An adaptive efficient memristive ink drop spread (IDS) computing system

Klidbary, Sajad Haghzad; Shouraki, Saeed Bagheri; Afrakoti, Iman Esmaili Paeen

doi:10.1007/s00521-018-3604-0

Cited by 10 publications

(3 citation statements)

References 45 publications

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“…The implementation of ink drop spread (IDS) in 2D plans on the memristor-crossbar structure is presented by Merrikh-Bayat et al [27]. There are also other references related to the implementation of the ALM (Active Learning Method) algorithm on memristor-crossbar [28][29][30]. Memristor has also been used as a synapse in neuromorphic computing [31][32][33][34].…”

Section: The Memristor Modelingmentioning

confidence: 99%

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Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

Klidbary

Javadian

Omidi

et al. 2021

IJE

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Section: The Memristor Modelingmentioning

confidence: 99%

“…It can be mentioned that by using the ladder structure [28,29], changes can be made as a Gaussian function in the memristance of memristors. As mentioned in section 3, Vertical Slice Representation (Vertical Slice Centroid) for T2F set is considered here.…”

Section: Proposed Hardwarementioning

confidence: 99%

Memristor Crossbar-Based Hardware Implementation of Type-2 Fuzzy Membership Function and On-Chip Learning

Klidbary

Javadian

Omidi

et al. 2021

IJE

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“…Although ALM works very well in engineering problems, it has some shortages like hardware implementation, high memory usage, needing complex optimization processes and etc [31]. To solve the ALM's shortages, many types of research have been done [32,33]. AGIDS was proposed based on ALM and specially IDS concept [34].…”

Section: Adaptive Group Of Ink Drop Spread (Agids)mentioning

confidence: 99%

Comparative study between the developed simulators based on the direct and indirect methods for calculation of the energy spectrum of the neutron source

et al. 2019

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The purpose of the present paper is to compare the various computer codes used in fast-neutron spectroscopy for spectrum unfolding and calculation of the response of the organic scintillation detectors. For neutron spectrum unfolding, the Fredholm integral equation is solved using iterative and soft computing algorithms. In the present paper, the Modified Least SQuaRe (MLSQR) method (iterative algorithm) and some soft computing algorithms are presented. The latter comprise Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Group of Ink Drop Spread (AGIDS) methods. Furthermore, a MCNPX based algorithm for calculating the neutron response of organic scintillation detectors is presented which is used for Time-Of-Flight based fast-neutron spectroscopy (nTOF) . To this end, the ability for simulating the emission and tracking of two simultaneously emitted particles as well as the light production and light transport in the scintillator were added to the MCNPX computer code. Also, a post-processing software was developed to analyze the massive amounts of data in the output of the PTRAC card. The presented techniques are benchmarked with a published spectrum from an 241Am-9Be neutron source. The results obtained from the calculation using the proposed methods have an acceptable agreement with the standard ISO-8592 spectrum of the 241Am-9Be neutron source. However, the accuracy of the calculation using the SVM and AGIDS is better than the accuracy of the other presented methods.

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