• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.7
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• pp.43-52
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• 2004

This paper proposes an efficient functional verification environment of microprocessor. This verification environment consists of test vector generator part, simulator part, and comparator part. To enhance efficiency of verification, it use a bias random test vector generator. In a part of simulation, retargetable instruction level simulator is used for reference model. This verification environment is excellent to find error which is not detected by general test vector and will become a good guide to find new error type

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.319-326
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• 2020

PRNGs(Pseudorandom number generators) are essential for generating encryption keys for to secure online communication. A bitstream generated by the PRNG must be generated at high speed to encrypt the big data effectively in a symmetric key cryptosystem and should ensure the randomness of the level to pass through the several statistical tests. CA(Cellular Automata) based PRNGs are known to be easy to implement in hardware and to have better randomness than LFSR based PRNGs. In this paper, we design PRNGs based on PMLCA(Programable Maximum Length CA) that can generate effective key sequences in symmetric key cryptosystem. The proposed PRNGs generate bit streams through nonlinear control method. First, we design a PRNG based on an (m,n)-cell PMLCA ℙ with a single complement vector that produces linear sequences with the long period and analyze the period and the generating polynomial of ℙ. Next, we design an (m,n)-cell PC-MLCA based PRNG with two complement vectors that have the same period as ℙ and generate nonlinear sequences, and analyze the location of outputting the nonlinear sequence.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.661-668
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• 2018

Biomedical signals using skin resistance have different characteristics according to stress diseases. Biological diagnostic devices for diagnosing stress diseases have been developed by using these characteristics, and devices have been developed so that the signals measured by the skin storage meter can be easily analyzed. Experts in the field will look directly at the output signal to determine the likelihood of any stress disorder. However, it is very difficult for a person to accurately determine whether a person to be measured has a stress disorder by analyzing a bio-signal measured by each person to be measured, and the result of the judgment is very likely to be wrong. In order to solve these problems, we implemented the function of determining the signal of a stress disorder by using the machine learning technique. SVM was used as a classification method in consideration of low computing ability of measurement equipment. Training data and test data were randomly generated for each disease using error range 5 based on 13 diseases. Simulation results showed more than 90% decision accuracy. In the future, if the measurement equipment is actually applied to the patients, we can retrain the classifier with the newly generated data.