I don't really get what this coin is about. Can someone tell me why it is good and what it can be used for in the real world now for?
You can find all information, on their website
Scheme Of RNG Algorithm:
We use a few layers of randomization, based on differing algorithms, mixed to provide unpredictable randomization for our platform.
https://s8.postimg.cc/bff7r246d/Screen_Shot_08-13-18_at_02.41_AM.pngExplanation Of RNG Methods:Coin Based Algorithms
The Four Sources Of RNG Coin’s Built-in Random Number Generation Function Are As Follows:
1. The source of entropy for random number generation is the original sequence of bytes. To get this, we use the Time Stamp Counter assembler instructions for the x86 and x86_64 platforms, reading the TSC counter and returns in the EDX: EAX 64-bit number of cycles since the last reset reboot of the processor.
https://en.wikipedia.org/wiki/Time_Stamp_Counter 2. The resulting bytes are mixed in an unrecoverable order, using RAND_add from the SSL library.
A description of the function can be found here:
https://www.openssl.org/docs/man1.0.2/crypto/RAND_add.html3. The OS (operating system) function of Linux is used to get another random 32 bytes.
http://man7.org/linux/man-pages/man2/getrandom.2.html 4. RDRAND - Read Random Number returns 32 bytes generated by the hardware of the computer (
http://www.felixcloutier.com/x86/RDRAND.html. ). The resulting bytes are mixed in the CSHA512 class, the Write method, and the Finalize method.
PCG Family:Permuted Congruential Generators (PCGs) are a family of simple, fast, space-efficient, and statistically sound algorithms for random number generation. Unlike many general-purpose RNGs, they are also hard to predict.
PCGs are a family of RNGs which use a linear congruential generator as the state-transition function, and use permutation functions on tuples to produce output that is much more random than the RNG's internal state.
PCGs Differ From Classical Linear Congruential Generators In Three Ways
● The LCG modulus and state are larger, often equalling twice the size of the desired output.
● it uses a power-of-2 modulus. This results in particularly efficient implementation, having a full period generator and unbiased output bits.
● The state is not output directly, but rather, the most significant bits of the state are used to select a bitwise rotation or shift, which is applied to the state to produce the output.
PCG was developed by applying TestU01 to reduced-size variants,[7] and by determining the minimum number of internal state bits required to pass BigCrush. BigCrush examines enough data to detect a period of 235, so that even an ideal generator requires 36 bits of state to pass it.
https://s8.postimg.cc/40pvy805x/Screen_Shot_08-13-18_at_02.45_AM.pngBlock Hash Method:Blockchain provides unpredictable hash numbers. If the miner finds that the value of the bet is much higher than the block reward, they may discard the block, rather than choosing to mine on it. There is also risk involved if there are too many block hashes involved between the first call and the final reward call of the bet. The block hash returns a value of zero if the block hash number exceeds 256. Block hash method is a very unpredictable method for receiving a strong RNG seed.
Combining all the aforementioned methods together, we provide strong and unpredictable RNG generation, which may pass BigCrush tests. Unlike competitors, we plan to pass the certification of our RNG algorithm in late 2018, and receive several certificates from proper authorities, officially improving our RNG. We will be the first crypto gambling project with a certified RNG.
