Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

InTech eBooks

A Reformulative Retouch on the Fourier Transform – “Unprincipled” Uncertainty Principle

Csaba Szántay, Jr.

Published Apr 11, 2012• 1 citation
Publisher

Abstract

According to a widespread notion, a monochromatic harmonic temporal wave’s frequency ω becomes inherently “uncertain” to a degree ω Δ if the wave lasts for only a limited time t Δ owing to the “Uncertainty Principle” which states that constant ≥ ω Δ ⋅ Δt (i.e. ω Δ cannot be zero if t Δ is finite). This frequency uncertainty is commonly thought of as also being reflected in the fact that the (“sinc”-shaped) frequency spectrum obtained by Fouriertransforming a finite-duration monochromatic wave spreads over a nonzero frequency range. Since, as often argued, the Fourier spectrum represents the ”frequency components” of a temporal function (or signal), the nonzero width of the spectrum tells us that the ”nominally” monochromatic ω frequency of a time-limited sinusoid is “effectively” polychromatic, with frequencies continuously distributed over a ω Δ range in accord with the Uncertainty Principle.

DOI
10.5772/35832

Related papers

NMR and the uncertainty principle: How to and how not to interpret homogeneous line broadening and pulse nonselectivity. III. Uncertainty?

Csaba Szántay

Abstract Following the treatments presented in Parts I and II, I herein discuss in more detail the popular notion that the frequency of a monochromatic RF pulse as well as that of a monochromatic FID is “in effect” uncertain due to the (Heisenberg) Uncertainty Principle, which also manifests itself in the fact that the FT‐spectrum of these temporal entities is spread over a nonzero frequency band. In Part III, I continue my preliminary review of some further fundamental concepts, such as the Heisenberg and Fourier Uncertainty Principles, that are needed to understand whether or not the NMR linewidth and the RF excitation bandwidth have anything to do with “uncertainty”. The article then culminates in re‐addressing our Two NMR Problems in a more conscientious frame of mind by using a more refined formalism. The correct interpretation of these problems will be discussed in Part IV. © 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part A 32A: 302–325, 2008.

Concepts in Magnetic Resonance Part A•Jul 1, 2008•18 citations

The Heisenberg Uncertainty Principle and Time-Frequency Decompositions

Barbara Burke Hubbard

It is sometimes said that one can't localize a signal simultaneously in time and frequency. This can be misleading. It's not that we can't localize the signal in time and frequency. The signal itself can't be concentrated simultaneously in time and in frequency. The Heisenberg uncertainty principle does not limit what we can know about reality; it describes that reality. The shorter-lived a function, the wider the band of frequencies given by its Fourier transform; the narrower the band of frequencies of its Fourier transform, the more the function is spread out in time.

A K Peters/CRC Press eBooks•May 30, 1998•0 citations

The Uncertainty Principle for the Short-Time Fourier Transform and Wavelet Transform

Leon W. Cohen

We obtain both a global and local uncertainty principle for the short-time Fourier transform. Explicit expressions are obtained for the uncertainty product in terms of the uncertainty product of the signal and window. Although the uncertainty product of the spectrogram is not the sum of the uncertainty products of the window and signal, nonetheless, we show that the global uncertainty product has to be greater than one. We also derive local uncertainty relations which indicate how the local spread in time and frequency are related at a particlar time–frequency point. In addition, we obtain general results for the time, scale, and frequency moments of the scalogram in terms of the moments of the signal and mother wavelet. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Birkhäuser Boston eBooks•Jan 1, 2001•29 citations

A physical model of Fourier uncertainty principle

Rohan Singh, Parbati Biswas

Abstract Fourier transform is an integral part of harmonic analysis, information theory and signal processing with wide applications. The transformation between the Fourier pair is governed by an uncertainty relation, most generally captured by the Bandwidth theorem, . The usual approaches to the uncertainty principle employ sophisticated methods which lack physical interpretation of the origin and description of the uncertainty for real systems. In this paper, we have proposed a physically inspired model of the Fourier transform with an aim to describe its underlying dynamics and how it leads to the fundamental uncertainty principle. A signal is modeled as helical spring in 3D space, which is wrapped around a circle with controllable wrapping frequency. This process is formulated to reproduce the Fourier transform. The model is used to describe the origin of uncertainty, and to formulate a new generator-based approach to the Bandwidth theorem.

Europhysics Letters (EPL)•Jun 17, 2025•0 citations

Fourier uncertainty principles

A mathematical uncertainty principle is an inequality or uniqueness theorem concerning the joint localization of a function or system and its spectrum. A Fourier uncertainty principle is thus a statement that a function and its Fourier transform cannot both decay too rapidly. The most familiar form is the Wiener-Heisenberg inequality, 5.1 $$ \left\| {(x - x_0 )f(x)} \right\|_2 \left\| {(\xi - \xi _0 )\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{f} (\xi )} \right\|_2 \geqslant \frac{{\mathop {\left\| f \right\|}\nolimits_2^2 }} {{4\pi }},$$ which becomes an equality only for multiples of Gaussians of the form exp(−πα(x − x0)2 + 2πiξ0x). In this chapter, D will denote the normalized differentiation or momentum operator D = (1/2πi)d/dx. The inequality (5.1) is often written 5.2 $$ \sigma _X (f)\sigma _D (f) \equiv \left\| {Xf - \left\langle {Xf,f} \right\rangle f} \right\|_2 \left\| {Df - \left\langle {Df,f} \right\rangle f} \right\|_2 \geqslant 1,$$ where X is the multiplication operator f(x) ↦ xf(x). Barnes [22] attributes the first proof of (5.1) to a lecture delivered by Wiener in Hilbert’s mathematical physics seminar in Göttingen in 1925. In fact, Wiener discusses this lecture, though not the explicit proof of (5.1), at length in his autobiography (see [365], p. 105). What distinguished Wiener’s approach, however, was his macroscopic interpretation of (5.1), a point of view that led to further important achievements, including the Landau, Pollack and Slepian “Bell Labs” uncertainty principles in Chapter 3

Birkhäuser Boston eBooks•Dec 20, 2007•0 citations

Prinsip Ketidakpastian Heisenberg Pada Transformasi Fourier = Heisenberg's Uncertainty Principle in the Fourier Transform

Sitti Nuraini Rahmah

Pada tugas akhir ini akan dibahas pembuktian prinsip ketidakpastian Heisenberg pada transformasi Fourier. Prinsip ketidakpastian Heisenberg secara umum menjelaskan tentang ketidakpastian terhadap posisi dan momentum, sedangkan pada prinsip ketidakpastian Heisenberg pada transformai Fourier memiliki ketidakpastian terhadap fungsi dan transformasi Fouriernya. ketidakpastian Heisenberg menyatakan bahwa “fungsi dari domain waktu f(t) dan fungsi di domain frekuensinya atau transformasi Fourier dari f(t) yang disimbolkan dengan f ̂(ω) tidak mungkin terlokalisasi dengan baik secara bersamaan, jika f(t) terkonsentrasi disekitar suatu titik, maka f ̂(ω) tersebar pada R dan sebaliknya”. Artinya, jika kedua hal tersebut diukur secara bersamaan maka semakin besar suatu nilai fungsi maka akan semakin kecil nilai transformasi Fouriernya, dan sebaliknya. Pada tugas akhir ini, diperoleh bentuk dari prinsip ketidakpastian Heisenberg sebagai berikut: ∫_(-∞)^∞▒〖t^2 |f(t)|^2 dt〗 ∫_(-∞)^∞▒〖ω^2 〖|f ̂(ω)|〗^2 dω≥π/2 (∫_(-∞)^∞▒〖|f(t)|^2 dt〗)^2 〗. Dari persamaaan tersebut akan mencapai minimum di fungsi Gauss. Kata kunci: Transformasi Fourier, Prinsip Ketidakpastian Heisenberg, Parseval dan Plancherel.

•Jun 22, 2022•0 citations
According to a widespread notion, a monochromatic harmonic temporal wave’s frequency ω becomes inherently “uncertain” to a degree ω Δ if the wave lasts for only a limited time t Δ owing to the “Uncertainty Principle” which states that constant ≥ ω Δ ⋅ Δt (i.e. ω Δ cannot be zero if t Δ is finite). This frequency uncertainty is commonly thought of as also being reflected in the fact that the (“sinc”-shaped) frequency spectrum obtained by Fouriertransforming a finite-duration monochromatic wave spreads over a nonzero frequency range. Since, as often argued, the Fourier spectrum represents the ”frequency components” of a temporal function (or signal), the nonzero width of the spectrum tells us that the ”nominally” monochromatic ω frequency of a time-limited sinusoid is “effectively” polychromatic, with frequencies continuously distributed over a ω Δ range in accord with the Uncertainty Principle.
Abstract Following the treatments presented in Parts I and II, I herein discuss in more detail the popular notion that the frequency of a monochromatic RF pulse as well as that of a monochromatic FID is “in effect” uncertain due to the (Heisenberg) Uncertainty Principle, which also manifests itself in the fact that the FT‐spectrum of these temporal entities is spread over a nonzero frequency band. In Part III, I continue my preliminary review of some further fundamental concepts, such as the Heisenberg and Fourier Uncertainty Principles, that are needed to understand whether or not the NMR linewidth and the RF excitation bandwidth have anything to do with “uncertainty”. The article then culminates in re‐addressing our Two NMR Problems in a more conscientious frame of mind by using a more refined formalism. The correct interpretation of these problems will be discussed in Part IV. © 2008 Wiley Periodicals, Inc. Concepts Magn Reson Part A 32A: 302–325, 2008.
It is sometimes said that one can't localize a signal simultaneously in time and frequency. This can be misleading. It's not that we can't localize the signal in time and frequency. The signal itself can't be concentrated simultaneously in time and in frequency. The Heisenberg uncertainty principle does not limit what we can know about reality; it describes that reality. The shorter-lived a function, the wider the band of frequencies given by its Fourier transform; the narrower the band of frequencies of its Fourier transform, the more the function is spread out in time.
We obtain both a global and local uncertainty principle for the short-time Fourier transform. Explicit expressions are obtained for the uncertainty product in terms of the uncertainty product of the signal and window. Although the uncertainty product of the spectrogram is not the sum of the uncertainty products of the window and signal, nonetheless, we show that the global uncertainty product has to be greater than one. We also derive local uncertainty relations which indicate how the local spread in time and frequency are related at a particlar time–frequency point. In addition, we obtain general results for the time, scale, and frequency moments of the scalogram in terms of the moments of the signal and mother wavelet. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Abstract Fourier transform is an integral part of harmonic analysis, information theory and signal processing with wide applications. The transformation between the Fourier pair is governed by an uncertainty relation, most generally captured by the Bandwidth theorem, . The usual approaches to the uncertainty principle employ sophisticated methods which lack physical interpretation of the origin and description of the uncertainty for real systems. In this paper, we have proposed a physically inspired model of the Fourier transform with an aim to describe its underlying dynamics and how it leads to the fundamental uncertainty principle. A signal is modeled as helical spring in 3D space, which is wrapped around a circle with controllable wrapping frequency. This process is formulated to reproduce the Fourier transform. The model is used to describe the origin of uncertainty, and to formulate a new generator-based approach to the Bandwidth theorem.
A mathematical uncertainty principle is an inequality or uniqueness theorem concerning the joint localization of a function or system and its spectrum. A Fourier uncertainty principle is thus a statement that a function and its Fourier transform cannot both decay too rapidly. The most familiar form is the Wiener-Heisenberg inequality, 5.1 $$ \left\| {(x - x_0 )f(x)} \right\|_2 \left\| {(\xi - \xi _0 )\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{f} (\xi )} \right\|_2 \geqslant \frac{{\mathop {\left\| f \right\|}\nolimits_2^2 }} {{4\pi }},$$ which becomes an equality only for multiples of Gaussians of the form exp(−πα(x − x0)2 + 2πiξ0x). In this chapter, D will denote the normalized differentiation or momentum operator D = (1/2πi)d/dx. The inequality (5.1) is often written 5.2 σX(f)σD(f)≡∥Xf−⟨Xf,f⟩f∥2∥Df−⟨Df,f⟩f∥2⩾1, \sigma _X (f)\sigma _D (f) \equiv \left\| {Xf - \left\langle {Xf,f} \right\rangle f} \right\|_2 \left\| {Df - \left\langle {Df,f} \right\rangle f} \right\|_2 \geqslant 1,σX​(f)σD​(f)≡∥Xf−⟨Xf,f⟩f∥2​∥Df−⟨Df,f⟩f∥2​⩾1, where X is the multiplication operator f(x) ↦ xf(x). Barnes [22] attributes the first proof of (5.1) to a lecture delivered by Wiener in Hilbert’s mathematical physics seminar in Göttingen in 1925. In fact, Wiener discusses this lecture, though not the explicit proof of (5.1), at length in his autobiography (see [365], p. 105). What distinguished Wiener’s approach, however, was his macroscopic interpretation of (5.1), a point of view that led to further important achievements, including the Landau, Pollack and Slepian “Bell Labs” uncertainty principles in Chapter 3
Pada tugas akhir ini akan dibahas pembuktian prinsip ketidakpastian Heisenberg pada transformasi Fourier. Prinsip ketidakpastian Heisenberg secara umum menjelaskan tentang ketidakpastian terhadap posisi dan momentum, sedangkan pada prinsip ketidakpastian Heisenberg pada transformai Fourier memiliki ketidakpastian terhadap fungsi dan transformasi Fouriernya. ketidakpastian Heisenberg menyatakan bahwa “fungsi dari domain waktu f(t) dan fungsi di domain frekuensinya atau transformasi Fourier dari f(t) yang disimbolkan dengan f ̂(ω) tidak mungkin terlokalisasi dengan baik secara bersamaan, jika f(t) terkonsentrasi disekitar suatu titik, maka f ̂(ω) tersebar pada R dan sebaliknya”. Artinya, jika kedua hal tersebut diukur secara bersamaan maka semakin besar suatu nilai fungsi maka akan semakin kecil nilai transformasi Fouriernya, dan sebaliknya. Pada tugas akhir ini, diperoleh bentuk dari prinsip ketidakpastian Heisenberg sebagai berikut: ∫_(-∞)^∞▒〖t^2 |f(t)|^2 dt〗 ∫_(-∞)^∞▒〖ω^2 〖|f ̂(ω)|〗^2 dω≥π/2 (∫_(-∞)^∞▒〖|f(t)|^2 dt〗)^2 〗. Dari persamaaan tersebut akan mencapai minimum di fungsi Gauss. Kata kunci: Transformasi Fourier, Prinsip Ketidakpastian Heisenberg, Parseval dan Plancherel.