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Rectifying memristor bridge circuit realized with human skin

Introduction A memristor ( mem ory r es istor ) is labelled as the fourth passive electrical circuit element [ 1 ] and its resistance may change with an applied electrical voltage or current. It is characterized by its state dependent Ohm’s law and its state equation that describes how the inner state changes with the applied electrical stimulus (1) v = M ( x ) i $$v=M\left( x \right)i$$ (2) d x d t = f ( x , i ) . $$\frac{dx}{dt}=f\left( x,i \right).$$ Equations ( 1 ) and ( 2 ) describe a generic memristor which is the

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An introduction to the memristor – a valuable circuit element in bioelectricity and bioimpedance

element that was equally fundamental as the other three [ 1 ]. He gave this element the name memristor since it, under certain conditions, acts as a resistor with memory, i.e. the resistance is dependent on the physical history. In the years following Chua’s work, the memristor concept was left quite alone until the birth of nanotechnology some years ago when a group at the Hewlett-Packard (HP) lab managed to construct a physical component acting as a memristor [ 2 , 3 ]. An image of this memristor is given in Figure 1 . Fig. 1 The first realized memristor

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Frequency dependent rectifier memristor bridge used as a programmable synaptic membrane voltage generator

Introduction The usage of memristors in analog circuit design enablesnew applications. In [ 1 ], an ADC consisting of memristors has been proposed. Other applications are an automatic gain control circuit [ 2 ], programmable analog circuits [ 3 ], an electrical potentiometer [ 3 ] or oscillators [ 4 , 5 ]. In [ 6 ] memristors are used for basic arithmetic operations. In this paper a frequency-dependent rectifier memristor bridge is presented and therefore, a broad description of memristive systems will be given first. In general, a memristive system is

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Analysis and Synthesis of Chaotic Circuits using Memristor Properties

References [1] CHUA, L. O. : Memristor - The Missing Circuit Element, IEEE Transaction Circuit Theory 18 No. 5 (1971), 507-519. [2] CHUA, L. O.-KANG, S. M. : Memristive Devices and Systems, Proceedings of the IEEE 64 No. 2 (1976), 209-223. [3] STRUKOV, D. B.-SNIDER, G. S.-STEWART, G. R.-WILLIAMS, R. S. : The Missing Memristor Found, Nature 453 (2008), 80-83. [4] ITOH, M.-CHUA, L. O. : Memristor Oscillators., International Journal of Bifurcation and Chaos 18 No. 11 (2008), 3183

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From Human Body Digitization to Internet of Bodies toward a New Dimension of Military Operations

), available at: , accessed on 04 August 2019. Sailesh, A. (2019). Internet of Bodies – An Overview. Medium Corporation, IEEE LINK , available at: , accessed on 03 July 2019. Schematic model of the HP memristor. (2019 ), available at: , accessed on 03 July 2019. Spintronic. (2019), available at: https

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Synchronization Analysis of Inertial Memristive Neural Networks with Time-Varying Delays

References [1]L. O. Chua Memristor-the missing circuit element, IEEE Transaction on Circuit Theory, Vol. 18, No 6, pp. 507-519, 1971 [2] D. Strukov, G. Snide and D. Stewart, The missing memristor found, Nature, Vol. 453, No. 6, pp. 80- 83, 2008. [3] R. Rakkiyappan, S. Premalatha, A. Chandrasekar and J. Cao, Stability and synchronization of innertial memristive neural networks with time delays, Cognit. Neurodyn., Vol. 10, No. 5, pp. 437-451, 2016. [4] N. Li and J. Cao, Lag synchronization of

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Novel Fuzzy-Based Self-Adaptive Single Neuron PID Load Frequency Controller for Power System

Engineering & Computer Sciences, 19(4), pp. 513–530. Shahrokhi, M. and Zomorrodi, A., 2013. Comparison of PID controller tuning methods. Department of Chemical & Petroleum Engineering Sharif University of Technology, pp.1–2. Wang, Q., Shuang, Y. (2015). A single neuron PID control algorithm of memristor-based. Computer Information System, 14, no. 20143104, pp. 5023– 5030. Xiao-dan, et al. (2017). Application of single neuron adaptive PID approach in rolling tension control. In: 2nd International Conference on Materials

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On the Topological Properties of the Certain Neural Networks

References [1] J. Cao, R. Li, Fixed-time synchronization of delayed memristor-based recurrent neural networks, Sci. China. Inf. Sci. 60(3) (2017) 032201. [2] Y. Huo, J. B-Liu, J. Cao, Synchronization analysis of coupled calcium oscillators based on two regular coupling schemes, Neurocomputing 165 (2015) 126-132. [3] Z. Guo, J. Wang, Z. Yan, Attractivity analysis of memristor-based cellular neural networks with time-varying delays, IEEE Trans. Neural Netw. Learn. Syst. 25 (2013) 704-717. [4] J

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Characterization of Novel 8T SRAM with Low Leakage and Optimized Area

-Hall, 2005. [18] P. Raikwal, V. Neema, & A. Verma, (2017, April). “High speed 8T SRAM cell design with improved read stability at 180nm technology”. In Electronics, Communication and Aerospace Technology (ICECA), 2017 International conference of (Vol. 2, pp. 563-568). IEEE. [19] K. F. Sharif, R. Islam, S. N. Biswas, & V. Groza. (2017, April). “4 Transistor and 2 memristor based memory”. In Computer Applications & Industrial Electronics (ISCAIE), 2017 IEEE Symposium on (pp. 37-40). IEEE. [20] C. Benton and A. P. Chandrakasan. “Static noise margin variation

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