Resistor Array as a Commutator

V. B. Kotov, Z. B. Sokhova
{"title":"Resistor Array as a Commutator","authors":"V. B. Kotov,&nbsp;Z. B. Sokhova","doi":"10.3103/S1060992X23060085","DOIUrl":null,"url":null,"abstract":"<p>Being necessary components of large smart systems (including the brain), commutators can be realized on the basis of a resistor array with variable resistors. The paper considers some switching (commutating) capabilities of the resistor array. A switching graph is used to describe the work of the resistor array. This sort of graph provides a visual representation of generated high-conductivity current flow channels. A two-terminal scheme is used to generate the switching graph. In the scheme a voltage is supplies to a particular couple of poles (conductors), other poles being isolated from the power sources. Changing couples of poles makes it possible to generate a series of switching graphs. We demonstrate the possibility to create an interconnection between two or more blocks connected to the appropriate poles of the array. To do this, the resistor array must have a suitable signature (resistor directions), the applied voltage must match the signature. The series we generate are defined by not only control signals, but also the prehistory of the resistor array. Given preset resistor characteristics, the competition between graph edges plays an important role in that it contributes to the thinning of the switching graph we generate.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"32 2","pages":"S226 - S236"},"PeriodicalIF":1.0000,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S1060992X23060085","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Being necessary components of large smart systems (including the brain), commutators can be realized on the basis of a resistor array with variable resistors. The paper considers some switching (commutating) capabilities of the resistor array. A switching graph is used to describe the work of the resistor array. This sort of graph provides a visual representation of generated high-conductivity current flow channels. A two-terminal scheme is used to generate the switching graph. In the scheme a voltage is supplies to a particular couple of poles (conductors), other poles being isolated from the power sources. Changing couples of poles makes it possible to generate a series of switching graphs. We demonstrate the possibility to create an interconnection between two or more blocks connected to the appropriate poles of the array. To do this, the resistor array must have a suitable signature (resistor directions), the applied voltage must match the signature. The series we generate are defined by not only control signals, but also the prehistory of the resistor array. Given preset resistor characteristics, the competition between graph edges plays an important role in that it contributes to the thinning of the switching graph we generate.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
作为换向器的电阻阵列
换向器是大型智能系统(包括大脑)的必要组成部分,可以在可变电阻阵列的基础上实现。本文考虑了电阻器阵列的一些开关(换流)能力。用开关图来描述电阻器阵列的工作。这种类型的图形提供了生成的高导电性电流通道的可视化表示。采用双端方案生成切换图。在该方案中,电压被提供给特定的一对极(导体),其他极与电源隔离。改变一对极点使得生成一系列切换图成为可能。我们演示了在连接到阵列的适当极点的两个或多个块之间创建互连的可能性。要做到这一点,电阻阵列必须有一个合适的签名(电阻方向),施加的电压必须匹配签名。我们生成的序列不仅由控制信号定义,而且由电阻阵列的历史定义。给定预设的电阻特性,图边之间的竞争在我们生成的开关图的细化中起着重要的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
期刊最新文献
uSF: Learning Neural Semantic Field with Uncertainty Two Frequency-Division Demultiplexing Using Photonic Waveguides by the Presence of Two Geometric Defects Enhancement of Neural Network Performance with the Use of Two Novel Activation Functions: modExp and modExpm Automated Lightweight Descriptor Generation for Hyperspectral Image Analysis Accuracy and Performance Analysis of the 1/t Wang-Landau Algorithm in the Joint Density of States Estimation
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1