Design automation of testable finite state machines

Miroschnyk, Maryna; Pakhomov, Yuriy; German, Edward C.; Shkil, Alexander; Kulak, Elvira; Kucherenko, D. Yu.

doi:10.1109/ewdts.2017.8110034

Cited by 6 publications

(6 citation statements)

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“…The complexity of constructing a testing experiment with an finite state machine is determined by the properties of its diagram. To easily tested finite state machine [6][7][8][9][10], we will assign finite state machine, for which the tasks of test diagnostics are solved as simply as possible within the established costs. Before discussing the design of checking experiments, it is appropriate to recall several definition relating to experiments performed on sequential circuits or finite state machines.…”

Section: Mcandfpga-2019mentioning

confidence: 99%

“…This approach allows to extend the class of detectable faults, which in structural-analytical test generation methods is limited, as a rule, to a multitude of single constant faults. The use of finite state models in the construction of verification tests allows detecting any malfunction that changes the diagram of a healthy device and does not increase the number of states of the elements of the memory [10][11].…”

Section: Functional Approach To Synthesismentioning

confidence: 99%

“…In this connection, the study of the diagnostic properties of sequences generated by shift register is an important task directly related to solving the problem of synthesis of easily tested devices and systems. automaton models for certain rules of construction trees and truncating its finite vertices [10].…”

Section: Graph Model Of Easy Tested Finite State Machinesmentioning

confidence: 99%

“…a way to describe of the models of the control FSM, the specification of which is specified by the state table or by the state diagram. This is a special structure of the HDL-model, in which the functions of transitions and outputs are isolated into separate processes (process), and the assignment of the new state is carried out in a special process associated with synchronization [9][10][11]. One of ways to describe DD models in the form of FSM using VHDL language is the FSM template, i.e.…”

Section: Realization Of Easy Tested Finite State Machinementioning

confidence: 99%

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Testability Increasing Method by Introducing Hardware Redundancy in the Easy-tested Finite State Machines

Miroshnyk¹,

Galkin²,

Zaichenko³

et al. 2019

I International Scientific and Practical Conference

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Testability increasing methods by introducing hardware redundancy into the circuit implementation are sufficiently developed and widely used in the design. Since the construction of the testing sequence is based on the use of automaton diagrams, it eliminates the need to analyze the circuit implementation of the remote control when building a diagnostic experiment. This approach allows us to extend the class of detectable faults, which in structural-analytical test generation methods is limited, as a rule, to a multitude of single constant faults. The use of automaton models in the construction of tests allows to detect any malfunction that changes the automaton diagram of a serviceable remote control and does not increase the number of states of remote control memory elements. There was described finite state machine using hardware description language. The method of computer-aided design of the easytested control FSM by introducing the hardware redundancy is presented in the paper. The FSM model is represented in VHDL in the form of the FSM template. The solution way is to add additional fragments of the VHDL code, which ensure the forced setting of the FSM into an arbitrary state without the use of synchronizing sequences. The use of the shift register in the memory part of the control FSM for organizing the path scanning was considered. The method of FSM state table expansion, which ensures the mode of bypassing all nodes of the FSM' state diagram in the diagnostic mode was proposed.

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Section: Mcandfpga-2019mentioning

confidence: 99%

Section: Functional Approach To Synthesismentioning

confidence: 99%

Section: Graph Model Of Easy Tested Finite State Machinesmentioning

confidence: 99%

Section: Realization Of Easy Tested Finite State Machinementioning

confidence: 99%

See 2 more Smart Citations

Testability Increasing Method by Introducing Hardware Redundancy in the Easy-tested Finite State Machines

Miroshnyk¹,

Galkin²,

Zaichenko³

et al. 2019

I International Scientific and Practical Conference

View full text Add to dashboard Cite

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“…One challenge, however, is how to design a security stream cipher generator to facilitate the process of generating the randomized and unpredictable sequence. Common stream cipher generators include linear feedback shift register [4], nonlinear feedback shift register [5], finite automation [6], linear congruence generators [7] and chaotic systems [8]. In most existing approaches involving the use of private key generators, the generators are manually designed (e.g., using mathematical formulas) to generate the private key in a specific "style" in order to achieve a certain security level.…”

Section: Introductionmentioning

confidence: 99%

DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption

Ding

Tan

Qin

et al. 2020

Preprint

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The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients' medical imaging data. In this paper, a novel deep learningbased key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.

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