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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.
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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Related Experiment Video

Updated: Oct 24, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Deep learning based classification of unsegmented phonocardiogram spectrograms leveraging transfer learning.

Kaleem Nawaz Khan1,2, Faiq Ahmad Khan1,3, Anam Abid1,4

  • 1AI in Healthcare, Intelligent Information Processing Lab, National Center of Artificial Intelligence, UET Peshawar, Pakistan.

Physiological Measurement
|August 13, 2021
PubMed
Summary

This study introduces a deep learning approach using phonocardiogram (PCG) spectrograms for detecting cardiovascular diseases (CVDs). The method achieves high accuracy in identifying heart abnormalities from PCG signals, offering a promising tool for CVD screening.

Keywords:
classificationconvolutional neural networkphonocardiogramshort-time Fourier transformtransfer learning

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Last Updated: Oct 24, 2025

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Cardiovascular diseases (CVDs) are a leading global cause of mortality.
  • Early detection of heart abnormalities is crucial for effective CVD management.
  • Phonocardiogram (PCG) signals offer valuable diagnostic information but present analysis challenges due to data variability.

Purpose of the Study:

  • To develop and evaluate a deep learning-based computer-aided system for analyzing PCG signals.
  • To improve the accuracy and timeliness of detecting heart abnormalities.
  • To address the challenges posed by heterogeneous PCG datasets (PhysioNet and PASCAL).

Main Methods:

  • Utilized short-time Fourier transform (STFT) to generate spectrograms from PCG signals.
  • Developed and tested various convolutional neural network (CNN) models on PhysioNet, PASCAL, and combined datasets.
  • Applied transfer learning techniques to enhance model performance on the PASCAL dataset.

Main Results:

  • The CNN model achieved high performance on the PhysioNet dataset (e.g., 95.75% accuracy).
  • Performance varied across datasets, with the PASCAL dataset yielding 75.25% accuracy.
  • A combined dataset approach reached 92.7% accuracy, and transfer learning improved precision to 96.98% on noisy data.

Conclusions:

  • A custom, lightweight CNN model effectively analyzes PCG spectrograms for CVD detection.
  • The proposed deep learning approach demonstrates high classification accuracy and precision.
  • This method shows potential for efficient and reliable screening of cardiovascular diseases using PCG signals.