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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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Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Relative Motion Analysis using Rotating Axes-Problem Solving01:29

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Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
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Related Experiment Video

Updated: Aug 2, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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Reduced-Parameter YOLO-like Object Detector Oriented to Resource-Constrained Platform.

Xianbin Zheng1, Tian He1

  • 1College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266071, China.

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|April 13, 2023
PubMed
Summary
This summary is machine-generated.

We developed a YOLO object detection algorithm optimized for Field-Programmable Gate Arrays (FPGAs). This approach significantly reduces power consumption for real-time environmental detection on mobile devices.

Keywords:
FPGAQNNneural network acceleratorobject detection

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Area of Science:

  • Computer Vision
  • Embedded Systems
  • Hardware Acceleration

Background:

  • Deep learning target detectors are crucial for robotics and automotive applications.
  • High computational demands of deep learning hinder deployment on resource-constrained, energy-efficient devices.

Purpose of the Study:

  • To propose a YOLO object detection algorithm deployable on Field-Programmable Gate Arrays (FPGAs).
  • To leverage FPGA parallel computing for accelerated inference of deep learning models.

Main Methods:

  • Quantized a YOLO model for efficient FPGA operation.
  • Developed custom hardware operators for model computational units (convolution, pooling, Concat).
  • Implemented and verified the object detection accelerator on a Xilinx ZYNQ platform.

Main Results:

  • Achieved detection accuracy comparable to common algorithms.
  • Demonstrated significantly lower power consumption compared to CPU and GPU.
  • Attained fast inference speeds suitable for mobile deployment.

Conclusions:

  • FPGA-based YOLO acceleration offers an efficient solution for resource-limited devices.
  • The developed accelerator is suitable for real-time environmental perception in mobile applications.
  • This method addresses the power and computational challenges of deploying deep learning detectors.