Or using the LFSR in a computer for the same purpose, of getting a more advanced algorithm jump started.Steganography is the art and science of hiding secret information in a cover medium such that the presence of the hidden information cannot be detected. Id favor using the lower three or four significant digits of the time expressed in microseconds, however, for something suitably unpredictable. There is nothing fundamentally wrong with using clock time to get an initial seed value, for example. Ultimately the hardest part and the heart of most studies in the subject is in the generation of those uniformly distributed integers, that form the basis of all other distributions. Additionally some distributions can be suitably approximated using simple mathematical functions applied to a uniform float. the Chi-Square is simply the sum of the squares of independent normal distributions). Uniform floating point numbers can easily be converted to any other non-uniform distribution such as the Normal, Chi-Square, exponential, etc., using a variety of methods, such as Inverse Transform sampling, Rejection sampling, or simple algebraic relationships on distributions (e.g.
Uniform discrete integer distributions (which is what PRNGs generate) can easily be extended or compressed to span any arbitrary interval of integers using a variety of scaling techniques that preserve uniformity, or converted into uniform floating points distributions by randomly choosing a large integer and scaling it down into a float, etc. In general, its a trivial task to convert one distribution into another, regardless if its TRNG or PRNG. Ideally, we are looking for an algorithm that produces a sequence of numbers which are suitably unpredictable (this is the hardest part) from prior terms in the sequence, while also following a particular distribution (uniform, typically), meaning that every value in a range is produced in equal proportion. In general, though, all a PRNG is, is an algorithm that produces a sequence of integers. There are a set of diehard tests which have the sole purpose of testing the quality of a random number generator. Pseudorandom number generators only have the appearance of randomness, namely, they follow a particular distribution and the ability to predict future values from prior ones is not easy. You can even rely on lookup tables from real world phenomenon that have been documented lookup tables is very old school.
Then, you also have RNG programs that check through an internet connection, to find random number servers online, most of which use TRNGs. Most modern personal computers do not use this and, if they do, probably only use it to find the seed value for PRNGs. From checking ambient conditions such as temperature and pressure, to phenomenon a more nuanced and bit more subject to conditions atomic/quantum, such as what state a multistable circuit with feedback settles in. That said, the invention of true random number generator (TRNG) hardware circuitry (typically analog) does exist, and are approached in different ways. They arent truly random because computers are deterministic machines (state machines) no predetermined algorithm can be programmed to generate truly random numbers from a known prior state. Properly, these are pseudorandom number generators (PRNG), because they arent truly random. One of the more modern generators (not the most modern), and which is considered cryptographically secure (in the sense of how unpredictable it is), is known as the Mersenne Twister. Some computers have a linear feedback shift register (LFSR) built into the circuitry for random number generation, but its not very advanced. One of the earliest and weakest is known as the Middle Square method - easy to implement in programming and suitable for many low level tasks. One page is devoted to just listing the various types of random number generators used throughout history. The wikipedia has a lot of great pages devoted to random number generation (RNG).