent function. And finally in the section 5, a conclusion is presented. 2. Brief review of the Ideal Filter In this section, we are going to see a brief introduction to the Ideal Filter [1]. Letโs consider a system, as shown in figure 1 whose input is ๐๐(๐ก๐ก) and output is ๐๐(๐ก๐ก). With the original notation of the Fourier transform we have ๐๐(๐ก๐ก) โ ๐น๐น(๐๐๐๐) (1) And ๐๐(๐ก๐ก) โ ๐บ๐บ(๐๐๐๐) (2) The transfer function of the system is: ๐ป๐ป(๐๐๐๐) = ๐บ๐บ(๐๐๐๐ )
๐น๐น(๐๐๐๐ )= |๐ป๐ป(๐๐๐๐)| โ ๐๐๐๐๐๐ (๐๐) (3)
Where |๐ป๐ป(๐๐๐๐)| is the amplitude response and ๐๐(๐๐) is the phase response. The system is called an ideal filter if its amplitude is constant within certain frequency bands, and exactly zero outside these bands.
Fig. 1: A linear system with input ๐๐(๐ก๐ก) and output ๐๐(๐ก๐ก)
In addition, in the bands where the amplitude is constant, the phase is a linear function of ๐๐. The amplitude response of the ideal filter is shown in figure 2 for the four main types of low-pass, high-pass, band-pass and band-stop filters.
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 3 of 10
Fig. 2: The ideal filter amplitude characteristics
The ideal phase response for the low-pass case is shown in figure 3 and is given by ๐๐(๐๐) = โ๐๐๐๐ with |๐๐| โค ๐๐0 Where ๐๐ is a constant.
Fig. 3: The ideal low-pass filter phase characteristics
To appreciate why these are the desirable ideal characteristics, letโs consider the low-pass case described by the transfer function
๐ป๐ป(๐๐๐๐) = ๏ฟฝ๐๐(โ๐๐๐๐๐๐ ) ๐๐๐๐๐๐ 0 โค |๐๐| โค ๐๐00 ๐๐๐๐๐๐ |๐๐| > ๐๐0
๏ฟฝ (4)
Then ๐บ๐บ(๐๐๐๐) = ๐ป๐ป(๐๐๐๐) โ ๐น๐น(๐๐๐๐) So we have for 0 โค |๐๐| โค ๐๐0 ๐บ๐บ(๐๐๐๐) = ๐๐(โ๐๐๐๐๐๐ ) โ ๐น๐น(๐๐๐๐) (5) The inverse Fourier transform of the equation (5) is ๐๐(๐ก๐ก) = ๐๐(๐ก๐ก โ ๐๐) (6) It means that the output is an exact replica of the input, but delayed by a constant time value ๐๐. It means that any input signal with spectrum lying within the pass-band of the ideal filter will be
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 4 of 10
transmitted without attenuation and without distortion in its phase spectrum; the signal is simply delayed by a constant time value. If we take the inverse Fourier transform of ๐ป๐ป(๐๐๐๐), we obtain the impulse response of the ideal filter as โ(๐ก๐ก) = ๐น๐นโ1{๐ป๐ป(๐๐๐๐)} = sin(๐๐0(๐ก๐กโ๐๐))
๐๐(๐ก๐กโ๐๐) (7)
which is a cardinal sine function (๐ ๐ ๐ ๐ ๐ ๐ ๐ ๐ ) and clearly exists for negative values of time, so that the required system is non-causal, which means that it is unrealizable by physical components. So in conclusion, the ideal filter canโt be realizable for the instance. Therefore, many scientists have introduced their own methods to resolve this problem, and the following section will introduce briefly the main methods used to implement realizable filters. 3. Amplitude-oriented design We now discuss the amplitude approximation problem. This consists in finding a realizable magnitude function |๐ป๐ป(๐๐๐๐)| or |๐ป๐ป(๐๐๐๐)|2 which is capable of meeting arbitrary specifications on the amplitude response of the filter. The discussion will be on the design of low-pass filters because the others are deduced from the low-pass filters. The low-pass approximation problem is to determine |๐ป๐ป(๐๐๐๐)|2 such that the typical specifications shown in figure 4 are met.
Fig. 4: Tolerance schemes for amplitude-oriented filter design, magnitude-squared (in the left) and
attenuation (in the right) The frequency ๐๐0 is called the pass-band edge or cut-off frequency while ๐๐๐ ๐ is referred to as the stopband edge. The amplitude-squared function of the filter may be written as |๐ป๐ป(๐๐๐๐)|2 = โ ๐๐๐๐๐๐2๐๐๐๐
๐๐=01+โ ๐๐๐๐๐๐2๐๐๐ ๐
๐๐=1 (8)
The problem may be posed as one of determining the coefficients ๐๐๐๐ and ๐๐๐๐ such that the above function is capable of meeting an arbitrary set of specifications. In the following subsections we are going to see the existing filters such as Butterworth filter and Chebyshev filter. 3.1. Maximally Flat Response (Filter) It is also called Butterworth response.
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 5 of 10
Fig. 5: General appearance of the maximally flat amplitude response for the Butterworth Filter
This is obtained by forcing the maximum possible number of derivatives of |๐ป๐ป(๐๐๐๐)|2, with respect to ๐๐, to vanish at ๐๐ = 0 and ๐๐ = โ. So the Butterworth filter takes the following form: |๐ป๐ป(๐๐๐๐)|2 = 1
1+๐๐2๐ ๐ (9) With ๐ ๐ is an integer and ๐ ๐ โ โ, it is called the degree of the filter and the 3dB occurs at ๐๐ = 1 for all ๐ ๐ 3.2. Chebyshev Response For the same degree ๐ ๐ as the Butterworth Filter, a considerable improvement in the rate of cut-off, over the Butterworth response, results if we require |๐ป๐ป(๐๐๐๐)|2 to be equiripple in the passband while retaining the maximally flat response in the stopband. The function takes the following form: |๐ป๐ป(๐๐๐๐)|2 = 1
1+๐๐2๐๐๐ ๐ 2(๐๐) (10)
Where ๐๐๐ ๐ (๐๐) is chosen to be an odd or even polynomial which oscillates between -1 and +1 the maximum number of times in the passband |๐๐| โค 1 and is monotonically increasing outside the interval. The size of the oscillations or ripple can be controlled by a suitable choice of the parameter ๐๐. The polynomial ๐๐๐ ๐ (๐๐) which leads to these desired properties is the Chebyshev polynomial of the first kind defined by:
๐๐๐ ๐ (๐๐) = ๏ฟฝcos(๐ ๐ โ ๐ ๐ ๐๐๐ ๐ โ1(๐๐)) ๐๐๐๐๐๐ 0 โค ๐๐ โค 1cosh(๐ ๐ โ ๐ ๐ ๐๐๐ ๐ โโ1(๐๐)) ๐๐๐๐๐๐ |๐๐| > 1
๏ฟฝ (11)
Using the recurrence formula, we obtain, ๐๐๐ ๐ +1(๐๐) = 2๐๐๐๐๐ ๐ (๐๐) โ ๐๐๐ ๐ โ1(๐๐) (12) With ๐๐0(๐๐) = 1, ๐๐1(๐๐) = ๐๐
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 6 of 10
Fig. 6: Chebyshev response for ๐ ๐ = 3 and ๐๐ = 0.5
The Chebyshev approximation is known to be the optimum solution to the problem of determining an |๐ป๐ป(๐๐๐๐)|2 which is constrained to lie in a band for 0 โค |๐๐| โค 1 and attain the maximum value for all ๐๐ in the range 1 < ๐๐ < โ for a given degree ๐ ๐ . 4. Proposed Filter by the Author (Hyperbolic Tangent Filter) This proposed filter by the author works mainly as low-pass filter. The high-pass filter, band-pass filter and band-stop filter can be deduced from the low-pass filter by varying some parameters as shown in section 4.2. Its advantages are presented in the section 4.3. 4.1. Hyperbolic Tangent Filter-Low pass filter The expression of the filter for a causal system is: ๐๐๐๐๐ ๐ โ๐น๐น(๐๐) = ๏ฟฝ1 โ tanh(๐๐ โ ๐๐2๐ ๐ )2๐๐ (13) With - ๐๐ > 0 and ๐๐ โ โ+, for unitary filter we take it equal to 1. - ๐ ๐ โ โโ is the order of the filter - ๐๐ โ โโ is the sharpness of the filter In the following figure 7, different orders and different shapes are formed. To form a low-pass filter, we shall take the following parameters: - ๐๐ = 1 - ๐ ๐ โ โ is the order of the filter - ๐๐ = 1
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 7 of 10
a) ๐ ๐ = 2
b) ๐ ๐ = 4
Fig. 7: Different forms formed by changing the order of the filter (๐ ๐ ). 4.2. Hyperbolic Tangent Filter, other types of filters -For the high-pass filter we change the value of ๐๐ to be equal to ๐๐
๐๐.
-For the band-stop filter we change the value of ๐๐ to be equal to 1
๐ฝ๐ฝ๏ฟฝ๐๐๐๐โ ๐๐๐๐๏ฟฝ.
-For the band-pass filter we change the value of ๐๐ to be equal to ๐ฝ๐ฝ ๏ฟฝ๐๐
๐๐โ ๐๐
๐๐๏ฟฝ.
With ๐ฝ๐ฝ and ๐๐ > 0 4.3. Advantages of the Hyperbolic Tangent Filter -It has no ripples, similar to the Butterworth filter. -It attenuates faster than other existing filters for the same order (compared to Butterworth, Chebyshev, Elliptic Filterโฆ). Refer to figures 8 through 10. -It is more flexible than other filters by varying some parameters. -It is an excellent attenuator for unneeded frequencies and excellent conservator for the needed frequencies.
Fig. 8: Comparison between the proposed filter Hyperbolic Tangent Filter (Black color), Chebyshev
Filter (Blue color) and Butterworth (Red color) for the same Order (๐ ๐ = 2).
The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 8 of 10
Fig. 9: Comparison between the proposed filter Hyperbolic Tangent Filter (Black color), Chebyshev
Filter (Blue color) and Butterworth (Red color) for the same Order (๐ ๐ = 3).
Fig. 10: Comparison between the proposed filter Hyperbolic Tangent Filter with ๐๐ = 3 (Black color),
Chebyshev Filter (Blue color) and Butterworth (Red color) for the same Order (๐ ๐ = 3). In conclusion, we can remark the importance of the Hyperbolic Tangent Filter and compare it with other types of filters. The attenuation of Hyperbolic Tangent Filter is much more important than other types of filters such as Butterworth, Chebyshev, Elliptic filterโฆ 5. Conclusion In this paper, the author introduced a new method in signal processing. A new filter is defined named โHyperbolic Tangent Filterโ. This filter has many advantages over the traditional filters as discussed in the previous sections. In the section 2, a brief introduction about the ideal filter is discussed; the ideal filter is not applicable as it is a non-causal system. In the section 3, some important filters are presented such as Butterworth and Chebyshev filters, these types of filters can be realized using electronic components such as resistors, capacitances, inductors and semiconductors. In the section 4, the author proposed a new filter based on Amplitude oriented design. Some advantages are discussed and finally some pictures are presented to compare three filters with the same order which are: Butterworth, Chebyshev and the proposed filter Hyperbolic Tangent Filter. References: [1] Hussein Baher, โSignal processing and integrated circuitsโ, Published by John Wiley & Sons Ltd., ISBN:
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The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 9 of 10
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The General Science Journal Claude Ziad BAYEH
ISSN: 1916-5382 Page 10 of 10
[33] Cyril W. Lander, Power electronics, third edition, McGraw-Hill Education, 1993. [34] Claude Bayeh, โIntroduction to the Rectangular Trigonometry in Euclidian 2D-Spaceโ, WSEAS
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[37] Claude Bayeh, โApplication of the Elliptical Trigonometry in industrial electronic systems with analyzing, modeling and simulating two functions Elliptic Mar and Elliptic Jes-xโ, WSEAS Transactions on Circuits and Systems, ISSN: 1109-2734, Issue 11, Volume 8, November 2009, pp. 843-852.
[38] Claude Bayeh, โA survey on the application of the Elliptical Trigonometry in industrial electronic systems using controlled waveforms with modeling and simulating of two functions Elliptic Mar and Elliptic Jes-xโ, in the book โ Latest Trends on Circuits, Systems and Signalsโ, publisher WSEAS Press, ISBN: 978-960-474-208-0, ISSN: 1792-4324, July 2010, pp.96-108.
About the author Claude Ziad Bayeh (or El-Bayeh), (born in 11 September 1982-) is a Lebanese Scientist and Researcher, he holds the following degrees: - PhD in Electronics and Signal Processing from (Princely International University)-USA-2012. - PhD in Physics and Relativity from (Princely International University)-USA-2013. - Currently PhD in Electrical Engineering from (Universidad Empresarial de Costa Rica)-Costa Rica. - Master in Electrical and Electronic Engineering from (Lebanese University Faculty of Engineering II)-Lebanon-2008. - Master in Organizational Management from (Quebec University UQAC)-Canada-2012. He has published numerous international papers in Mathematics, Engineering, Physics, Management and Chemistry (more than 100 papers). The most published papers are considered as revolutionary papers in their fields. You can find a brief history using the link: http://www.linkedin.com/pub/claude-ziad-bayeh/34/9b9/ab6 For any additional information, any question or suggestions, please donโt hesitate to contact the author.