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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

62
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
62
Convergence of Fourier Series01:21

Convergence of Fourier Series

126
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
126
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

85
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....
85
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

170
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...
170
Downsampling01:20

Downsampling

127
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...
127
Upsampling01:22

Upsampling

201
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...
201

You might also read

Related Articles

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

Sort by
Same author

Establishing a GRU-GCN coordination-based prediction model for miRNA-disease associations.

BMC genomic data·2025
Same author

Feasibility of Early Surgical Treatment for Adolescent Patients with Prolactinoma: A Case Report and Literature Review.

Medicina (Kaunas, Lithuania)·2024
Same author

A Deep Learning-Based Chinese Semantic Parser for the Almond Virtual Assistant.

Sensors (Basel, Switzerland)·2022
Same author

3-To-1 Pipeline: Restructuring Transfer Learning Pipelines for Medical Imaging Classification via Optimized GAN Synthetic Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2020
Same author

Real-Time Identification of Irrigation Water Pollution Sources and Pathways with a Wireless Sensor Network and Blockchain Framework.

Sensors (Basel, Switzerland)·2020
Same author

Governance on the Drug Supply Chain via Gcoin Blockchain.

International journal of environmental research and public health·2018

Related Experiment Video

Updated: Jun 3, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K

Progressive Bounded Error Piecewise Linear Approximation with Resolution Reduction for Time Series Data Compression.

Jeng-Wei Lin1, Shih-Wei Liao2, Yu-Hung Tsai1

  • 1Department of Information Management, Tunghai University, Taichung 407224, Taiwan.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces PBEPLA-RR, a novel method for compressing time series data from AIoT devices. It efficiently generates multiple approximations with varying accuracy, significantly reducing storage needs and energy consumption.

Keywords:
PBEPLA-RRSwing-RRbounded error piecewise linear approximationhierarchical residual encodingprogressive data compressionsensor datatime series

More Related Videos

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

445
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

Related Experiment Videos

Last Updated: Jun 3, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

17.6K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

445
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

Area of Science:

  • Data Science
  • Artificial Intelligence of Things (AIoT)
  • Data Compression

Background:

  • AIoT devices generate vast amounts of time series data, leading to high energy costs for transmission, storage, and processing.
  • Existing lossy compression methods offer better ratios by sacrificing accuracy, but different applications require varying data fidelity.
  • Storing multiple compressed versions for different error bounds is inefficient.

Purpose of the Study:

  • To develop a method that efficiently generates multiple time series approximations with different error bounds from a single compressed representation.
  • To reduce the overall data size and energy consumption associated with storing and managing time series data.
  • To dynamically provide data versions tailored to specific accuracy requirements.

Main Methods:

  • Progressive decomposition of time series into piecewise linear functions, starting with the largest error bound.
  • Utilizing Swing-Recursive Residual (Swing-RR) algorithm to generate Bounded Error Piecewise Linear Approximations (BEPLA) at each decomposition step.
  • Aggregating multiple BEPLAs to create approximations for successively smaller error bounds.

Main Results:

  • The proposed method, PBEPLA-RR, was evaluated on eight real-world datasets for error bounds of 5%, 1%, and 0.5%.
  • The combined data size of multiple BEPLAs was comparable to storing a single version at the smallest error bound.
  • This significantly reduced storage requirements compared to maintaining independent compressed versions for each error bound.

Conclusions:

  • PBEPLA-RR effectively achieves high compression ratios for time series data.
  • The method provides a flexible way to obtain multiple approximations with different error bounds from a single compressed model.
  • This approach offers substantial energy and storage savings for AIoT applications.