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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
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An Improved Multispectral Palmprint Recognition System Using Autoencoder with Regularized Extreme Learning Machine.

Abdu Gumaei1, Rachid Sammouda1, Abdul Malik S Al-Salman1

  • 1Department of Computer Science, King Saud University, Riyadh, Saudi Arabia.

Computational Intelligence and Neuroscience
|July 7, 2018
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Summary
This summary is machine-generated.

This study introduces a faster multispectral palmprint recognition system (MPRS) using autoencoders and regularized extreme learning machines. The novel approach effectively reduces features for quicker human identification without sacrificing accuracy.

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Multispectral palmprint recognition systems (MPRS) are crucial for human identification.
  • Improving MPRS accuracy and speed is an ongoing challenge.
  • Existing methods may struggle with feature dimensionality and computational efficiency.

Purpose of the Study:

  • To develop a novel, efficient, and accurate MPRS.
  • To reduce palmprint feature dimensionality without compromising recognition accuracy.
  • To enhance the speed of palmprint-based identification and verification.

Main Methods:

  • Region of interest (ROI) extraction using David Zhang's method.
  • Palmprint feature extraction via normalized Gist (NGist) descriptor.
  • Dimensionality reduction using an optimized autoencoder (AE).
  • Classification using a regularized extreme learning machine (RELM).

Main Results:

  • The proposed AE-RELM approach significantly improved recognition accuracy.
  • Feature dimensionality reduction led to faster processing speeds.
  • Experimental results on the MS-PolyU dataset demonstrated superior performance compared to state-of-the-art methods.
  • The system exhibited robustness and efficiency in palmprint recognition.

Conclusions:

  • The proposed autoencoder and regularized extreme learning machine approach offers a robust and efficient solution for multispectral palmprint recognition.
  • This method effectively balances speed and accuracy, making it suitable for practical human identification applications.
  • The study highlights the potential of deep learning techniques for enhancing biometric systems.