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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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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...
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Upsampling01:22

Upsampling

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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...
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Downsampling01:20

Downsampling

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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...
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Design Example: Aggregate Gradation01:24

Design Example: Aggregate Gradation

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The right type and quality of aggregates are crucial for concrete as they significantly influence its properties, mix proportions, and cost-effectiveness. If different sources are available for sand, the commonly used fine aggregate in concrete, the selection of sand is primarily based on its gradation.
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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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...
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Aliasing01:18

Aliasing

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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.
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Related Experiment Video

Updated: Jan 7, 2026

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells
06:03

AMEBaS: Automatic Midline Extraction and Background Subtraction of Ratiometric Fluorescence Time-Lapses of Polarized Single Cells

Published on: June 23, 2023

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WISEST: Weighted Interpolation for Synthetic Enhancement Using SMOTE with Thresholds.

Ryotaro Matsui1, Luis Guillen2, Satoru Izumi3

  • 1Graduate School of Information Sciences, Tohoku University, Aramaki Aza Aoba 6-3-09, Aoba-ku, Sendai 980-8579, Miyagi, Japan.

Sensors (Basel, Switzerland)
|December 31, 2025
PubMed
Summary
This summary is machine-generated.

WISEST, a new algorithm, effectively addresses imbalanced learning by creating synthetic minority samples. It improves detection of rare events, enhancing recall and F1 scores on diverse datasets.

Keywords:
Borderline-SMOTEKEELSMOTEWISESTclass imbalanceimbalanced datasetslocality-aware oversamplingoversamplingsynthetic data generationweighted interpolation

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Imbalanced learning poses challenges in identifying rare but critical events due to classifier bias towards majority classes.
  • This bias leads to the underperformance of machine learning models on minority classes, impacting real-world applications.

Purpose of the Study:

  • To introduce WISEST, a novel locality-aware weighted-interpolation algorithm for generating synthetic minority samples.
  • To evaluate WISEST's effectiveness in improving minority class detection on a wide range of imbalanced datasets.

Main Methods:

  • WISEST employs a locality-aware weighted-interpolation approach to synthesize minority samples near class boundaries.
  • The algorithm was benchmarked on over a hundred real-world imbalanced datasets, including KEEL, IoT-23, and BoT-IoT, with varying characteristics.

Main Results:

  • WISEST demonstrated consistent improvements in minority detection metrics, such as recall and F1 score, on approximately half of the tested datasets.
  • Relative recall increased by up to 25%, and F1 score by up to 18% compared to baseline methods.
  • Trade-offs were observed in accuracy and precision, varying by dataset and classifier.

Conclusions:

  • WISEST is a practical and robust method for imbalanced learning when minority data distribution allows for safe synthesis.
  • The algorithm shows significant potential for improving the detection of rare events in critical applications.
  • No single data sampling method uniformly excels across all imbalanced datasets, highlighting the need for tailored approaches.