In geometry, the mean width is a measure of the "size" of a body; see Hadwiger's theorem for more about the available measures of bodies. In dimensions, one has to consider -dimensional hyperplanes perpendicular to a given direction in , where is the n-sphere (the surface of a -dimensional sphere).The "width" of a body in a given direction is the distance between the closest pair of such planes, such that the body is entirely in between the two hyper planes (the planes only intersect with the boundary of the body). The mean width is the average of this "width" over all in . More formally, define a compact body B as being equivalent to set of points in its interior plus the points on the boundary (here, points denote elements of ). The support function of body B is defined as where is a direction and denotes the usual inner product on . The mean width is then where is the -dimensional volume of .Note, that the mean width can be defined for any body (that is compact), but it is mostuseful for convex bodies (that is bodies, whose corresponding set is a convex set). (Wikipedia).
From playlist Dimensions Arabe/Arabic / العربية
Ex: Find the Dimensions of a Bookcase Using a Linear Equation
This video provides an example of how to determine the dimensions of a bookcase given the relationship between the width and height given the amount of material used. Complete Video Library: http://www.mathispower4u.com Search Videos: http://www.mathispower4u.wordpress.com
From playlist Applications: Solving Linear Equations in One Variable
From playlist Dimensions Arabe/Arabic / العربية
From playlist Dimensions Arabe/Arabic / العربية
From playlist Dimensions Arabe/Arabic / العربية
From playlist Dimensions Arabe/Arabic / العربية
From playlist Dimensions Arabe/Arabic / العربية
What does resolution mean (for display devices)
In this tutorial we explain what the term resolution means. If you're not familiar with the concept of pixels, have a look at my previous tutorial here: https://www.youtube.com/watch?v=PjvLKED-byg
From playlist Mobile web design
Laser Fundamentals I | MIT Understanding Lasers and Fiberoptics
Laser Fundamentals I Instructor: Shaoul Ezekiel View the complete course: http://ocw.mit.edu/RES-6-005S08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT Understanding Lasers and Fiberoptics
HW#5 Page 17 #10 Predicting Length and Width from Perimeter
In this problem, we look at the pattern of a rectangle with perimeter of 70
From playlist Middle School This Year
Highlight: Pitfall style game 13 - The player can now run through the neverending maze
Brooks is building a Pitfall clone called Jungle (until he finds a better name). Don't know what pitfall is? Watch this gameplay of the original game at https://www.youtube.com/watch?v=pslbO6Fddhw&t=391s. On this stream Brooks Makes the player run to the right and left fully animated usin
From playlist Pitfall clone in web assembly (Rust)
AP Calculus AB: Lesson 6.2 Riemann and Trapezoidal Sums
AP Calculus AB Unit 6: Integration Lesson 2: Riemann and Trapezoidal Sums
From playlist AP Calculus AB
Averages from a Histogram (New GCSE Topic!) 🤯 | Difficult Mean, Median & Quartiles | Grade 9 | TGMT
A video revising the techniques and strategies for finding averages from histograms (Higher Only). This video is part of the Statistics module in GCSE maths, see my other videos below to continue with the series. Join this channel to get access to perks: https://www.youtube.com/channel/U
From playlist Grade 9 Maths Revision Series
Testing Sparsity over Known and Unknown Bases by Arnab Bhattacharyya
Statistical Physics Methods in Machine Learning DATE:26 December 2017 to 30 December 2017 VENUE:Ramanujan Lecture Hall, ICTS, Bengaluru The theme of this Discussion Meeting is the analysis of distributed/networked algorithms in machine learning and theoretical computer science in the "th
From playlist Statistical Physics Methods in Machine Learning
Eureka Math Grade 4 Module 3 Lesson 21
EngageNY/Eureka Math Grade 4 Module 3 Lesson 21 For more Eureka Math (EngageNY) videos and other resources, please visit http://EMBARC.online PLEASE leave a message if a video has a technical difficulty (audio separating from the video, writing not showing up, etc). Occasionally, Explain
From playlist Eureka Math Grade 4 Module 3
Eureka Math Grade 3 Module 7 Lesson 21
EngageNY/Eureka Math Grade 3 Module 7 Lesson 21 For more Eureka Math (EngageNY) videos and other resources, please visit http://EMBARC.online PLEASE leave a message if a video has a technical difficulty (audio separating from the video, writing not showing up, etc). Occasionally, Explain
From playlist Eureka Math Grade 3 Module 7
Histograms | Revision for maths GCSE and IGCSE
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From playlist GCSE Maths Revision | Statistics and Probability
Lecture 16: TC of perfect rings
In this video, we compute TC, CT^- and TP of perfect rings of characteristic p. In order to do that we also have to discuss the Witt vectors and their universal property. Feel free to post comments and questions at our public forum at https://www.uni-muenster.de/TopologyQA/index.php?qa=t
From playlist Topological Cyclic Homology
From playlist Miscellaneous