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

Basic Operations on Signals01:22

Basic Operations on Signals

Basic signal operations include time reversal, time scaling, time shifting, and amplitude transformations. These operations are fundamental in signal processing and analysis.
Time Reversal mirrors a continuous-time signal about the vertical axis at t=0. This is achieved by substituting t with −t. For example, if a signal x(t) is considered, the time-reversed signal is x(−t). This operation can be graphically represented, showing the mirrored signal.
Transformations of Functions III01:20

Transformations of Functions III

Transformations modify the graphical representation of a function without changing its fundamental form. One common transformation is reflection, which flips the graph across a designated axis. When the vertical coordinates of all points are multiplied by the negative one, the entire graph is mirrored over the horizontal axis. This transformation reverses the vertical orientation of peaks and troughs, akin to signal inversion in electrical systems, where a waveform is flipped, but the timing of...
Properties of DTFT I01:24

Properties of DTFT I

In signal processing, Discrete-Time Fourier Transforms (DTFTs) play a critical role in analyzing discrete-time signals in the frequency domain. Various properties of the DTFTs such as linearity, time-shifting, frequency-shifting, time reversal, conjugation, and time scaling help understand and manipulate these signals for different applications.
The linearity property of DTFTs is fundamental. If two discrete-time signals are multiplied by constants a and b respectively, and then combined to...
Reconstruction of Signal using Interpolation01:10

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...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
Properties of the z-Transform I01:17

Properties of the z-Transform I

The z-transform is a fundamental tool in digital signal processing, enabling the analysis of discrete-time systems through its various properties. It is an invaluable tool for analyzing discrete-time systems, offering a range of properties that simplify complex signal manipulations. One fundamental property is linearity. For any two discrete-time signals, the z-transform of their linear combination equals the same linear combination of their individual z-transforms. This property is essential...

You might also read

Related Articles

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

Sort by
Same author

Fall prediction algorithm with built-in instability metrics.

Journal of biomechanics·2025
Same author

Differential phase-diversity electrooptic modulator for cancellation of fiber dispersion and laser noise.

Nature communications·2023
Same author

Phase Diversity Electro-optic Sampling: A new approach to single-shot terahertz waveform recording.

Light, science & applications·2022
Same author

Neural network enabled time stretch spectral regression.

Optics express·2021
Same author

Chromo-modal dispersion for optical communication and time-stretch spectroscopy.

Optics letters·2021
Same author

The mechanisms of cellular crosstalk between mesenchymal stem cells and natural killer cells: Therapeutic implications.

Journal of cellular physiology·2020

Related Experiment Video

Updated: May 7, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Anamorphic transformation and its application to time-bandwidth compression.

Mohammad H Asghari, Bahram Jalali

    Applied Optics
    |October 3, 2013
    PubMed
    Summary

    A novel physics-based method reshapes analog signals before digitization, enabling capture of high-frequency components and reducing data size. This lossless compression technique enhances digitizer capabilities for big data challenges.

    Area of Science:

    • Signal Processing
    • Data Compression
    • Analog-to-Digital Conversion

    Background:

    • Conventional digitizers have limitations in capturing high-frequency components and managing large data volumes.
    • Existing data compression methods often involve trade-offs between compression ratio and information loss.

    Purpose of the Study:

    • To introduce a general method for compressing the modulation time-bandwidth product of analog signals.
    • To enable conventional digitizers to capture signals beyond their bandwidth and reduce digital data size.
    • To present a physics-based signal grooming technique for efficient data handling.

    Main Methods:

    • A physics-based signal grooming technique is applied in the analog domain prior to sampling.
    • The method involves feature-selective reshaping of the signal's complex field.

    Related Experiment Videos

    Last Updated: May 7, 2026

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

  • Inspired by biological (Fovea centralis) and artistic (anamorphic transformation) principles.

    • Main Results:

    • Achieved lossless compression of the analog signal's modulation time-bandwidth product.
    • Enabled capture of frequency components beyond the digitizer's bandwidth.
    • Reduced the total digital data size, alleviating storage and transmission bottlenecks.

    Conclusions:

    • The proposed analog-domain signal reshaping offers a powerful approach for enhancing digitizer performance.
    • This method provides a lossless data compression solution applicable to big data challenges.
    • The technique can also be implemented in the digital domain as a data compression algorithm.