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Convolution Properties II01:17

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The important convolution properties include width, area, differentiation, and integration properties.
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Convolution Properties I01:20

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Convolution computations can be simplified by utilizing their inherent properties.
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The fineness of cement directly influences the rate of hydration, as the hydration begins at the surface of the cement particles. In addition to hydration, the fineness of cement is vital for various properties of concrete including workability, gypsum requirement, and long-term behavior. The fineness of cement is represented in terms of the specific surface of cement which is typically measured in square meters per kilogram, with several methods available for this determination.
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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
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Coarse-Fine Convolutional Deep-Learning Strategy for Human Activity Recognition.

Carlos Avilés-Cruz1, Andrés Ferreyra-Ramírez2, Arturo Zúñiga-López3

  • 1Autonomous Metropolitan University. Electronics Department, Av. San Pablo 180, Col. Reynosa, C.P. 02200 Mexico City, Mexico. caviles@azc.uam.mx.

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|April 3, 2019
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Summary
This summary is machine-generated.

This study introduces a new deep learning framework for human activity recognition (HAR) using smartphone sensors. The novel convolutional neural network (CNN) strategy accurately classifies six daily activities.

Keywords:
CNNclassificationdeep-learninghuman action recognition

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Area of Science:

  • Computer Science
  • Machine Learning
  • Signal Processing

Background:

  • Deep learning has significantly advanced human activity recognition (HAR) performance.
  • Existing methods often require complex feature engineering or large datasets.

Purpose of the Study:

  • To propose a novel deep learning framework for single-user HAR using smartphone sensors.
  • To enhance HAR accuracy through a multi-scale convolutional neural network (CNN) feature fusion strategy.

Main Methods:

  • A new CNN strategy employing three parallel CNNs (fine, medium, coarse) for local feature extraction.
  • Fusion of features from parallel CNNs for the final classification task.
  • Utilized tri-axial accelerometer and gyroscope data from a smartphone to capture motion signals.

Main Results:

  • Successfully classified six distinct human activities: walking, walking upstairs, walking downstairs, sitting, standing, and laying.
  • Demonstrated the effectiveness of the proposed CNN feature fusion approach for HAR.

Conclusions:

  • The novel CNN framework offers an effective approach for smartphone-based HAR.
  • Feature fusion from multi-scale CNNs improves the accuracy of human activity classification.