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Automation of Verification Using Artificial Neural Networks
Fajčík, Martin ; Husár, Adam (referee) ; Zachariášová, Marcela (advisor)
The goal of this thesis is to analyze and to find solutions of optimization problems derived from automation of functional verification of hardware using artificial neural networks. Verification of any integrated circuit (so called Design Under Verification, DUV) using technique called coverage-driven verification and universal verification methodology (UVM) is carried out by sending stimuli inputs into DUV. The verification environment continuously monitors percentual coverage of DUV functionality given by the specification. In current context, coverage stands for measurable property of DUV, like count of verified arithemtic operations or count of executed lines of code. Based on the final coverage, it is possible to determine whether the coverage of DUV is high enough to declare DUV as verified. Otherwise, the input stimuli set needs to change in order to achieve higher coverage. Current trend is to generate this set by technique called constrained-random stimulus generation. We will practice this technique by using pseudorandom program generator (PNG). In this paper, we propose multiple solutions for following two optimization problems. First problem is ongoing modification of PNG constraints in such a way that the DUV can be verified by generated stimuli as quickly as possible. Second one is the problem of seeking the smallest set of stimuli such that this set verifies DUV. The qualities of the proposed solutions are verified on 32-bit application-specific instruction set processors (ASIPs) called Codasip uRISC and Codix Cobalt.
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Comparison of the determination of hormones (Follicle stimulating hormone, Luteinizing hormone, Prolactin, Testosterone, Progesteron) by two analytical systems. Converting accredited method and its verification.
Kucejová, Soňa ; Martínková, Markéta (advisor) ; Mrízová, Iveta (referee)
Analytical system ARCHITECT i2000SR was verified according to requirements of ÚLBLD VFN and 1. LF UK laboratory in Prague. Repeatability, intermediate precision, and measurement uncertainty were determined as performance parameters for verification of analytical assays for testosterone, progesterone, luteinizing hormone, follicule stimulating hormone and prolactin. Results of Lyphochek control samples, which were measured, were consistent with values given by manufacture. Repeatability: coefficients of variation for testosterone Lyphochek 1 6,81%, for Lyphochek 3 6,40%, progesterone 2,4% and 1,8%, luteinizing hormone 5,38% and 1,89%, follicle stimulating hormone 5,12% and 3,24% prolactin 1,45% a 1,83%. Intermediate precision: coefficients of variation for testosterone Lyphochek 1 6,02%, Lyphochek 2 3,60%, Lyphochek 3 3,07%, progesterone 7,9%, 4,9% and 5,8%, luteinizing hormone 4,50%, 5,51% and 5,83%, follicle stimulating hormone 4,00%, 3,72% and 4,87%, prolactin 4,60%, 4,20% and 5,00%. Measurement uncertainty: testosterone 6,02%, progesterone 7,9%, luteinizing hormone 5,83%, follicle stimulating hormone 4,87%, prolactin 5,00%. Analytical System Architect i2000SR was compared with previously used ADVIA Centaur system to find out, whether it is possible to convert the method Centaur Testosterone,...
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Visualization and verification of plans
Glinský, Radoslav ; Barták, Roman (advisor) ; Dvořák, Filip (referee)
Title: Visualization and Verification of Plans Author: Radoslav Glinský Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor of the bachelor thesis: Doc. RNDr. Roman Barták, Ph.D. Abstract: Plan analysis is an important part of complete planning systems. In order to make even larger plans transparent and human readable, we have developed a program which helps users with the analysis and visualization of plans. This program is called VisPlan - Interactive Visualization and Verification of Plans. VisPlan is an inevitable part of this thesis as it practically implements its plan verification and visualization solutions. VisPlan finds and displays causal relations between actions, it identifies possible flaws in plans (and thus verifies plans' correctness), it highlights the flaws found in the plan and finally, it allows users to interactively modify the plan and hence manually repair the flaws or just fine-tune the plan. Keywords: Planning, Artificial Intelligence, PDDL, Verification
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