Characterization of Single Cycle CA and its Application in Pattern Classification

AbstractThe special class of irreversible cellular automaton (CA) with multiple attractors is of immense interest to the CA researchers. Characterization of such a CA is the necessity to devise CA based solutions for diverse applications. This work explores the essential properties of CA attractors towards characterization of the 1-dimensional cellular automata with point states (single length cycle attractors). The concept of Reachability Tree is introduced for such characterization. It enables identification of the pseudo-exhaustive bits (PE bits) of a CA defining its point states. A theoretical framework has been developed to devise schemes for synthesizing a single length cycle multiple attractor CA with the specific set of PE bits. It also results in a linear time solution while synthesizing a CA for the given set of attractors and its PE bits. The experimentation establishes that the proposed CA synthesis scheme is most effective in designing the efficient pattern classifiers for wide range of applications

Design of Efficient Full Adder in Quantum-Dot Cellular Automata

Further downscaling of CMOS technology becomes challenging as it faces limitation of feature size reduction. Quantum-dot cellular automata (QCA), a potential alternative to CMOS, promises efficient digital design at nanoscale. Investigations on the reduction of QCA primitives (majority gates and inverters) for various adders are limited, and very few designs exist for reference. As a result, design of adders under QCA framework is gaining its importance in recent research. This work targets developing multi-layered full adder architecture in QCA framework based on five-input majority gate proposed here. A minimum clock zone (2 clock) with high compaction (0.01 μm2) for a full adder around QCA is achieved. Further, the usefulness of such design is established with the synthesis of high-level logic. Experimental results illustrate the significant improvements in design level in terms of circuit area, cell count, and clock compared to that of conventional design approaches

A Population Based Approach to Model Network Lifetime in Wireless Sensor Networks

The physical constraints of battery-powered sensors impose limitations on their processing capacity and longetivity. As battery power in the nodes decays, certain parts of the network may become disconnected or the coverage may shrink, thereby reducing the reliability and the potency of the sensor network. Since sensor networks operate unattended and without maintainence, it is imperative that network failures are detected early enough so that corrective measures can be taken. Existing research has primarily concentrated on developing algorithms, be it distributed or centralized, to optimize network longetivity metrics. For instance, [4], [5] propose MAC layer optimizations to prolong longetivity, while [7], [6] look at the problem from a Layer 3 perspective. Works along the lines of actually building network models for energy consumption are addressed in [2], [3], but these models fail to capture the interplay between a node’s spatial location and it’s energy consumption. Also, the generic nature of our proposed model facilitates the modeling and analysis of rechargable sensor networks [8]. In our current work, we develop an unifying framework to characterize the lifetime of such energy constrained networks, and obtain insights into their working. In particular, we employ a framework similar to population models for biological systems, to model the network lifetime. We consider both spatial scenarios, where a node’s power consumption is governed by it’s position in space as well as non spatial scenarios, where the node’s location and power consumption model are independent entities. We also extend our framework to obtain the optimal power or node replenishment rates necessary to ensure that a given fraction of nodes always have a desired power level

An Analytic Framework for Modeling Peer to Peer Networks

This paper presents an analytic framework to evaluate the performance of peer to peer (P2P) networks. Using the time to download or replicate an arbitrary file as the metric, we present a model which accurately captures the impact of various network and peer level characteristics on the performance of a P2P network. We propose a queueing model which evaluates the delays in the routers using a single class open queueing network and the peers as M/G/1/K processor sharing queues. The framework takes into account the underlying physical network topology and arbitrary file sizes, the search time, load distribution at peers and number of concurrent downloads allowed by a peer. The model has been validated using extensive simulations with campus level, power law AS level and ISP level topologies. The paper also describes the impact of various parameters associated with the network and peers incluing external traffic rates, service variability, file popularity etc. on the download times. We also show that in scenarios with multi-part downloads from different peers, a rate proportional allocation strategy minimizes the download times

CELLULAR AUTOMATA BASED DOCUMENT COMPRESSION TECHNOLOGY FOR ON-LINE NETWORK TRANSMISSION *

This paper reports an efficient document compression technology suitable for on-line network transmission. It identifies the different segments – such as text, image, and background within a scanned document. Three distinct compression techniques are developed around the regular structure of Cellular Automata (CA) for the compression of text, image and background segments to achieve better compression. As the low cost high speed VLSI implementation of CA is done, the proposed technology ideally suits for on-line network transmission of documents. 1