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Pseudorandom number generators | Computer Science | Khan Academy
 
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Random vs. Pseudorandom Number Generators Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/modern-crypt/v/the-fundamental-theorem-of-arithmetic-1?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/crypt/v/perfect-secrecy?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 162806 Khan Academy Labs
Pseudo Random Numbers and Stream Ciphers (CSS322, L9, Y14)
 
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Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching
Views: 2501 Steven Gordon
cryptography - Pseudorandom Generators
 
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Cryptography To get certificate subscribe: https://www.coursera.org/learn/cryptography ======================== Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/
Views: 2697 intrigano
Pseudo Random Number Generator - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 9303 Udacity
Pseudo Random Number Generators (CSS322, Lecture 7, 2013)
 
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Pseudo random number generators; Linear Congruential Generator. Lecture 7 of CSS322 Security and Cryptography at Sirindhorn International Institute of Technology, Thammasat University. Given on 12 December 2013 at Bangkadi, Pathumthani, Thailand by Steven Gordon. Course material via: http://sandilands.info/sgordon/teaching
Views: 22337 Steven Gordon
Lecture 3: Stream Ciphers, Random Numbers and the One Time Pad by Christof Paar
 
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For slides, a problem set and more on learning cryptography, visit www.crypto-textbook.com
How to Generate Pseudorandom Numbers | Infinite Series
 
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Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: https://to.pbs.org/donateinfi What is a the difference between a random and a pseudorandom number? And what can pseudo random numbers allow us to do that random numbers can't? Tweet at us! @pbsinfinite Facebook: facebook.com/pbsinfinite series Email us! pbsinfiniteseries [at] gmail [dot] com Previous Episode How many Cops to catch a Robber? | Infinite Series https://www.youtube.com/watch?v=fXvN-pF76-E Computers need to have access to random numbers. They’re used to encrypt information, deal cards in your game of virtual solitaire, simulate unknown variables -- like in weather prediction and airplane scheduling, and so much more. But How can a computer possibly produce a random number? Written and Hosted by Kelsey Houston-Edwards Produced by Rusty Ward Graphics by Ray Lux Assistant Editing and Sound Design by Mike Petrow Made by Kornhaber Brown (www.kornhaberbrown.com) Special Thanks to Alex Townsend Big thanks to Matthew O'Connor and Yana Chernobilsky who are supporting us on Patreon at the Identity level! And thanks to Nicholas Rose and Mauricio Pacheco who are supporting us at the Lemma level!
Views: 126444 PBS Infinite Series
cryptography - Pseudorandom Functions and Block Ciphers
 
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Cryptography To get certificate subscribe: https://www.coursera.org/learn/cryptography ======================== Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu ============================ Youtube channel: https://www.youtube.com/user/intrigano ============================ https://scsa.ge/en/online-courses/ https://www.facebook.com/cyberassociation/
Views: 3823 intrigano
Coding Math: Episode 51 - Pseudo Random Number Generators Part I
 
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Back to School Special. This short series will discuss pseudo random number generators (PRNGs), look at how they work, some algorithms for PRNGs, and how they are used. Support Coding Math: http://patreon.com/codingmath Source Code: https://jsbin.com/nifutup/1/edit?js,output Earlier Source Code: http://github.com/bit101/codingmath
Views: 28569 Coding Math
Prng Implementation - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 3651 Udacity
CSE571-11-07: Pseudorandom Number Generation and Stream Ciphers
 
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Audio/Video Recording of Professor Raj Jain's class lecture on Pseudorandom Number Generation and Stream Ciphers. It covers Pseudo Random Numbers, A Sample Generator, Terminology, Linear-Congruential Generators, Blum Blum Shub Generator, Random & Pseudorandom Number Generators, Using Block Ciphers as PRNGs, ANSI X9.17 PRG, Natural Random Noise, Stream Ciphers, RC4, RC4 Key Schedule, RC4 Encryption, RC4
Views: 4954 Raj Jain
Section 4.5 Pseudorandom Numbers
 
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I do an example of finding pseudorandom numbers.
Views: 1201 Michael Venn
LFSR 1
 
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An introduction to linear feedback shift registers, and their use in generating pseudorandom numbers for Vernam ciphers. For more cryptography, subscribe to my channel: https://www.youtube.com/channel/UC1KV5WfubHTV6E7sVCnTidw
Views: 33659 Jeff Suzuki
What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean?
 
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What is PSEUDORANDOM NUMBER GENERATOR? What does PSEUDORANDOM NUMBER GENERATOR mean? PSEUDORANDOM NUMBER GENERATOR meaning - PSEUDORANDOM NUMBER GENERATOR definition - PSEUDORANDOM NUMBER GENERATOR explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. The PRNG-generated sequence is not truly random, because it is completely determined by a relatively small set of initial values, called the PRNG's seed (which may include truly random values). Although sequences that are closer to truly random can be generated using hardware random number generators, pseudorandom number generators are important in practice for their speed in number generation and their reproducibility. PRNGs are central in applications such as simulations (e.g. for the Monte Carlo method), electronic games (e.g. for procedural generation), and cryptography. Cryptographic applications require the output not to be predictable from earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Good statistical properties are a central requirement for the output of a PRNG. In general, careful mathematical analysis is required to have any confidence that a PRNG generates numbers that are sufficiently close to random to suit the intended use. John von Neumann cautioned about the misinterpretation of a PRNG as a truly random generator, and joked that "Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin." A PRNG can be started from an arbitrary initial state using a seed state. It will always produce the same sequence when initialized with that state. The period of a PRNG is defined thus: the maximum, over all starting states, of the length of the repetition-free prefix of the sequence. The period is bounded by the number of the states, usually measured in bits. However, since the length of the period potentially doubles with each bit of "state" added, it is easy to build PRNGs with periods long enough for many practical applications. If a PRNG's internal state contains n bits, its period can be no longer than 2n results, and may be much shorter. For some PRNGs, the period length can be calculated without walking through the whole period. Linear Feedback Shift Registers (LFSRs) are usually chosen to have periods of exactly 2n-1. Linear congruential generators have periods that can be calculated by factoring. Although PRNGs will repeat their results after they reach the end of their period, a repeated result does not imply that the end of the period has been reached, since its internal state may be larger than its output; this is particularly obvious with PRNGs with a one-bit output. Most PRNG algorithms produce sequences which are uniformly distributed by any of several tests. It is an open question, and one central to the theory and practice of cryptography, whether there is any way to distinguish the output of a high-quality PRNG from a truly random sequence, knowing the algorithms used, but not the state with which it was initialized. The security of most cryptographic algorithms and protocols using PRNGs is based on the assumption that it is infeasible to distinguish use of a suitable PRNG from use of a truly random sequence. The simplest examples of this dependency are stream ciphers, which (most often) work by exclusive or-ing the plaintext of a message with the output of a PRNG, producing ciphertext. The design of cryptographically adequate PRNGs is extremely difficult, because they must meet additional criteria (see below). The size of its period is an important factor in the cryptographic suitability of a PRNG, but not the only one. A PRNG suitable for cryptographic applications is called a cryptographically secure PRNG (CSPRNG). A requirement for a CSPRNG is that an adversary not knowing the seed has only negligible advantage in distinguishing the generator's output sequence from a random sequence. In other words, while a PRNG is only required to pass certain statistical tests, a CSPRNG must pass all statistical tests that are restricted to polynomial time in the size of the seed. Though a proof of this property is beyond the current state of the art of computational complexity theory, strong evidence may be provided by reducing the CSPRNG to a problem that is assumed to be hard, such as integer factorization. In general, years of review may be required before an algorithm can be certified as a CSPRNG.
Views: 3477 The Audiopedia
NodeJS Crypto Create Random Number
 
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Learn how to Create Random Values using Crypto Module in NodeJS.
Views: 1885 DevNami
Substitution-Permutation Networks, Pseudorandom Function ...
 
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Talk at crypto 2012. Authors: Eric Miles, Emanuele Viola. See http://www.iacr.org/cryptodb/data/paper.php?pubkey=24289
Views: 7585 TheIACR
PRNG Implementation Solution - Applied Cryptography
 
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This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.
Views: 1433 Udacity
6.875 (Cryptography) L7: Pseudorandom Functions
 
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MIT's Spring 2018 Cryptography & Cryptanalysis Class (6.875) Prof. Vinod Vaikuntanathan
Views: 268 Andrew Xia
PRNG Part 1
 
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Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)
Applied Cryptography: Random Numbers in Java (1/5)
 
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Previous video: https://youtu.be/KuthrX4G1ss Next video: https://youtu.be/FhrsUCICh-Y
Views: 1046 Leandro Junes
Cryptography stream ciphers and pseudo random generators
 
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Cryptography Stream ciphers and pseudo random generators To get certificate subscribe: https://www.coursera.org/learn/crypto Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWYosqucluZghEVjUkopdD1e About this course: Cryptography is an indispensable tool for protecting information in computer systems. In this course you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic. We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key.
Views: 692 intrigano
Cryptographically secure pseudorandom number generator Top # 7 Facts
 
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Cryptographically secure pseudorandom number generator Top # 7 Facts
Views: 93 Duryodhan Trivedi
Pseudo Random Number Generators - Peter Faiman
 
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Peter Faiman White Hat VP, talks about pseudo-random number generators (PRNGs), random number quality, and the importance of unpredictable random numbers to cryptography.
Views: 3066 White Hat Cal Poly
Proofs in Cryptography: Lecture 5 Pseudo Random Generators
 
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Proofs in Cryptography Lecture 5 Pseudo Random Generators ALPTEKİN KÜPÇÜ Assistant Professor of Computer Science and Engineering Koç University http://crypto.ku.edu.tr
Views: 2965 KOLT KU
Random Numbers with Block Ciphers (CSS441, L09, Y15)
 
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PRNGs with block ciphers in counter and OFB mode; ANSI X9.17; RC4. Course material via: http://sandilands.info/sgordon/teaching
Views: 1417 Steven Gordon
Pseudorandom Number Generation and Stream Ciphers
 
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Fundamental concepts of Pseudorandom Number Generation are discussed. Pseudorandom Number Generation using a Block Cipher is explained. Stream Cipher & RC4 are presented.
Views: 1286 Scholartica Channel
Coding Math: Episode 52 - Pseudo Random Number Generators, Part II
 
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This time we look at a couple of existing PRNG libraries available in JavaScript, and look at some examples of how PRNGs can be used in cryptography, games, and generative art. Support Coding Math: http://patreon.com/codingmath Source Code: Crypto: http://jsbin.com/kipequk/2/edit?js,console Landscape: http://jsbin.com/zizeje/1/edit?js,output Circles: http://jsbin.com/zizeje/2/edit?js,output
Views: 6163 Coding Math
5 Pseudo-random Number Generators (PRNGs) हिन्दी Information Security HINDI
 
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http://www.atozsky.com/ https://www.facebook.com/atozsky.computer/ All credits goes to NIELIT, Delhi INDIA
Views: 518 AtoZ COMPUTER
Applied Cryptography: Random Numbers (2/2)
 
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Previous video: https://youtu.be/g3iH74XFaT0 Next video:
Views: 1509 Leandro Junes
Pseudo Random Number Generators (CSS441, L08, Y15)
 
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True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching
Views: 3443 Steven Gordon
How does randomness work in Random Number Generators? (Cryptography Crashcourse Part 3)
 
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Part 1 of the course: https://youtu.be/GGILQcO843s Part 2 of the course: https://youtu.be/4RnqrLeY4xY Book: Understanding Cryptography https://www.amazon.com/Understanding-Cryptography-Textbook-Students-Practitioners/dp/3642041000/ref=as_li_ss_tl?ie=UTF8&qid=1541146284&sr=8-1&keywords=Understanding+Cryptography:+A+Textbook+for+Students+and+Practitioners&linkCode=sl1&tag=julianhosp-20&linkId=8e14aad9056003d3eefcacb57c2e0b73&language=en_US ---------- New to cryptocurrencies? You might want to read this book first! http://cryptofit.community/cryptobook If you liked the video, subscribe to my channel, give a "thumbs up" and share this video to make the world together #cryptofit :) ► Subscribe: https://www.youtube.com/channel/UCseN... ► Cryptocurrency Exchange: https://www.binance.com/?ref=11272739 ► Hardware Wallet: http://www.julianhosp.com/hardwallet ► Ruben's Trinkgeld Adressen: Bitcoin: 3MNWaot64Fr1gRGxv4YzHCKAcoYTLXKxbc Litecoin: MTaGwg5EhKooonoVjDktroiLqQF6Rvn8uE --------------- ► Completely NEW? What is Blockchain, Bitcoin and Co? Get this book from me: https://www.amazon.com/Cryptocurrenci... ► Join our Facebook group: https://www.facebook.com/groups/crypt... ► iTunes Podcast: https://itunes.apple.com/sg/podcast/t... ► My website: http://www.julianhosp.com ---------------- My name is Dr. Julian Hosp or just Julian. My videos are about Bitcoin, Ethereum, Blockchain and crypto currencies in general, to avoid scam, rip-off and fraud especially in mining. I'm talking about how you can invest wisely and do it rationally and simply. My ultimate goal is to make people all around the world #CRYPTOFIT. I.E fit for this new wave of decentralization and blockchain. Have fun! ► Follow me here and stay in touch: Facebook: www.facebook.com/julianhosp/ Twitter: https://twitter.com/julianhosp Instagram: https://www.instagram.com/julianhosp/ Linkedin: https://www.linkedin.com/julianhosp
Views: 701 Dr. Julian Hosp
Is Anything Truly Random?
 
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In 2012, scientists developed a system to predict what number a rolled die would land on. Is anything truly random or is it all predictable? Can Game Theory Help A Presidential Candidate Win? - http://bit.ly/2bMqILU Sign Up For The Seeker Newsletter Here - http://bit.ly/1UO1PxI Read More: On Fair And Randomness http://www.sciencedirect.com/science/article/pii/S0890540109001369 "We investigate the relation between the behavior of non-deterministic systems under fairness constraints, and the behavior of probabilistic systems. To this end, first a framework based on computable stopping strategies is developed that provides a common foundation for describing both fair and probabilistic behavior. On the basis of stopping strategies it is then shown that fair behavior corresponds in a precise sense to random behavior in the sense of Martin-Löf's definition of randomness." Predicting A Die Throw http://phys.org/news/2012-09-die.html "Vegas, Monte Carlo, and Atlantic City draw people from around the world who are willing to throw the dice and take their chances. Researchers from the Technical University of Lodz, Poland, have spotted something predictable in the seemingly random throw of the dice." HTG Explains: How Computers Generate Random Numbers http://www.howtogeek.com/183051/htg-explains-how-computers-generate-random-numbers/ "Computers generate random number for everything from cryptography to video games and gambling. There are two categories of random numbers - "true" random numbers and pseudorandom numbers - and the difference is important for the security of encryption systems." ____________________ DNews is dedicated to satisfying your curiosity and to bringing you mind-bending stories & perspectives you won't find anywhere else! New videos daily. Watch More DNews on Seeker http://www.seeker.com/show/dnews/ Subscribe now! http://www.youtube.com/subscription_center?add_user=dnewschannel DNews on Twitter http://twitter.com/dnews Trace Dominguez on Twitter https://twitter.com/tracedominguez DNews on Facebook https://facebook.com/DiscoveryNews DNews on Google+ http://gplus.to/dnews Discovery News http://discoverynews.com Sign Up For The Seeker Newsletter Here: http://bit.ly/1UO1PxI Special thanks to Jules Suzdaltsev for hosting DNews! Check Jules out on Twitter: https://twitter.com/jules_su
Views: 190422 Seeker
How Machines Generate Random Numbers with Time
 
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Pseudorandom number generators are explained using John Von Neumann's middle squares method. Machines can't roll dice so they do a trick to generate randomness - they grow randomness. The middle squares method is explained from a computer science perspective using clocks as seeds. This is a clip from Art of the Problem episode #1. This clip features original music from Hannah Addario-Berry
Views: 40277 Art of the Problem
Pseudorandom Generators I
 
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Raghu Meka, UCLA https://simons.berkeley.edu/talks/pseudorandom-generators-1 Pseudorandomness Boot Camp
Views: 1114 Simons Institute
6.875 (Cryptography) L6: Pseudorandom Generators
 
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Spring 2018 Cryptography & Cryptanalysis Prof. Vinod Vaikuntanathan
Views: 261 Andrew Xia
COSIC Seminar - Entropy Sources For Cryptographic Random Number Generation (John Kelsey)
 
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Random number generation underlies all of cryptography—if you can’t generate good random numbers, you probably can’t do any useful crypto. In this tutorial, I will go over how cryptographic random number generation works, and then zoom in on entropy sources—the ultimate source of unpredictability in any cryptographic RNG. I’ll discuss the problems of designing and analyzing an entropy source, and the approach we’ve used in SP 800-90B for specifying how they should work and how labs should try to validate them. I’ll also talk about the related problem of extractors, the functions that process entropy-bearing inputs and yield some kind of seed for a deterministic RNG.
Random Numbers (How Software Works)
 
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How Software Works is a book and video series explaining the magic behind software encryption, CGI, video game graphics, and a lot more. The book uses plain language and lots of diagrams, so no technical or programming background is required. Come discover what's really happening inside your computer! This episode is about random numbers--why software needs them, why they can't really make them, and why that's okay. Learn more about the book... - At the Amazon page (http://amzn.to/1mZ276M). - At my publisher (http://www.nostarch.com/howsoftwareworks) - At my site (http://www.vantonspraul.com/HSW). If you'd like to contact me visit my site (http://vantonspraul.com), or just leave a comment below. Suggestions for future topics are welcome!
Views: 17081 V. Anton Spraul
Minecraft Redstone Tutorial 16 Bit Linear Feedback Shift Register | Pseudorandom Number Generator
 
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www.facebook.com/mrcrucialclothing - visit and post for chances to win free art&apparel How to Build a 16 Bit Fibonacci LFSR in Minecraft. THERE IS a complete Tutorial on how to Build the Shift Register we are working from. This Ciruit Generates pseudorandom numbers in minecraft. We Start out with a 16 BIT Serail Out Shift Register, Shifting Right to Left. The most commonly used linear function of single bits is XOR. Thus, an LFSR is most often a shift register whose input bit is driven by the exclusive-or (XOR) of some bits of the overall shift register value. The initial value of the LFSR is called the seed, and because the operation of the register is deterministic, the stream of values produced by the register is completely determined by its current (or previous) state. Likewise, because the register has a finite number of possible states, it must eventually enter a repeating cycle. However, an LFSR with a well-chosen feedback function can produce a sequence of bits which appears random and which has a very long cycle. The 16 Bit Register: This Shift Register is a cascade of 1 Wide D Flip-Flops, sharing the same clock, which has the output of any one but the last flip-flop connected to the "data" input of the next one in the chain, resulting in a circuit that shifts by one position, when enabled to do so by a transition of the clock input. Video Tutorial link below.. Fibonacci LFSR: The bit positions that affect the next state are called the TAPS. [16,14,13,11]. The rightmost bit of the LFSR is called the output bit. The taps are XOR'd sequentially with the output bit and then fed back into the leftmost bit. The sequence of bits in the rightmost position is called the output stream. The sequence of numbers generated by an LFSR can be considered a binary numeral system just as valid as Gray code or the natural binary code. The arrangement of taps for feedback in an LFSR can be expressed in finite field arithmetic as a polynomial mod 2. This means that the coefficients of the polynomial must be 1's or 0's. This is called the feedback polynomial or characteristic polynomial. Pseudorandomness: A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG), is an algorithm for generating a sequence of numbers that approximates the properties of random numbers. The sequence is not truly random in that it is completely determined by a relatively small set of initial values, called the PRNG's state, which includes a truly random seed. pseudorandom numbers are important in practice for their speed in number generation and their reproducibility, and they are thus central in applications such as simulations, in cryptography, and in procedural generation. Good statistical properties are a central requirement for the output of a PRNG, and common classes of suitable algorithms include linear congruential generators, lagged Fibonacci generators, and linear feedback shift registers. Applications: generating pseudo-random numbers, pseudo-noise sequences, fast digital counters, and whitening sequences, Rave House. Shift Register Tutorial: http://www.youtube.com/watch?v=LgAZ5iRsrLM Linear Feedback Shift Register: http://en.wikipedia.org/wiki/Linear_feedback_shift_register Pseudorandomness: http://en.wikipedia.org/wiki/Pseudorandom_number_generator
Views: 20563 MRCRUCIAL
Linear Congruential Random Number Generators
 
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Random Number Generators (RNGs) are useful in many ways. This video explains how a simple RNG can be made of the 'Linear Congruential Generator' type. This type of generator is not very robust, but it is quick and easy to program with little memory requirement.
Views: 25035 physics qub
Blum Blum Shub Random Number Generator
 
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I should have said p = 3 (mod 4) and q = 3 (mod 4) https://asecuritysite.com/encryption/blum
Views: 184 Bill Buchanan OBE
Math for Game Developers - Making Randomness (Pseudorandom Number Generators)
 
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How random number generators work and how to get good numbers out of them. Find the source code here: https://github.com/BSVino/MathForGameDevelopers/tree/probability-random New video every Thursday. Question? Leave a comment below, or ask me on Twitter: https://twitter.com/VinoBS EXERCISES: 1. Modify the function to pass the current time into the random number seed and verify that a new sequence is always produced. 2. Create a pseudorandom number generator that generates only 1's and 0's, false and true values. 3. How would the difference in probabilities be between outputs if there were only 2^8 input values and 100 output values? What about if there were 2^8 input values and 128 output values? 4. Tricky: How would you design a pseudorandom number generator over arbitrary output ranges where all of the output values are exactly equally likely?
Views: 5799 Jorge Rodriguez
A Quantum Random Number Generator for cryptographic applications
 
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This project presents a quantum random number generator for a multitude of cryptographic applications based on the alpha decay of a household radioactive source.
Views: 688 BTYoungScientists
Digital Logic - Linear Feedback Shift Register
 
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This is another video in my series of videos where I talk about Digital Logic. In this video, I show how you can make a Linear Feedback Shift Register, which is a circuit that allows you to generate pseudo-random numbers.
Views: 41900 Robot Brigade
EC-VOPRF (Elliptic Curve Verifiable Oblivious Pseudo-Random Function)
 
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Medium: https://medium.com/asecuritysite-when-bob-met-alice/for-control-of-the-internet-its-bots-v-humans-solving-captchas-in-a-crediable-and-secret-way-d947c21ec62a?source=friends_link&sk=871062acea1a89d5c26f538cf6744011 Coding: https://asecuritysite.com/encryption/vop
Views: 111 Bill Buchanan OBE