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Shapiro polynomials

Shapiro polynomials is a science 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 Shapiro polynomials rather than just read about it. In short: In mathematics, the Shapiro polynomials are a sequence of polynomials which were first studied by Harold S. Shapiro in 1951 when considering the magnitude of specific trigonometric sums.

Shapiro polynomials — main illustration
Shapiro polynomials — illustration

Key takeaways

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

Reference excerpt

In mathematics, the Shapiro polynomials are a sequence of polynomials which were first studied by Harold S. Shapiro in 1951 when considering the magnitude of specific trigonometric sums. In signal processing, the Shapiro polynomials have good autocorrelation properties and their values on the unit circle are small. The first few members of the sequence are:

P 1 ( x )

= 1 + x P 2 ( x )

= 1 + x + x 2 − x 3 P 3 ( x )

= 1 + x + x 2 − x 3 + x 4 + x 5 − x 6 + x 7 . . . Q 1 ( x )

= 1 − x Q 2 ( x )

= 1 + x − x 2 + x 3 Q 3 ( x )

= 1 + x + x 2 − x 3 − x 4 − x 5 + x 6 − x 7 . . . {\displaystyle {\begin{aligned}P_{1}(x)&{}=1+x\\P_{2}(x)&{}=1+x+x^{2}-x^{3}\\P_{3}(x)&{}=1+x+x^{2}-x^{3}+x^{4}+x^{5}-x^{6}+x^{7}\\...\\Q_{1}(x)&{}=1-x\\Q_{2}(x)&{}=1+x-x^{2}+x^{3}\\Q_{3}(x)&{}=1+x+x^{2}-x^{3}-x^{4}-x^{5}+x^{6}-x^{7}\\...\\\end{aligned}}}

where the second sequence, indicated by Q, is said to be complementary to the first sequence, indicated by P.

Construction The Shapiro polynomials Pn(z) may be constructed from the Golay–Rudin–Shapiro sequence an, which equals 1 if the number of pairs of consecutive ones in the binary expansion of n is even, and −1 otherwise. Thus a0 = 1, a1 = 1, a2 = 1, a3 = −1, etc. The first Shapiro Pn(z) is the partial sum of order 2n − 1 (where n = 0, 1, 2, ...) of the power series

f(z) := a0 + a1 z + a2 z2 + ... The Golay–Rudin–Shapiro sequence {an} has a fractal-like structure – for example, an = a2n – which implies that the subsequence (a0, a2, a4, ...) replicates the original sequence {an}. This in turn leads to remarkable functional equations satisfied by f(z). The second or complementary Shapiro polynomials Qn(z) may be defined in terms of this sequence, or by the relation Qn(z) = (−1)nz2n−1Pn(−1/z), or by the recursions

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Shapiro polynomials

Start with the simplest possible case. Write down what Shapiro polynomials claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 Shapiro polynomials 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 Shapiro polynomials 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 Shapiro polynomials

In research
Shapiro polynomials appears in science 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 Shapiro polynomials 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
Shapiro polynomials is common in secondary-school and first-year university syllabi. It links to neighbouring topics Digital signal processing, Fourier analysis, Polynomials, so understanding it makes those chapters shorter.
In everyday life
Look for Shapiro polynomials 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 Shapiro polynomials in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Shapiro polynomials 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 Shapiro polynomials out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Shapiro polynomials in simple terms?

In mathematics, the Shapiro polynomials are a sequence of polynomials which were first studied by Harold S. Shapiro in 1951 when considering the magnitude of specific trigonometric sums.

Why does Shapiro polynomials matter?

Because it connects several science 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 Shapiro polynomials?

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 Shapiro polynomials.

Tags

  • Digital signal processing
  • Fourier analysis
  • Polynomials

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