Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Properties of Fourier Transform I01:21

Properties of Fourier Transform I

256
The application of Fourier Transform properties in radio broadcasting is multifaceted, enabling significant advancements in the way signals are transmitted and received. Key areas where these properties are utilized include simultaneous multi-channel transmission, audio clip speed adjustments, live broadcast delays for different time zones, audio frequency adjustments, and signal demodulation.
In radio broadcasting, multiple audio signals often need to be transmitted simultaneously. The Fourier...
256
Properties of Fourier Transform II01:24

Properties of Fourier Transform II

327
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
327
Basic signals of Fourier Transform01:07

Basic signals of Fourier Transform

599
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
599
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

426
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
426
Properties of Fourier series II01:21

Properties of Fourier series II

284
Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
284
Properties of Fourier series I01:20

Properties of Fourier series I

462
The Fourier series is a powerful tool in signal processing and communications, allowing periodic signals to be expressed as sums of sine and cosine functions. A foundational property of the Fourier series is linearity. If we consider two periodic signals, their linear combination results in a new signal whose Fourier coefficients are simply the corresponding linear combinations of the original signals' coefficients. This property is crucial in applications like frequency modulation (FM)...
462

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

QSAR modelling of enzyme inhibition toxicity of ionic liquid based on chaotic spotted hyena optimization algorithm.

SAR and QSAR in environmental research·2024
Same author

Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm.

SAR and QSAR in environmental research·2023
Same author

QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm.

SAR and QSAR in environmental research·2023
See all related articles

Related Experiment Video

Updated: Sep 19, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K

Investigating the dynamics and uncertainties in portfolio optimization using the Fourier-Millen transform.

Muhammad Hilal Alkhudaydi1, Aiedh Mrisi Alharthi2

  • 1Department of Mathematics and Statistics, College of Science, Taif University, Taif City, Saudi Arabia.

Plos One
|June 17, 2025
PubMed
Summary

This study explores using Fourier transforms and neural networks for portfolio optimization. The research identifies key factors for optimal portfolio composition using financial data analysis.

More Related Videos

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

516
Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

3.8K

Related Experiment Videos

Last Updated: Sep 19, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

43.0K
Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

516
Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
06:37

Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy

Published on: June 15, 2022

3.8K

Area of Science:

  • Quantitative Finance
  • Financial Engineering
  • Computational Finance

Background:

  • Portfolio optimization is crucial for managing investment risk and achieving desired returns.
  • Securities within a portfolio introduce uncertainty, necessitating analysis of contributing factors.
  • Understanding these uncertainties is vital for effective investment strategies.

Purpose of the Study:

  • To investigate primary elements contributing to optimal portfolio composition.
  • To evaluate the efficacy of physical analysis methods in portfolio optimization.
  • To apply feature-based models and artificial neural networks to financial data.

Main Methods:

  • Feature-based models utilizing Fourier transform, wavelet transforms, and Fourier-Mellin transform.
  • Inputting geometric features into artificial neural networks (convolutional and recurrent).
  • Comparison with algorithms like vector autoregression on US stock market data.

Main Results:

  • Preliminary findings on the utility of physical analysis and neural networks for portfolio optimization.
  • Identification of key geometric features influencing portfolio composition.
  • Demonstration of model performance on real-world financial data.

Conclusions:

  • Physical analysis methods show potential for identifying critical factors in portfolio optimization.
  • Artificial neural networks, when combined with feature engineering, offer a viable approach to portfolio optimization.
  • The study provides initial insights into advanced analytical techniques for financial markets.