Quiz for Chapter 3 with Solutions
... (4) Unbiased to the nearest even: +0.100101100binary The result is +0.100110 (5) Unbiased to the nearest even: -0.100100110binary The result is -0.100100 (d) What is the result of the square root of a negative number? In the IEEE 754 standard (defined since 1985), the square root of negative number ...
... (4) Unbiased to the nearest even: +0.100101100binary The result is +0.100110 (5) Unbiased to the nearest even: -0.100100110binary The result is -0.100100 (d) What is the result of the square root of a negative number? In the IEEE 754 standard (defined since 1985), the square root of negative number ...
108_01_basics
... Why COMP108? Pre-requisite for: COMP202, COMP218 (COMP202 is pre-requisite for COMP309) ...
... Why COMP108? Pre-requisite for: COMP202, COMP218 (COMP202 is pre-requisite for COMP309) ...
Probability Distribution Function of the Internal Rate of Return in One
... stands for project life. The alternative is acceptable when ...
... stands for project life. The alternative is acceptable when ...
Lower bounds of shortest vector lengths in random knapsack lattices
... lattices, as the Gaussian heuristic may not hold for them. In the paper we study two types of random lattices in cryptography: the knapsack lattices and the NTRU lattices. For random knapsack lattices, we prove lower bounds of shortest vector lengths, which are very close to lengths predicted by the ...
... lattices, as the Gaussian heuristic may not hold for them. In the paper we study two types of random lattices in cryptography: the knapsack lattices and the NTRU lattices. For random knapsack lattices, we prove lower bounds of shortest vector lengths, which are very close to lengths predicted by the ...
The Hardest Random SAT Problems
... One explanation for the extraordinary diculty of some problems in the mostly satis able region is that these problems are just unsatis able or are satis able but give rise to subproblems which are just unsatis able. Such problems are \critically constrained". That is, there are just enough constrai ...
... One explanation for the extraordinary diculty of some problems in the mostly satis able region is that these problems are just unsatis able or are satis able but give rise to subproblems which are just unsatis able. Such problems are \critically constrained". That is, there are just enough constrai ...
Hardware random number generator
In computing, a hardware random number generator (TRNG, True Random Number Generator) is an apparatus that generates random numbers from a physical process, rather than a computer program. Such devices are often based on microscopic phenomena that generate low-level, statistically random ""noise"" signals, such as thermal noise, the photoelectric effect, and other quantum phenomena. These processes are, in theory, completely unpredictable, and the theory's assertions of unpredictability are subject to experimental test. A hardware random number generator typically consists of a transducer to convert some aspect of the physical phenomena to an electrical signal, an amplifier and other electronic circuitry to increase the amplitude of the random fluctuations to a measurable level, and some type of analog to digital converter to convert the output into a digital number, often a simple binary digit 0 or 1. By repeatedly sampling the randomly varying signal, a series of random numbers is obtained. The main application for electronic hardware random number generators is in cryptography, where they are used to generate random cryptographic keys to transmit data securely. They are widely used in Internet encryption protocols such as Secure Sockets Layer (SSL).Random number generators can also be built from ""random"" macroscopic processes, using devices such as coin flipping, dice, roulette wheels and lottery machines. The presence of unpredictability in these phenomena can be justified by the theory of unstable dynamical systems and chaos theory. Even though macroscopic processes are deterministic under Newtonian mechanics, the output of a well-designed device like a roulette wheel cannot be predicted in practice, because it depends on the sensitive, micro-details of the initial conditions of each use. Although dice have been mostly used in gambling, and in more recent times as ""randomizing"" elements in games (e.g. role playing games), the Victorian scientist Francis Galton described a way to use dice to explicitly generate random numbers for scientific purposes in 1890.Hardware random number generators generally produce a limited number of random bits per second. In order to increase the data rate, they are often used to generate the ""seed"" for a faster Cryptographically secure pseudorandom number generator, which then generates the pseudorandom output sequence.