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

Classification of Signals01:30

Classification of Signals

381
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
381
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

213
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
213
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

198
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...
198
Aliasing01:18

Aliasing

115
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
115
Properties of the z-Transform I01:17

Properties of the z-Transform I

157
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...
157
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

165
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...
165

You might also read

Related Articles

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

Sort by
Same author

Homeostatic dendritic neuron based on co-integrated volatile and non-volatile memristors for neuromorphic processing.

Nature communications·2026
Same author

Supernetwork-based efficient mapping of deep learning applications to mixed-precision hardware using model adaptation.

Nature communications·2026
Same author

Data-In-situ Computing with One-Pixel-Multiple-Memristor Architecture for Neuromorphic Sequential Vision.

Nature communications·2026
Same author

Technology Roadmap of Bioinspired Computing Hardware.

ACS nano·2026
Same author

Homogeneous integration of two-dimensional material-based optoelectronic neurons and ferroelectric synapses for neuromorphic vision.

Nature communications·2026
Same author

In Situ Quantization with Memory-Transistor Transfer Unit Based on Electrochemical Random-Access Memory for Edge Applications.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same journal

Bioinspired Artificial Bioenergetic Organelles: Design Principles, Nanofabrication and Therapeutic Translation.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Advanced Electrolyte Materials Design for High-Energy Lithium Metal Batteries Beyond 500 Wh Kg<sup>-1</sup>.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Hydrophilic-Stable Nucleoside-Based Hydrogen-Bonded Organic Frameworks (N-HOF) for Therapeutic Bacterial Hybrid Systems.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Lanthanide-Bridged Dual-Atom Catalysts for Efficient Chlorine Electrosynthesis.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Composite Liquid Marble Templated Millimetric Capsule With Tunable Rigidity, Porosity, and Thermal Reconfigurability Toward 3D Cell Culture.

Advanced materials (Deerfield Beach, Fla.)·2026
Same journal

Bias-Triggered Conductivity Relaxation (BCR): A Unique Tool to Simultaneously Investigate Thermodynamics, Kinetics, and Electrostatic Effects of Oxygen Reactions in MIEC Thin Films.

Advanced materials (Deerfield Beach, Fla.)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.0K

Emerging Materials and Computing Paradigms for Temporal Signal Analysis.

Teng Zhang1, Stanislaw Wozniak2, Ghazi Sarwat Syed2

  • 1Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, 100871, China.

Advanced Materials (Deerfield Beach, Fla.)
|February 12, 2025
PubMed
Summary
This summary is machine-generated.

Emerging materials and computing paradigms offer new ways to analyze temporal signals, improving efficiency in fields like healthcare and finance. This research explores their potential to overcome limitations of traditional methods.

Keywords:
computing paradigmsemerging devicesneuromorphic computingtemporal signal

More Related Videos

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K

Related Experiment Videos

Last Updated: May 28, 2025

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
04:13

Computer-based Multitaper Spectrogram Program for Electroencephalographic Data

Published on: November 13, 2019

12.0K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.3K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K

Area of Science:

  • Computer Science
  • Materials Science
  • Signal Processing

Background:

  • Increasing data generation necessitates advanced temporal signal analysis.
  • Traditional methods struggle with complex, time-varying data.
  • Domains like healthcare, finance, and telecommunications require robust solutions.

Purpose of the Study:

  • To explore emerging materials and computing paradigms for temporal signal analysis.
  • To highlight the potential of these innovations in overcoming traditional limitations.
  • To identify challenges and opportunities in this evolving field.

Main Methods:

  • Perspective study analyzing current trends and future directions.
  • Review of emerging materials and computing paradigms.
  • Discussion of in situ processing capabilities for real-time analysis.

Main Results:

  • Emerging materials enable in situ processing, reducing latency.
  • New computing paradigms enhance the interpretation of temporal signals.
  • Significant potential exists for advancing signal analysis capabilities.

Conclusions:

  • Emerging materials and computing paradigms are crucial for next-generation temporal signal analysis.
  • Harnessing these innovations is key to unlocking complex temporal data.
  • This field promises to expand the accessibility of previously intractable signal analysis problems.