Cryptography
To get certificate subscribe: https://www.coursera.org/learn/cryptography
========================
Playlist URL: https://www.youtube.com/playlist?list=PL2jykFOD1AWb07OLBdFI2QIHvPo3aTTeu
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Youtube channel: https://www.youtube.com/user/intrigano
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https://scsa.ge/en/online-courses/
https://www.facebook.com/cyberassociation/

Views: 2694
intrigano

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: 162765
Khan Academy Labs

Raghu Meka, UCLA
https://simons.berkeley.edu/talks/pseudorandom-generators-1
Pseudorandomness Boot Camp

Views: 1114
Simons Institute

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: 3817
intrigano

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

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

Views: 12469
Eddie Woo

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 3640
Udacity

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 9297
Udacity

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: 28548
Coding Math

Previous video: https://youtu.be/6ro3z2pTiqI
Next video: https://youtu.be/KuthrX4G1ss

Views: 4696
Leandro Junes

Raghu Meka, UCLA
https://simons.berkeley.edu/talks/pseudorandom-generators-IV
Pseudorandomness Boot Camp

Views: 231
Simons Institute

MIT's Spring 2018 Cryptography & Cryptanalysis Class (6.875)
Prof. Vinod Vaikuntanathan

Views: 268
Andrew Xia

Raghu Meka, UCLA
https://simons.berkeley.edu/talks/pseudorandom-generators-II
Pseudorandomness Boot Camp

Views: 238
Simons Institute

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: 3064
White Hat Cal Poly

Pseudo random number generators; stream ciphers. Course material via: http://sandilands.info/sgordon/teaching

Views: 2501
Steven Gordon

Spring 2018 Cryptography & Cryptanalysis
Prof. Vinod Vaikuntanathan

Views: 261
Andrew Xia

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: 126400
PBS Infinite Series

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: 22329
Steven Gordon

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

Views: 1433
Udacity

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: 4953
Raj Jain

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: 6159
Coding Math

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: 3474
The Audiopedia

Part 1 of a 3 part lesson on Pseudo Random Number Generators (PRNGs)

Views: 979
Mobile Computer Science Principles

Views: 7743
Internetwork Security

Views: 2787
Udacity

Pseudorandom generators (definitions and constructions; the hybrid method), a lecture by Benny Applebaum.
The topic of the 4th Annual Bar-Ilan Winter School on Cryptography
held in January 2014, was Symmetric Encryption in Theory and in Practice.
The winter school studied symmetric encryption in theory and in practice, and included a study of the theoretical foundations of symmetric encryption on the one hand, and practical constructions and cryptanalysis on the other hand.
As every year, the event organizers were Prof. Yehuda Lindell and Prof. Benny Pinkas, of BIU's Department of Computer Science.
This year,the Winter School featured speakers from such institutions as the Royal Holloway at the University of London , and the University of Wisconsin - Madison.
For all videos of this playlist: https://www.youtube.com/playlist?list=PLXF_IJaFk-9BmvxWhnxPId32CPJhVtU6D
4th Annual Bar-Ilan Winter School on Cryptography:
http://crypto.biu.ac.il/winterschool2014/
Prof. Lindell's Lab
http://www1.biu.ac.il/indexE.php?id=8043&pt=30&pid=7711&level=2&cPath=7702,7711,8043
Prof. Pinkas' Lab
http://www1.biu.ac.il/indexE.php?id=8046&pt=30&cPath=7702,7711,8046
Dept. of Computer Science: http://cs.biu.ac.il/en/
Bar-Ilan University: http://www1.biu.ac.il/en

Views: 713
barilanuniversity

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: 33603
Jeff Suzuki

True and pseudo random numbers; Linear Congruential Generator. Course material via: http://sandilands.info/sgordon/teaching

Views: 3441
Steven Gordon

For slides, a problem set and more on learning cryptography, visit www.crypto-textbook.com

Views: 103597
Introduction to Cryptography by Christof Paar

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

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: 24990
physics qub

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
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New to cryptocurrencies? You might want to read this book first!
http://cryptofit.community/cryptobook
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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!
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Views: 699
Dr. Julian Hosp

Twenty minute introduction to randomness and pseudorandom number generators, with demos. The New Mexico CS for All project is teaching computational thinking and programming.
Production supported by the National Science Foundation, award # CNS 1240992

Views: 28123
Dave Ackley

Lectures on Introduction to Cryptography.

Views: 60
Wobbly Bit

Cryptographically secure pseudorandom number generator Top # 7 Facts

Views: 93
Duryodhan Trivedi

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

Views: 225
Decision modeling

Previous video: https://youtu.be/g3iH74XFaT0
Next video:

Views: 1509
Leandro Junes

In cryptography, a zero-knowledge proof or zero-knowledge protocol is a method by which one party (the prover) can prove to another party (the verifier) that they know a value x, without conveying any information apart from the fact that they know the value x. The essence of zero-knowledge proofs is that it is trivial to prove that one possesses knowledge of certain information by simply revealing it; the challenge is to prove such possession without revealing the information itself or any additional information.
If proving a statement requires that the prover possess some secret information, then the verifier will not be able to prove the statement to anyone else without possessing the secret information. The statement being proved must include the assertion that the prover has such knowledge, but not the knowledge itself. Otherwise, the statement would not be proved in zero-knowledge because it provides the verifier with additional information about the statement by the end of the protocol. A zero-knowledge proof of knowledge is a special case when the statement consists only of the fact that the prover possesses the secret information.
Interactive zero-knowledge proofs require interaction between the individual (or computer system) proving their knowledge and the individual validating the proof.
A protocol implementing zero-knowledge proofs of knowledge must necessarily require interactive input from the verifier. This interactive input is usually in the form of one or more challenges such that the responses from the prover will convince the verifier if and only if the statement is true, i.e., if the prover does possess the claimed knowledge. If this were not the case, the verifier could record the execution of the protocol and replay it to convince someone else that they possess the secret information. The new party's acceptance is either justified since the replayer does possess the information (which implies that the protocol leaked information, and thus, is not proved in zero-knowledge), or the acceptance is spurious, i.e., was accepted from someone who does not actually possess the information.
Some forms of non-interactive zero-knowledge proofs exist, but the validity of the proof relies on computational assumptions (typically the assumptions of an ideal cryptographic hash function).
Lecture 1 Encryption Schemes
Lecture 2 Probabilistic and Game based Security Definitions
Lecture 3 Reduction Proofs - What are they?
Lecture 4 Reduction Proofs - How to do?
Lecture 5 Pseudo Random Generators
Lecture 6 Reduction Proof Example - PRG based Encryption
Lecture 7 Reduction Proof Examples - PRF Family
Lecture 8 PRG Output Expansion
Lecture 9 Hybrid Proofs - Defining Hybrids
Lecture 10 Hybrid Proof Example - PRG Output Expansion
Lecture 11 Random Oracle Model ROM
Lecture 12 ROM Construction Example - CPA secure RSA
Lecture 13 ROM Proof Example - CPA secure RSA
Lecture 14 ROM Construction Examples - RSA FDH Signatures
Lecture 15 ROM Proof Examples - RSA FDH Signatures
For more topics please check the link bellow:
https://www.youtube.com/playlist?list=PLOBV8lhF_YPtE-P5D8mJZVqy3p4w2kWvp
https://www.youtube.com/playlist?list=PLOBV8lhF_YPtujh1D7kzZw1Z2vve5huuZ

Views: 9
Vijay S

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."
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Special thanks to Jules Suzdaltsev for hosting DNews!
Check Jules out on Twitter: https://twitter.com/jules_su

Views: 190399
Seeker

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

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

*Click Below to Sign up for the free Arduino Video Course:*
http://bit.ly/Arduino_Course
*Click Below to Check Out the Premium Arduino Video Course:*
http://bit.ly/Premium_Arduino
*Click Below to Read About This Topic on Our Website*
http://bit.ly/Random_Arduino
*Description:*
In this video we demonstrate how to create pseudo random numbers with Arduino - with a useful twist.
This lesson was inspired by the following viewer question:
"How do I create Random Non-Consecutive numbers with Arduino.
P.S. These are the best tutorials that a complete idiot like you could ever make, thanks."
-Anonymous
*Let's overview exactly what we will talk about in todays episode:*
Talk about pseudo random numbers.
Identify the problem - using an Arduino sketch to demonstrate.
Discuss how we might solve the problem.
Write an Arduino sketch that solves the problem.
Review what we talked about.
*Pseudo Random Numbers*
Before we answer the viewer’s question it is important to talk about what a pseudo random number is.
A purely random number in the mathematical sense can't be predicted. The microcontroller that the Arduino uses (and for that case, most computers in general) can't really create pure random numbers.
What they create instead are called pseudo random numbers. These are numbers that appear to be randomly generated, but if studied over time a predictable pattern emerges.
The bottom line is that the random numbers we create with Arduino can be predicted.
Now there are clever ways to create pseudo random numbers that act like the real deal – you can learn about one method in our video tutorial talking all about random numbers – but for this discussion, let’s return to our viewers inquiry.
*Identify the Viewer’s Problem - use an Arduino sketch to demonstrate.*
Ok, so let's go back to the viewers question, he wants to generate random numbers, but he never wants the same number generated two times in a row.
Let's write an Arduino Sketch to make this clear.
//This sketch outputs pseudo random integers.
//A variable to hold pseudo random integers.
int randomInt = 0;
void setup() {
//Initiate serial communication.
Serial.begin(9600);
}//Close setup function
void loop() {
//Create a random number and assign it to the randomInt variable.
randomInt = random(0, 10);
//Send randomInt to the serial port for displaying on the serial monitor window.
Serial.print(randomInt);
}//Close loop function.
In the first block of code a variable that will hold the pseudo random integers is declared and initialized.
//A variable to hold pseudo random integers.
int randomInt = 0;
In the setup() function we begin serial communication in order to display the numbers we generate on a computer display.
void setup() {
//Initiate serial communication.
Serial.begin(9600);
}//Close setup function
In the loop() we create the random number with the Arduino random() function and assign the output to the variable we had just created. The random() function can take two arguments 1) the minimum value of the number we want generated 2) the maximum value we want generated.
//Create a random number and assign it to the randomInt variable.
randomInt = random(0, 10);
I will use 0 for the minimum, and 10 for the maximum.
Every time through the loop, a new random number will be assigned the randomInt variable.
Finally, the value of randomInt is sent over the serial port to be displayed in the serial monitor window.
//Send randomInt to the serial port for displaying on the serial monitor window.
Serial.print(randomInt);
If you upload this code and open the serial monitor you will see in some cases where the same number shows up two times in a row.
This is the problem. The viewer doesn't ever want the same number two times in a row.
*Discuss how we might solve the problem.*
So let's talk about how we might solve this problem. We know we need to generate a random number.
What if we create a variable to track the previous random number?
Then we could use a condition that says something like "If the previous random number is equal to the random number that was just generated, toss that number out the window, and create a different one.”
The final thing we would need to do is set the previous random number equal to the new random number, that way we keep updating our previous random number every time through the loop().
*Let’s Implement our solution in an Arduino Sketch.*
Copy and paste this code into your Arduino IDE. All you need is an Arduino board attached to your computer to make it work.
*Get the Code from the below address*
http://bit.ly/Random_Arduino
*About Us:*
This Arduino tutorial was created by Open Source Hardware Group. We are an education company who seek to help people learn about electronics and programming through the ubiquitous Arduino development board.

Views: 8764
Programming Electronics Academy