Robust Simulation of a TaO Memristor Model

This work presents a continuous and differentiable approximation of a Tantalum oxide memristor model which is suited for robust numerical simulations in software. The original model was recently developed at Hewlett Packard labs on the basis of experiments carried out on a memristor manufactured in house. The Hewlett Packard model of the nano-scale device is accurate and may be taken as reference for a deep investigation of the capabilities of the memristor based on Tantalum oxide. However, the model contains discontinuous and piecewise differentiable functions respectively in state equation and Ohm's based law. Numerical integration of the differential algebraic equation set may be significantly facilitated under substitution of these functions with appropriate continuous and differentiable approximations. A detailed investigation of classes of possible continuous and differentiable kernels for the approximation of the discontinuous and piecewise differentiable functions in the original model led to the choice of near optimal candidates. The resulting continuous and differentiable DAE set captures accurately the dynamics of the original model, delivers well-behaved numerical solutions in software, and may be integrated into a commercially-available circuit simulator

Resistive Switching Assisted by Noise

We extend results by Stotland and Di Ventra on the phenomenon of resistive switching aided by noise. We further the analysis of the mechanism underlying the beneficial role of noise and study the EPIR (Electrical Pulse Induced Resistance) ratio dependence with noise power. In the case of internal noise we find an optimal range where the EPIR ratio is both maximized and independent of the preceding resistive state. However, when external noise is considered no beneficial effect is observed.Comment: To be published in "Theory and Applications of Nonlinear Dynamics: Model and Design of Complex Systems", Proceedings of ICAND 2012 (Springer, 2013

Memristive excitable cellular automata

The memristor is a device whose resistance changes depending on the polarity and magnitude of a voltage applied to the device's terminals. We design a minimalistic model of a regular network of memristors using structurally-dynamic cellular automata. Each cell gets info about states of its closest neighbours via incoming links. A link can be one 'conductive' or 'non-conductive' states. States of every link are updated depending on states of cells the link connects. Every cell of a memristive automaton takes three states: resting, excited (analog of positive polarity) and refractory (analog of negative polarity). A cell updates its state depending on states of its closest neighbours which are connected to the cell via 'conductive' links. We study behaviour of memristive automata in response to point-wise and spatially extended perturbations, structure of localised excitations coupled with topological defects, interfacial mobile excitations and growth of information pathways.Comment: Accepted to Int J Bifurcation and Chaos (2011

Detecting Flow Anomalies in Distributed Systems

Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank them using a proposed metric within distributed systems. We evaluate our approach on passengers' records of an urbanized city's public transportation system and correlate our findings with passengers' postings on social media microblogs. Our experiments show that the metric derived using our localization algorithm gives a better ranking of anomalies as compared to standard deviation measures from statistical models. Our case studies also demonstrate that transportation events reported in social media microblogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems