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

Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

441
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...
441
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

390
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
390
Discrete Fourier Transform01:15

Discrete Fourier Transform

441
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...
441
Effective Value of a Periodic Waveform01:07

Effective Value of a Periodic Waveform

726
The concept of effective value, the root mean square (RMS) value, is crucial in understanding electrical circuits and power delivery. This idea emerges from the necessity to measure the effectiveness of a voltage or current source in supplying power to a resistive load.
The effective value of a periodic current represents the direct current (DC) that conveys the same average power to a resistor as the periodic current itself. This concept is crucial when assessing AC circuits. To determine the...
726
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

405
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
405
NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences01:17

NMR Spectrometers: Radiofrequency Pulses and Pulse Sequences

944
A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
944

You might also read

Related Articles

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

Sort by
Same author

FPGA Implementation of a Radar-Based Fall Detection System Using Binarized Convolutional Neural Networks.

Sensors (Basel, Switzerland)·2026
Same author

Design of Network-on-Chip-Based Restricted Coulomb Energy Neural Network Accelerator on FPGA Device.

Sensors (Basel, Switzerland)·2024
Same author

Hand Gesture Recognition Using FSK Radar Sensors.

Sensors (Basel, Switzerland)·2024
Same author

FMCW Radar Sensors with Improved Range Precision by Reusing the Neural Network.

Sensors (Basel, Switzerland)·2024
Same author

FPGA Implementation of Keyword Spotting System Using Depthwise Separable Binarized and Ternarized Neural Networks.

Sensors (Basel, Switzerland)·2023
Same author

The Effect of Intervention for Improving Colonoscopy Quality Is Associated with the Personality Traits of Endoscopists.

Gut and liver·2023

Related Experiment Video

Updated: Sep 26, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K

High-Speed Continuous Wavelet Transform Processor for Vital Signal Measurement Using Frequency-Modulated Continuous

Chanhee Bae1, Seongjoo Lee2, Yunho Jung1,3

  • 1Department of Smart Air Mobility, Korea Aerospace University, Goyang-si 10540, Korea.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

This study introduces a high-speed continuous wavelet transform (CWT) processor for analyzing vital signals from FMCW radar. The FPGA-based processor significantly reduces processing time for heartbeat and respiration detection.

Keywords:
frequency-modulated continuous wave (FMCW)mixed-radix multipath delay commutator (MRMDC)radix-2 single-path delay feedback (R2SDF)vital signal measurement

More Related Videos

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis
07:55

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

Published on: September 22, 2017

10.3K
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

241

Related Experiment Videos

Last Updated: Sep 26, 2025

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
09:36

Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements

Published on: June 25, 2021

3.2K
High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis
07:55

High-speed Continuous-wave Stimulated Brillouin Scattering Spectrometer for Material Analysis

Published on: September 22, 2017

10.3K
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

241

Area of Science:

  • Signal Processing
  • Biomedical Engineering
  • Embedded Systems

Background:

  • Frequency-modulated continuous wave (FMCW) radar sensors are increasingly used for non-contact vital sign monitoring.
  • Analyzing vital signals from FMCW radar requires efficient signal processing techniques, such as the continuous wavelet transform (CWT).
  • Existing CWT implementations may face challenges in achieving high-speed processing for real-time vital sign analysis.

Purpose of the Study:

  • To propose and implement a high-speed continuous wavelet transform (CWT) processor for analyzing vital signals.
  • To optimize the CWT processor for high-throughput and reduced circuit area using advanced architectures.
  • To validate the processor's performance in real-time heartbeat and respiration detection using FMCW radar data.

Main Methods:

  • Designed a CWT processor integrating Fast Fourier Transform (FFT) and Inverse FFT (IFFT) modules.
  • Utilized pipeline FFT architectures (R2SDF and MRMDC) for high-throughput processing.
  • Implemented four-channel operations and a mixed-radix algorithm to minimize processing time and circuit area.
  • Supported variable transform lengths (8 to 1024) for versatile vital signal analysis.
  • Implemented the processor on a Field-Programmable Gate Array (FPGA) device.

Main Results:

  • Achieved significant reductions in processing time: 48.4-fold and 40.7-fold compared to MATLAB on an Intel i7 CPU.
  • Demonstrated a 73.3% reduction in processing time compared to previous FPGA-based CWT implementations.
  • Successfully verified the processor's capability in measuring heartbeat and respiration from FMCW radar signals.

Conclusions:

  • The proposed FPGA-based CWT processor offers a high-speed and efficient solution for vital signal analysis from FMCW radar.
  • The optimized architecture significantly enhances processing throughput and reduces hardware resource utilization.
  • This advancement enables more effective real-time health monitoring systems utilizing radar technology.