Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Discrete-time Fourier transform

Discrete-time Fourier transform

The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...

Discrete Fourier Transform

Discrete Fourier Transform

The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next sampling...

Convolution Properties II

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

Convolution: Math, Graphics, and Discrete Signals

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...

You might also read

Related Articles

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

Sort by

Same author

Risk Factors of Neutropenic Enteropathy during Induction Phase of Treatment of Childhood Acute Lymphoblastic Leukemia.

Mymensingh medical journal : MMJ·2026

Same author

Assessment of Disseminated Intravascular Coagulation in Children with Acute Lymphoblastic Leukemia during Induction Chemotherapy in A Tertiary Care Hospital.

Mymensingh medical journal : MMJ·2025

Same author

Shrinkage of Food Materials During Drying: Current Status and Challenges.

Comprehensive reviews in food science and food safety·2020

Same author

Multiscale Modeling for Food Drying: State of the Art.

Comprehensive reviews in food science and food safety·2020

Same author

Effect of drying methods and storage with agro-ecological conditions on phytochemicals and antioxidant activity of fruits: a review.

Critical reviews in food science and nutrition·2020

Same author

A mathematical model for predicting the transport process and quality changes during intermittent microwave convective drying.

Food chemistry·2020

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Real-time digital optical matrix multiplication with a joint-transform correlator.

S Zhang¹, M A Karim

¹Department of Electrical and Computer Engineering, The University of Dayton, 300 College Park, Dayton, Ohio 45469-0226, USA.

|February 29, 2008

Summary

This summary is machine-generated.

This study introduces a joint-transform correlator architecture for fast and accurate digital matrix multiplication. The method efficiently handles various matrix types through input data programming, leveraging real-time processing capabilities.

More Related Videos

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

Related Experiment Videos

Last Updated: Jul 7, 2026

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Demonstration of Spin-Multiplexed and Direction-Multiplexed All-Dielectric Visible Metaholograms

Published on: September 25, 2020

Area of Science:

Digital Signal Processing
Optical Computing
Matrix Algebra

Background:

Digital matrix multiplication is fundamental in numerous computational tasks.
Existing methods may face limitations in speed or accuracy.
Joint-transform correlators offer potential for parallel processing.

Purpose of the Study:

To propose and evaluate a novel joint-transform correlation architecture for digital matrix multiplication.
To demonstrate the versatility of the architecture for various matrix types (real/complex, vector/matrix).
To highlight the benefits of speed and accuracy offered by the proposed method.

Main Methods:

Utilizing a joint-transform correlation architecture for matrix operations.
Programming data arrangement in the input plane of a multiple-input joint-transform correlator.
Employing computer simulations with negative binary encoding for matrix elements.

Main Results:

Successful realization of real-valued and complex-valued matrix-vector multiplication.
Successful realization of real-valued and complex-valued matrix-matrix multiplication.
Demonstrated high accuracy due to digital representation and speed from real-time processing.

Conclusions:

The joint-transform correlation architecture provides an efficient approach to digital matrix multiplication.
The method is adaptable to different matrix types through input programming.
The architecture combines the advantages of speed and accuracy for computational efficiency.