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Mathieu wavelet

Mathieu wavelet is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Mathieu wavelet rather than just read about it. In short: The Mathieu equation is a linear second-order differential equation with periodic coefficients. The French mathematician, E.

Mathieu wavelet — main illustration
Mathieu wavelet — illustration

Key takeaways

  • Mathieu wavelet belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Mathieu wavelet to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Mathieu wavelet from memory before moving on to harder problems.

Reference excerpt

The Mathieu equation is a linear second-order differential equation with periodic coefficients. The French mathematician, E. Léonard Mathieu, first introduced this family of differential equations, nowadays termed Mathieu equations, in his “Memoir on vibrations of an elliptic membrane” in 1868. "Mathieu functions are applicable to a wide variety of physical phenomena, e.g., diffraction, amplitude distortion, inverted pendulum, stability of a floating body, radio frequency quadrupole, and vibration in a medium with modulated density"

Elliptic-cylinder wavelets This is a wide family of wavelet system that provides a multiresolution analysis. The magnitude of the detail and smoothing filters corresponds to first-kind Mathieu functions with odd characteristic exponent. The number of notches of these filters can be easily designed by choosing the characteristic exponent. Elliptic-cylinder wavelets derived by this method possess potential application in the fields of optics and electromagnetism due to its symmetry.

Mathieu differential equations Mathieu's equation is related to the wave equation for the elliptic cylinder. In 1868, the French mathematician Émile Léonard Mathieu introduced a family of differential equations nowadays termed Mathieu equations. Given a ∈ R , q ∈ C {\displaystyle a\in \mathbb {R} ,q\in \mathbb {C} } , the Mathieu equation is given by

d 2 y d w 2 + ( a − 2 q cos ⁡ 2 w ) y = 0. {\displaystyle {\frac {d^{2}y}{dw^{2}}}+(a-2q\cos 2w)y=0.}

The Mathieu equation is a linear second-order differential equation with periodic coefficients. For q = 0, it reduces to the well-known harmonic oscillator, a being the square of the frequency. The solution of the Mathieu equation is the elliptic-cylinder harmonic, known as Mathieu functions. They have long been applied on a broad scope of wave-guide problems involving elliptical geometry, including:

analysis for weak guiding for step index elliptical core optical fibres power transport of elliptical wave guides evaluating radiated waves of elliptical horn antennas elliptical annular microstrip antennas with arbitrary eccentricity ν {\displaystyle \nu } ) scattering by a coated strip.

Mathieu functions: cosine-elliptic and sine-elliptic functions In general, the solutions of Mathieu equation are not periodic. However, for a given q, periodic solutions exist for infinitely many special values (eigenvalues) of a. For several physically relevant solutions y must be periodic of period π {\displaystyle \pi } or 2 π {\displaystyle 2\pi } . It is convenient to distinguish even and odd periodic solutions, which are termed Mathieu functions of first kind. One of four simpler types can be considered: Periodic solution ( π {\displaystyle \pi } or 2 π {\displaystyle 2\pi } ) symmetry (even or odd). For q ≠ 0 {\displaystyle q\neq 0} , the only periodic solutions y corresponding to any characteristic value a = a r ( q ) {\displaystyle a=a_{r}(q)} or a = b r ( q ) {\displaystyle a=b_{r}(q)} have the following notations: ce and se are abbreviations for cosine-elliptic and sine-elliptic, respectively.

Even periodic solution: c e r ( ω , q ) = ∑ m A r , m cos ⁡ m ω for a = a r ( q ) {\displaystyle ce_{r}(\omega ,q)=\sum _{m}A_{r,m}\cos {m\omega }{\text{ for }}a=a_{r}(q)}

Odd periodic solution: s e r ( ω , q ) = ∑ m A r , m sin ⁡ m ω for a = b r ( q ) {\displaystyle se_{r}(\omega ,q)=\sum _{m}A_{r,m}\sin {m\omega }{\text{ for }}a=b_{r}(q)}

where the sums are taken over even (respectively odd) values of m if the period of y is π {\displaystyle \pi } (respectively 2 π {\displaystyle 2\pi } ). Given r, we denote henceforth A r , m {\displaystyle A_{r,m}} by A m {\displaystyle A_{m}} , for short. Interesting relationships are found when q → 0 {\displaystyle q\to 0} , r ≠ 0 {\displaystyle r\neq 0} :

… excerpt ends here. Continue reading the full article.

Illustrations

Mathieu wavelet: Figure 2 - Magnitude of the transfer function for Mathieu multiresolution analysis filters. (smoothing filter 
  
    
      
        
          H
          
            ν
          
        
        (
        ω
        )
      
    
    {\displaystyle H_{\nu }(\omega )}
  
 and detail filter 
  
    
      
        
          G
          
            ν
          
        
        (
        ω
        )
      
    
    {\displaystyle G_{\nu }(\omega )}
  
 for a few Mathieu parameters.) (a) 
  
    
      
        ν
        =
        1
      
    
    {\displaystyle \nu =1}
  
, q=5, a = 1.85818754...;  (b) 
  
    
      
        ν
        =
        1
      
    
    {\displaystyle \nu =1}
  
, q = 10, a = −2.3991424...; (c) 
  
    
      
        ν
        =
        5
      
    
    {\displaystyle \nu =5}
  
, q = 10, a = 25.5499717...; (d) 
  
    
      
        ν
        =
        5
      
    
    {\displaystyle \nu =5}
  
, q = 10, a = 27.70376873...
Figure 2 - Magnitude of the transfer function for Mathieu multiresolution analysis filters. (smoothing filter H ν ( ω ) {\displaystyle H_{\nu }(\omega )} and detail filter G ν ( ω ) {\displaystyle G_{\nu }(\omega )} for a few Mathieu parameters.) (a) ν = 1 {\displaystyle \nu =1} , q=5, a = 1.85818754...; (b) ν = 1 {\displaystyle \nu =1} , q = 10, a = −2.3991424...; (c) ν = 5 {\displaystyle \nu =5} , q = 10, a = 25.5499717...; (d) ν = 5 {\displaystyle \nu =5} , q = 10, a = 27.70376873...
Mathieu wavelet: Figure 3: FIR-based approximation of Mathieu wavelets. Filter coefficients holding h < 10−10 were thrown away (20 retained coefficients per filter in both cases.) (a) Mathieu Wavelet with ν = 5 and q = 5 and (b) Mathieu wavelet with ν = 1 and q = 5.
Figure 3: FIR-based approximation of Mathieu wavelets. Filter coefficients holding h < 10−10 were thrown away (20 retained coefficients per filter in both cases.) (a) Mathieu Wavelet with ν = 5 and q = 5 and (b) Mathieu wavelet with ν = 1 and q = 5.

Worked examples

Example 1 — a first encounter with Mathieu wavelet

Start with the simplest possible case. Write down what Mathieu wavelet claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Mathieu wavelet before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Mathieu wavelet ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Mathieu wavelet

In research
Mathieu wavelet appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Mathieu wavelet in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Mathieu wavelet is common in secondary-school and first-year university syllabi. It links to neighbouring topics Wavelets, so understanding it makes those chapters shorter.
In everyday life
Look for Mathieu wavelet outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Mathieu wavelet in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Mathieu wavelet means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Mathieu wavelet out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Mathieu wavelet in simple terms?

The Mathieu equation is a linear second-order differential equation with periodic coefficients. The French mathematician, E.

Why does Mathieu wavelet matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Mathieu wavelet?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Mathieu wavelet.

Tags

  • Wavelets

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