Description: This is noise generator with given statistical and spectral characteristics based on pseudorandom number generator which realises Merssenne Twister algorithm Platform: |
Size: 746496 |
Author:Roman Burzakovskiy |
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Description: 此程式是利用DES 來進行加密與解密的動作，Mode 採用ECB，Padding 方
法則採用PKCS5Padding。Key 是由程式自行產生，採用Pseudorandom number
generator 來產生Key。利用Framework 為Java Cryptography Extension (JCE) 。-This program is the use of DES to encrypt and decrypt action, Mode ECB Padding method using PKCS5Padding. Key is generated by the program on their own Pseudorandom number generator to generate the Key. The use of Framework for the Java Cryptography Extension (JCE). Platform: |
Size: 229376 |
Author:eason |
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Description: the LFSR is coded in VHDL, using a structural description, which is instantiated as a
separate component in the top-level design. Then we can get a random number by a pseudorandom number generator based on a linear feedback
shift register (LFSR) Platform: |
Size: 2048 |
Author:宋臣 |
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Description: A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG),[1] 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.[2]
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 earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed.-A pseudorandom number generator (PRNG), also known as a deterministic random bit generator (DRBG),[1] 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.[2]
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 earlier outputs, and more elaborate algorithms, which do not inherit the linearity of simpler PRNGs, are needed. Platform: |
Size: 9216 |
Author:rajendra |
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Description: 在实际应用中往往使用伪随机数就足够了。这些数列是“似乎”随机的数，实际上它们是通过一个固定的、可以重复的计算方法产生的。计算机或计算器产生的随机数有很长的周期性。它们不真正地随机，因为它们实际上是可以计算出来的，但是它们具有类似于随机数的统计特征。这样的发生器叫做伪随机数发生器。
在真正关键性的应用中，比如在密码学中，人们一般使用真正的随机数。-In actual applications tend to use a pseudorandom number is sufficient. The number of columns is " as if" a random number, in fact they are through a fixed, repeatable calculation produced. A random number generated by a computer or a calculator has a long periodicity. They are not really random, because they can actually be calculated, but they have similar statistical characteristics of random numbers. Such a pseudo random number generator is called the generator. In the real critical applications, such as in cryptography, people generally use a truly random number. Platform: |
Size: 1024 |
Author:张羽翔 |
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