A transformada de ondeletas
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Neste traballo presentaranse distintas ferramentas matemáticas para o estudo de sináis. Partindo da teoría das series de Fourier, que permite expresar unha función periódica como suma de funcións máis sinxelas (seno e coseno), veremos a súa extensión natural para funcións non necesariamente periódicas, a transformada de Fourier. Analizaremos o problema de que, se ben a transformada de Fourier permite un estudo global das frecuencias presentes nunha función, non é capaz de ubicar en qué momento ten lugar cada unha delas. En base a esto presentaranse dous candidatos a solventar esta problemática: A transformada de Fourier ventaneada e a transformada de ondeletas. Veremos tamén que esta última é máis adecuada en certos casos.
In this work, di erent mathematical tools for the study of signals will be presented. Starting from the theory of Fourier series, which allows expressing a periodic function as a sum of more simple functions (sine and cosine), we will see its natural extension for not necessarily periodic functions, the Fourier transform. We will analyze the problem that although the Fourier transform allows a global study of the frequencies present in a function, it is not able to locate at what moment each one of them has place. Based on this, two candidates will be presented to solve this problem: The windowed Fourier transform and the wavelet transform. We will also see that the latter is more appropriate in certain cases.
In this work, di erent mathematical tools for the study of signals will be presented. Starting from the theory of Fourier series, which allows expressing a periodic function as a sum of more simple functions (sine and cosine), we will see its natural extension for not necessarily periodic functions, the Fourier transform. We will analyze the problem that although the Fourier transform allows a global study of the frequencies present in a function, it is not able to locate at what moment each one of them has place. Based on this, two candidates will be presented to solve this problem: The windowed Fourier transform and the wavelet transform. We will also see that the latter is more appropriate in certain cases.
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