Computers from plants we never made. Speculations

We discuss possible designs and prototypes of computing systems that could be based on morphological development of roots, interaction of roots, and analog electrical computation with plants, and plant-derived electronic components. In morphological plant processors data are represented by initial configuration of roots and configurations of sources of attractants and repellents; results of computation are represented by topology of the roots' network. Computation is implemented by the roots following gradients of attractants and repellents, as well as interacting with each other. Problems solvable by plant roots, in principle, include shortest-path, minimum spanning tree, Voronoi diagram, $\alpha$-shapes, convex subdivision of concave polygons. Electrical properties of plants can be modified by loading the plants with functional nanoparticles or coating parts of plants of conductive polymers. Thus, we are in position to make living variable resistors, capacitors, operational amplifiers, multipliers, potentiometers and fixed-function generators. The electrically modified plants can implement summation, integration with respect to time, inversion, multiplication, exponentiation, logarithm, division. Mathematical and engineering problems to be solved can be represented in plant root networks of resistive or reaction elements. Developments in plant-based computing architectures will trigger emergence of a unique community of biologists, electronic engineering and computer scientists working together to produce living electronic devices which future green computers will be made of.Comment: The chapter will be published in "Inspired by Nature. Computing inspired by physics, chemistry and biology. Essays presented to Julian Miller on the occasion of his 60th birthday", Editors: Susan Stepney and Andrew Adamatzky (Springer, 2017

Optimized Quality Factor of Fractional Order Analog Filters with Band-Pass and Band-Stop Characteristics

Fractional order (FO) filters have been investigated in this paper, with band-pass (BP) and band-stop (BS) characteristics, which can not be achieved with conventional integer order filters with orders lesser then two. The quality factors for symmetric and asymmetric magnitude response have been optimized using real coded Genetic Algorithm (GA) for a user specified center frequency. Parametric influence of the FO filters on the magnitude response is also illustrated with credible numerical simulations.Comment: 6 pages, 13 figures; 2012 Third International Conference on Computing, Communication and Networking Technologies (ICCCNT'12), July 2012, Coimbator

Evolving unipolar memristor spiking neural networks

© 2015 Taylor & Francis. Neuromorphic computing – brain-like computing in hardware – typically requires myriad complimentary metal oxide semiconductor spiking neurons interconnected by a dense mesh of nanoscale plastic synapses. Memristors are frequently cited as strong synapse candidates due to their statefulness and potential for low-power implementations. To date, plentiful research has focused on the bipolar memristor synapse, which is capable of incremental weight alterations and can provide adaptive self-organisation under a Hebbian learning scheme. In this paper, we consider the unipolar memristor synapse – a device capable of non-Hebbian switching between only two states (conductive and resistive) through application of a suitable input voltage – and discuss its suitability for neuromorphic systems. A self-adaptive evolutionary process is used to autonomously find highly fit network configurations. Experimentation on two robotics tasks shows that unipolar memristor networks evolve task-solving controllers faster than both bipolar memristor networks and networks containing constant non-plastic connections whilst performing at least comparably

Modeling and design of memristor-based fuzzy systems

The incessant down scaling of CMOS technology has been the main driving force for the semiconductor industry over the past decades. Yet, as process variations and leakage current continue to exhibit more pronounced effect with every technology node, this down scaling paradigm is expected to saturate in the few coming years. This prospect has led the research community to seek new technologies to surpass those challenges. Amongst the promising candidates is the memristor technology recently characterized by HP Labs. The miniaturized features and the peculiar behavior exhibited by the memsitor make it very well suited in some applications. For instance, memrsitors are used as memory cells in state-of-the-art memories known as Resistive RAMs in which the non-volatility of the memristor is exploited. The programmable nature of the memristor has made it a powerful candidate in neuromorphic and fuzzy systems that, in essence, go beyond the classical Von Neumann computing paradigm. In such systems, ideas from Artificial Intelligence, that for so long have been implemented on the software level, are implemented as electronic circuitry which renders benefits such as compact area and reduced power consumption. This work focuses on memrsitor-based Fuzzy applications. First, memristor-based Min-Max circuit used in the Fuzzy Inference engine is analyzed. It is proven that memrsitor-based Min-Max circuits can be extended to an arbitrary number of inputs ‘N’ under the proper design constraints. In addition, the effect of the memristor threshold is analyzed and a closed form expression is derived. It is shown that, for a given memristor with a specific OFF resistance and threshold current, there is a trade-off between the size and the resolution of the circuit. Then, a memrsitor-based Defuzzifier circuit is proposed. A major challenge in Defuzzifiers is their area occupancy due to the use of Multiplier and Divider circuits. In this design, the memrsitor analog programmability is leveraged to reduce the multiplication operation into simple Ohm’s Law which alleviates the need for dedicated hardware for multiplier circuit and, accordingly, reduces the area occupancy

Symbolic Representation for Analog Realization of A Family of Fractional Order Controller Structures via Continued Fraction Expansion

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.This paper uses the Continued Fraction Expansion (CFE) method for analog realization of fractional order differ-integrator and few special classes of fractional order (FO) controllers viz. Fractional Order Proportional-Integral-Derivative (FOPID) controller, FO[PD] controller and FO lead-lag compensator. Contemporary researchers have given several formulations for rational approximation of fractional order elements. However, approximation of the controllers studied in this paper, due to having fractional power of a rational transfer function, is not available in analog domain; although its digital realization already exists. This motivates us for applying CFE based analog realization technique for complicated FO controller structures to get equivalent rational transfer functions in terms of the controller tuning parameters. The symbolic expressions for rationalized transfer function in terms of the controller tuning parameters are especially important as ready references, without the need of running CFE algorithm every time and also helps in the synthesis of analog circuits for such FO controllers