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Related Concept Videos

What is a Frequency Distribution00:51

What is a Frequency Distribution

27.2K
A frequency is the number of times a value of the data occurs. The sum of all the frequency values represents the total number of students included in the sample. It is commonly used to group data of quantitative types. Frequency distributions can be displayed in a table, histogram, line graph, dot plot, or pie chart, just to name a few. A histogram is a graphical representation of tabulated frequencies, shown as adjacent rectangles, erected over discrete intervals (bins), with an area equal to...
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Mean From a Frequency Distribution01:11

Mean From a Frequency Distribution

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Sometimes, data gathered from an experiment on a large sample or population are organized into concise tables. In such cases, the frequency of the quantitative data set is plotted in the form of a table. Or else, the data values are grouped into the quantity’s intervals, which form classes, and their respective frequencies are known. That is, the data values are distributed over different categories or classes. This is known as frequency distribution.
When such a data set is encountered,...
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Construction of Frequency Distribution01:15

Construction of Frequency Distribution

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A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...
12.8K
Percentage Frequency Distribution00:57

Percentage Frequency Distribution

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A percentage frequency distribution, in general, is a display of data that indicates the percentage of observations for each data point or grouping of data points. It is a commonly used method for expressing the relative frequency of survey responses and other data. The percentage frequency distributions are often displayed as bar graphs, pie charts, or tables.
The process of making a percentage frequency distribution involves the following few steps: note the total number of observations;...
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Relative Frequency Distribution00:55

Relative Frequency Distribution

13.4K
A relative frequency distribution is the proportion or fraction of times a value occurs in a data set. To find the relative frequencies, one can divide each frequency by the total number of data points in the sample. It is very similar to a regular frequency distribution, except that instead of reporting how many data values fall in a class, a relative frequency distribution reports the fraction of data values that fall in a class. These fractions or proportions are called relative frequencies...
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Cumulative Frequency Distribution01:04

Cumulative Frequency Distribution

8.4K
A cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
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Synthesis of Phase-shift Nanoemulsions with Narrow Size Distributions for Acoustic Droplet Vaporization and Bubble-enhanced Ultrasound-mediated Ablation
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Distributed multi-parameter sensing utilizing Brillouin frequency shifts contributed by multiple acoustic modes in

Chen Xing, Changjian Ke, Zhen Guo

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    A novel optical-fiber sensor uses multiple acoustic modes in stimulated Brillouin scattering (SBS) to simultaneously measure temperature and strain in standard single-mode fiber (SMF). This method enables accurate, distributed sensing over long ranges.

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    Area of Science:

    • Optoelectronics and Photonics
    • Fiber Optic Sensing Technology
    • Materials Science for Optical Fibers

    Background:

    • Distributed sensing of temperature and strain is crucial for structural health monitoring and various industrial applications.
    • Standard single-mode fibers (SMF) are widely available but typically support only a single acoustic mode for sensing.
    • Existing methods often struggle with simultaneous and accurate discrimination between temperature and strain measurements.

    Purpose of the Study:

    • To propose and experimentally demonstrate a multi-parameter optical-fiber sensor capable of distributed temperature and strain measurement.
    • To utilize multiple acoustic modes within the stimulated Brillouin scattering (SBS) effect in standard single-mode fiber (SMF).
    • To investigate the feasibility of achieving discriminative measurement of temperature and strain by analyzing different acoustic modes.

    Main Methods:

    • Theoretical analysis of Brillouin gain spectrum (BGS) properties related to guided optical and acoustic modes by manipulating fiber doping and refractive index profiles.
    • Simulation of multiple acoustic mode excitation in SMF and analysis of their impact on the BGS.
    • Experimental validation using two different standard single-mode fibers (SSMF), analyzing their multi-peak BGS and Brillouin frequency shifts.

    Main Results:

    • Simulations confirmed the excitation of multiple acoustic modes in SMF, resulting in multi-peak BGS.
    • Unequal temperature and strain sensitivities were observed for different acoustic modes, proving discriminative measurement capability.
    • Experimental results successfully demonstrated the discrimination of temperature and strain, with a specific SSMF achieving high accuracy (0.98 °C, 19.6 με) over a 20 km range.

    Conclusions:

    • The proposed multi-parameter sensor based on multiple acoustic modes in SBS is effective for distributed temperature and strain measurement using SSMF.
    • Fiber structure parameters significantly influence measurement accuracy, highlighting the importance of tailored fiber design.
    • The demonstrated technique offers a promising approach for advanced fiber optic sensing applications requiring simultaneous environmental parameter monitoring.