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

Upsampling

Upsampling

Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...

Sampling Theorem

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

Sampling Continuous Time Signal

Sampling Continuous Time Signal

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...

Downsampling

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...

Sampling Methods: Overview

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling.
In analytical chemistry, the choice of sampling...

Bandpass Sampling

Bandpass Sampling

In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2. The spectrum...

You might also read

Related Articles

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

Sort by

Same author

Unmixing the Neck: Accurate Jugular Venous Pulse Detection From Wearable PPG.

IEEE journal of biomedical and health informatics·2026

Same author

Evaluating the Pulse Rate Estimation Performance of the DS-EWMA Algorithm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

A TinyML Motion-Based Embedded Cough Detection System.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Heart Rate Estimation from Neck Photoplethysmography using FFT-Based Scoring and a Shallow Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same author

Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device.

Journal of clinical medicine·2024

Same author

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices.

Scientific reports·2024

Search research articles

Related Experiment Video

Updated: Jun 6, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Compressive sensing: from "compressing while sampling" to "compressing and securing while sampling".

Amir M Abdulghani¹, Esther Rodriguez-Villegas

¹Department of Electrical and Electronic Engineering, Imperial College London SW7 2AZ, UK. amirm@imperial.ac.uk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 25, 2010

Summary

Compressive sensing samples signals below the Nyquist rate, reducing computational load and power consumption. This novel approach also enhances data privacy by securing electroencephalogram (EEG) signals during sampling.

More Related Videos

A New Workflow for Sampling and Digitizing Increment Cores

A New Workflow for Sampling and Digitizing Increment Cores

Published on: September 27, 2024

Measurement of Compressive Stress-Strain Response at Small-Strains

Measurement of Compressive Stress-Strain Response at Small-Strains

Published on: December 5, 2025

Related Experiment Videos

Last Updated: Jun 6, 2026

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

A New Workflow for Sampling and Digitizing Increment Cores

A New Workflow for Sampling and Digitizing Increment Cores

Published on: September 27, 2024

Measurement of Compressive Stress-Strain Response at Small-Strains

Measurement of Compressive Stress-Strain Response at Small-Strains

Published on: December 5, 2025

Area of Science:

Signal Processing
Information Theory
Neuroscience

Background:

Traditional signal sampling requires high frequencies (Nyquist rate) for complete signal recovery.
Subsequent processing like encryption and compression is computationally intensive and power-hungry, especially for battery-operated systems.

Purpose of the Study:

To explore compressive sensing as an alternative sampling paradigm.
To analyze the potential of compressive sensing in reducing computational complexity and power consumption.
To investigate the data privacy benefits of compressive sensing for electroencephalogram (EEG) signals.

Main Methods:

Information theoretic analysis of real EEG signals.
Evaluation of compressive sensing's effectiveness in sampling and securing data.

Main Results:

Compressive sensing enables signal sampling at sub-Nyquist rates without compromising recovery.
Analysis demonstrates that compressive sensing offers significant advantages in preserving data privacy for EEG signals.
The study confirms that compressive sensing inherently compresses and secures data during the sampling process.

Conclusions:

Compressive sensing presents a powerful tool for efficient and secure signal acquisition.
This technology can lead to reduced system complexity and enhanced data security, particularly for sensitive biological signals like EEG.