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Self-Organization in computational systems
Published in Alfredo Pereira, William Alfred Pickering, Ricardo Ribeiro Gudwin, Systems, Self-Organization and Information, 2018
A final concern must be addressed as well. A computer is inherently a deterministic machine, which means that given the same initial conditions, a computer program will always generate exactly the same behavior. Apparently (Bonabeau et al., 1997), randomness is a requirement for self-organized systems.2 If we want a computational system to be self-organized, how can we conciliate these facts? The issue of randomness has, for a long time, been a concern in the construction of simulators (James, 1995; Wang, 1996). The solution to this problem is in the use of pseudo-random number generators (Hull and Dobell, 1962), which from a given seed as input, typically exhibit statistical randomness while being generated by an entirely deterministic causal process. Even though the sequence is deterministic, if we use a random seed, as, for example, the millisecond in which the simulation was started by the user, the sequence will be completely different each time the program using it is run. From a statistical point of view, the sequence provides a behavior, which can be classified as random.
Nano CMOS Logic-Based Security Primitive Design
Published in Mark Tehranipoor, Domenic Forte, Garrett S. Rose, Swarup Bhunia, Security Opportunities in Nano Devices and Emerging Technologies, 2017
Fahim Rahman, Atul Prasad Deb Nath, Domenic Forte, Swarup Bhunia, Mark Tehranipoor
To achieve more uniformity and statistical randomness in spite of low inherent entropy, researchers have proposed cryptographic hash functions, a von Neumann corrector, and stream ciphers to be employed at the TRNG outputs. However, the modification reduces the throughput, and increases area and power overhead. Further, a vendor agnostic TRNG design for FPGA is proposed in Reference 49. Rahman et al. [15] proposed a technology-independent (TI) TRNG to combat the security issues arising from such various runtime/environmental condition-based degradations. It uses a “tunable”-RO architecture to leverage power supply noise along with clock jitter as the entropy source and can overcome environmental variation and aging-induced bias by controlling jitter, and adjusting RO delay by monitoring the runtime condition. A proposed power supply noise enhancement and tuning block, and a self-calibration scheme on bias detection further improve the performance and serve as countermeasures against the hardware-based attack [50]. The TRNG model presented by Robson et al. [51] utilizes multiple threshold-crossing methods to increase timing jitters.
Cash Flow Risks
Published in Willie Tan, Principles of Project and Infrastructure Finance, 2007
The discussion up to now has assumed that the initial cost and cash flows are certain. In practice, the initial cost and cash flows are affected by factors such as unexpected events, inadequate understanding of the business, insufficient data to make informed decisions, opportunism on the part of consultants, suppliers, and subcontractors, subjective judgments, statistical randomness, measurement errors, and linguistic imprecision.
Functional risk-oriented integrated preventive maintenance considering product quality loss for multistate manufacturing systems
Published in International Journal of Production Research, 2021
Yixiao Zhao, Yihai He, Di Zhou, Anqi Zhang, Xiao Han, Yao Li, Wenzhuo Wang
Considering the statistical randomness of Equation (19) result and independence of each element in , Equation (19) under the influence of part degradation uncertainty can be derived as where , , , and is the element of whose total element number is .
A novel colour image encryption based on fractional order Lorenz system
Published in Systems Science & Control Engineering, 2021
Shixu Li, Yongjin Yu, Xingquan Ji, Qi Sun
The randomness of the algorithm is very important to the password generator and ciphertext, and it affects the algorithm's ability to resist statistical attacks. This article uses the randomness detection kit provided by the National Bureau of Technology and Standards to conduct statistical randomness analysis of the algorithm in this article. In order to accept the randomness of the bit sequence, it is hoped that the significance level p of each test is above 0.01. The analysis results are shown in Table 5.