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Algorithms Behind Space Missions ~xRay Pixy

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Learn different algorithms used in Space Missions. Video Link Video Chapters: Algorithms Behind Space Missions 00:00 Introduction 00:52 Space Missions 04:26 Space Missions Challenges 07:04 Algorithms Used in Space Missions 10:36 Optimization Techniques 11:44 Conclusion  NASA conducts space missions to explore the universe for various scientific, technological, and practical reasons: Understanding Our Place in the Universe Search for Life Beyond Earth Studying Earth from Space Advancing Technology Supporting Human Exploration Resource Utilization Inspiring Humanity Examples of NASA Space Missions Apollo Program: Sent humans to the Moon (1969–1972). Mars Rovers (Spirit, Opportunity, Perseverance): Explored Mars' surface and geology. Voyager Missions: Studied the outer planets and interstellar space. Hubble Space Telescope: Captured breathtaking images of the universe. International Space Station (ISS): Supports research in microgravity and international collaboration. Different ...
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introduction to Merkle-Hellman knapsacks Algorithm

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  Ralph Merkle and Martin Hellman Developed the first algorithm for Public Key for public-key Encryption, called t he knapsacks Algorithm. This algorithm is based on the Knapsack Problem .[this is actually a simple problem] Given a pile of Items, each with different weights, is it possible to put some of them in a bag (i.e., knapsack) in such a way that the knapsack has a certain weight? If M1, M2, M3,.., Mn are given values and S is the Sum, Find out bi so that:            S = b1M1 + b2M2 + ... + bnMn Each bi can be 0 or 1.  [1 indicates the item is in the knapsack and 0 indicates that it is not]. A Block of Plain text equal in length to the number of items in the pile would select the items in the knapsack. The ciphertext is the resulting sum.  For example: if the knapsack is 1, 7, 8, 12, 14, 20 then the plain text and the resulting Ciphertext is shown as: 
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3d Display Methods in Computer Graphics

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 3D Display Methods 3D Projection Q. What is 3D Projection/Graphical Projection? It is a design technique used to display 3D objects on a 2D surface. Why we are using a 2D surface to display 3D objects? Because all graphics devices are 2D [like Monitor Screen]. When we want to draw 3D objects on a computer monitor. Convert the world coordinates into screen coordinates [i.e., project 3D objection on the 2D plane]. Q. What is the Projection? Process of representing 3D objects into 2D. [i.e., shadow of object]. Q. How an object is projected?  By projecting each of its points [create a map of points]. There are infinite points in Object. Take projection of only corner points of an object on a 2D plane than join these projected points by a straight line in the 2D plane. Q. What is a Parallel Projection? Projection of point in 3D space. Parallel Projection is a method for generating a view of a solid object. Project point on the object surface along parallel lines onto the display p...
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Nicholl - Lee Nicholl (NLN) Line Clipping in Computer Graphics

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 In the Cohen-Sutherland method, multiple intersections may be calculated along the path of a single line before an intersection on the clipping rectangle or line is completely rejected.  In Nicholl - Lee Nicholl Line Clipping, Extra intersection calculations are eliminated.  ADVANTAGE OF Nicholl - Lee Nicholl (NLN) Line Clipping 1.) The  number of clipping points is reduced to one, compared to other algorithms that may require two or more clipping.  2.)  Nicholl - Lee Nicholl (NLN) Line Clipping is FAST. Clipping algorithms for Circle, Curved boundary clipping regions are SLOWER because intersections calculations involve nonlinear curve equations.  3.)    Nicholl - Lee Nicholl (NLN) Line Clipping avoids multiple clipping of Line segments.  4.) As Compare to Cohen Sutherland and Liang-Barsy  Nicholl - Lee Nicholl Line Clipping performed fewer comparisons.    How Nicholl - Lee Nicholl Line Clipping works?  Nicholl...
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PSO (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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