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Arithmetic Mean01:08

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The arithmetic mean is the most commonly used measure of the central tendency of a data set. It is defined as the sum of all the elements constituting the data set, divided by the total number of elements. It is sometimes loosely referred to as the “average.”
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An arithmetic sequence is a structured arrangement of numbers where each term is derived by adding a constant value, known as the common difference, to the previous term. This consistent pattern allows for the efficient computation of any term within the sequence as well as the cumulative sum of multiple terms. The formula for finding the nth term of an arithmetic sequence is:Here, aₙ represents the nth term of the sequence, a is the first term, d is the common difference, and n is the...
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Theorems of Pappus and Guldinus: Problem Solving01:12

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Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
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Phasor Arithmetics01:13

Phasor Arithmetics

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Phasors and their corresponding sinusoids are interrelated, offering unique insights into the behavior of alternating current (AC) circuits. One way to understand this relationship is through the operations of differentiation and integration in both the time and phasor domains.
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Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

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A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
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Binomial Expansion Using Pascal's Triangle01:30

Binomial Expansion Using Pascal's Triangle

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Expanding a binomial expression such as (a + b)n results in a predictable sequence of terms that can be systematically derived using Pascal’s Triangle. This triangular array of numbers plays a central role in understanding and computing the coefficients of binomial expansions.Pascal’s Triangle is constructed such that each row corresponds to the coefficients of a binomial raised to a power. The topmost row, known as the zeroth row, corresponds to (a + b)0, and each successive row...
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Related Experiment Video

Updated: Jan 9, 2026

Measuring the Switch Cost of Smartphone Use While Walking
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Published on: April 30, 2020

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A Novel Arithmetic Optimization PDR Algorithm for Smartphones.

Mingze Zhang1, Aigong Xu1

  • 1School of Geomatics, Liaoning Technical University, Fuxin 123000, China.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

A novel Arithmetic Optimization Algorithm for Pedestrian Dead Reckoning (PDR) efficiently optimizes system parameters for smartphones. This AO-PDR algorithm achieves accurate pedestrian positioning with a mean error of 0.2893 m.

Keywords:
AO-PDRfitness functionheading correction mechanismlocation-based serviceoptimal system parameterssmartphones

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Area of Science:

  • Computer Science
  • Navigation Systems
  • Algorithm Optimization

Background:

  • Pedestrian Dead Reckoning (PDR) systems require accurate parameter settings for reliable performance.
  • Existing PDR algorithms face challenges in parameter optimization, impacting positioning accuracy.

Purpose of the Study:

  • To propose a novel Arithmetic Optimization PDR (AO-PDR) algorithm for smartphones.
  • To optimize PDR system parameters for enhanced accuracy and efficiency.

Main Methods:

  • Developed an AO-PDR algorithm utilizing the Arithmetic Optimization Algorithm (AOA).
  • Established a fitness function based on positioning error, step deviation, and step length deviation.
  • Applied AOA to search for optimal parameters for step detection, length estimation, and heading correction.

Main Results:

  • The AO-PDR algorithm efficiently and accurately obtained optimal system parameters.
  • Achieved a mean optimal fitness of 1.352 with a mean running time of 164.85 seconds.
  • Demonstrated high adaptability, efficiency, and stability across different users and devices, with a mean positioning error of 0.2893 m.

Conclusions:

  • The AO-PDR algorithm effectively optimizes PDR parameters, leading to improved pedestrian positioning accuracy.
  • The proposed method meets the accuracy requirements for pedestrian location-based services.
  • AO-PDR offers a robust and efficient solution for smartphone-based pedestrian navigation.