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

Power Factor Correction01:20

Power Factor Correction

The power transmission to a factory involves the transfer of apparent power, a combination of active and reactive power. The power factor measures how effectively electrical power is converted into useful work output. The ratio of the real power (KW) that does the work to the apparent power (KVA) supplied to the circuit.
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
The proportional control gain, combined with the system's...
Sampling Continuous Time Signal01:11

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...
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

You might also read

Related Articles

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

Sort by
Same author

A hybrid ST-ViT-driven multimodal architecture combining spatiotemporal MRI patterns and radiomic features for enhanced prediction of pCR in neoadjuvant breast cancer therapy.

Biology direct·2026
Same author

Editorial: Machine learning and applied neuroscience, volume II.

Frontiers in neurorobotics·2026
Same author

Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.

Brain informatics·2025
Same author

Oscillometric blood pressure estimation using machine learning-based mapping of waveform features.

Biomedical engineering letters·2025
Same author

Transparent brain tumor detection using DenseNet169 and LIME.

Scientific reports·2025
Same author

Global, Regional, and National Burden of Nontraumatic Subarachnoid Hemorrhage: The Global Burden of Disease Study 2021.

JAMA neurology·2025

Related Experiment Video

Updated: Jun 24, 2026

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

Correlation-controlled stochastic computing for low-power FIR and IIR filters in edge DSP.

Raghav Krishna1, Gopinath Palanisamy2, Goutham Veerapu1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.

Scientific Reports
|June 22, 2026
PubMed
Summary

This study introduces a novel correlation-controlled stochastic computing (SC) framework for digital filters, significantly reducing power consumption and hardware costs for edge devices. This approach enables efficient signal processing in low-power biomedical and IoT applications.

Keywords:
Correlation controlledEdge IoTEncoderdecoder optimizationFIR/IIR filtersLow Power DSPStochastic computing

More Related Videos

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

Related Experiment Videos

Last Updated: Jun 24, 2026

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

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
07:11

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

Published on: November 10, 2023

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy
06:51

Confocal Microscopy Reveals Cell Surface Receptor Aggregation Through Image Correlation Spectroscopy

Published on: August 2, 2018

Area of Science:

  • Digital Signal Processing
  • Computer Engineering
  • Low-Power Electronics

Background:

  • Conventional digital filters in edge devices are power-hungry due to fixed-point arithmetic.
  • This limits their use in resource-constrained biomedical and IoT applications.

Purpose of the Study:

  • To propose a correlation-controlled stochastic computing (SC) framework for Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters.
  • To reduce power consumption and hardware costs in edge devices for digital signal processing (DSP).

Main Methods:

  • Implemented arithmetic operations using stochastic bitstreams instead of fixed-point units.
  • Introduced correlation-controlled bitstream generation and optimized encoder-decoder architectures.
  • Validated through software simulations on diverse edge DSP workloads (ECG, audio, PPG, IoT vibration).

Main Results:

  • Achieved Mean Squared Error (MSE) below 1% across all tested signals.
  • Obtained high signal-to-noise ratios (23-49 dB) at a bitstream length of [Formula: see text].
  • Reduced switching activity by up to 10x compared to a 16-bit fixed-point DSP baseline.
  • Correlation control reduced bias from 3.8% to below 0.5%.

Conclusions:

  • The correlation-controlled SC framework offers a practical solution for low-power DSP in edge devices.
  • Demonstrates a favorable trade-off between computational efficiency and signal fidelity.
  • Enables efficient signal processing for resource-constrained biomedical and IoT sensor nodes.