Minimal cost one-dimensional linear hybrid cellular automata of degree through 500

Cattell, Kevin; Zhang, Shujian

doi:10.1007/bf00993091

Cited by 45 publications

(18 citation statements)

References 4 publications

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“…The initial population is polarized by strongly biasing towards the canonical 90/150 solution [CaZa95]. On the contrary, the initial CA state is completely not polarized.…”

Section: Computation Of Optimal Ca Rulesmentioning

confidence: 92%

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Exploiting the selfish gene algorithm for evolving cellular automata

Corno

Reorda²,

Squillero

2000

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challeng

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This paper shows an application in the field of Electronic CAD of the Selfish Gene algorithm, an evolutionary algorithm based on a recent interpretation of the Darwinian theory. Testing is a key issue in the design and production of digital circuits and the adoption of Built-In Self-Test (BIST) techniques is increasingly popular. In this paper, the Selfish Gene algorithm is adopted for determining the logic for a BIST architecture based on Cellular Automata (CA). A Genetic Algorithm has already been proposed for identifying good BIST architectures based on CA. However, adopting 2-bit cells, such method introduced a significant area overhead. Thanks to the adoption of the new and more powerful search engine, we were able to identify simpler BIST structures with a lower area overhead, but still able to obtain the same fault coverage.

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“…The initial population is polarized by strongly biasing towards the canonical 90/150 solution [CaZa95]. On the contrary, the initial CA state is completely not polarized.…”

Section: Computation Of Optimal Ca Rulesmentioning

confidence: 92%

“…Linear Hybrid CA (LHCA) are often used by hardware designers as IPG and the most common is the so-called 90450 LHCA [CaZa95] since rules 90 and 150 can be combined to achieve maximum-length pseudo-random sequences.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the selfish gene algorithm for evolving cellular automata

Corno

Reorda²,

Squillero

2000

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challeng

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“…The quantity of two components in is controlled by a parameter that specifies the jitter noise power expressed as a function of the total medium noise power (12) From (5)- (12), we can obtain the variance of the noise processes as (13) (14) (15)…”

Section: B Snr and Statistics Of Noise Processesmentioning

confidence: 99%

“…How to determine the positions of Rule 90 and Rule 150 in a register array can be found in [11] and [12].…”

Section: Rulementioning

confidence: 99%

Reconfigurable Readback-Signal Generator Based on a Field-Programmable Gate Array

2004

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Abstract-We have designed a readback-signal generator to provide noise-corrupted signals to a read channel simulator. It is implemented in a Xilinx Virtex-E field-programmable gate array (FPGA) device. The generator simulates in hardware the noise processes and distortions observed in hard drives. It uses embedded nonuniform random number generators to simulate the random characteristics of various disturbances in the read/write process. The signal generator can simulate readback pulses, intersymbol interference, transition noise, electronics noise, head and media nonlinearity, intertrack interference, and write timing error according to the characteristics specified by the user. A sample implementation operates at a 70-MHz clock speed. The design can easily be scaled for different error rates. The generator can be reconfigured in real time to give the user flexibility and increase the capacity of the FPGA device. The readback-signal generator can be integrated into an FPGA read channel simulator or serve as a test bench for data-recovery circuits.Index Terms-Field programmable gate array (FPGA), Gaussian pseudorandom number generator, magnetic recording, read channel, read channel modeling, reconfigurable computing, transition noise.

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“…The theory for cellular automata (CA) based pseudorandom number generators (PRNG) is well developed [1,2] and n -bit linear CA can be designed to generate sequences with desirable properties: maximum period 2 1 n p = − , uniform distribution of n-bit tuples and balanced distribution of '1' and '0'. These so-called m-sequences cannot be used directly due to their linear structure while the theory for nonlinear CA are less developed since it is difficult to ensure desirable sequence properties [8].…”

Section: Introductionmentioning

confidence: 99%

Evolving cellular automata to generate nonlinear sequences with desirable properties

Tan

Guan

2007

Applied Soft Computing

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Abstract-This paper presents a new chromosomal representation and associated genetic operators for the evolution of highly nonlinear cellular automata that generate pseudorandom number sequences with desirable properties ensured. This chromosomal representation reduces the computational complexity of genetic operators to evolve valid solutions while facilitating fitness evaluation based on the DIEHARD statistical tests.

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Minimal cost one-dimensional linear hybrid cellular automata of degree through 500

Cited by 45 publications

References 4 publications

Exploiting the selfish gene algorithm for evolving cellular automata

Exploiting the selfish gene algorithm for evolving cellular automata

Reconfigurable Readback-Signal Generator Based on a Field-Programmable Gate Array

Evolving cellular automata to generate nonlinear sequences with desirable properties

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