Energy-efficient switching of nanomagnets for computing: Straintronics and other methodologies

The need for increasingly powerful computing hardware has spawned many ideas stipulating, primarily, the replacement of traditional transistors with alternate "switches" that dissipate miniscule amounts of energy when they switch and provide additional functionality that are beneficial for information processing. An interesting idea that has emerged recently is the notion of using two-phase (piezoelectric/magnetostrictive) multiferroic nanomagnets with bistable (or multi-stable) magnetization states to encode digital information (bits), and switching the magnetization between these states with small voltages (that strain the nanomagnets) to carry out digital information processing. The switching delay is ~1 ns and the energy dissipated in the switching operation can be few to tens of aJ, which is comparable to, or smaller than, the energy dissipated in switching a modern-day transistor. Unlike a transistor, a nanomagnet is "non-volatile", so a nanomagnetic processing unit can store the result of a computation locally without refresh cycles, thereby allowing it to double as both logic and memory. These dual-role elements promise new, robust, energy-efficient, high-speed computing and signal processing architectures (usually non-Boolean and often non-von-Neumann) that can be more powerful, architecturally superior (fewer circuit elements needed to implement a given function) and sometimes faster than their traditional transistor-based counterparts. This topical review covers the important advances in computing and information processing with nanomagnets with emphasis on strain-switched multiferroic nanomagnets acting as non-volatile and energy-efficient switches - a field known as "straintronics". It also outlines key challenges in straintronics.Comment: This is a commissioned topical review article published in Nanotechnolog

Optimal Intelligent Control for Wind Turbulence Rejection in WECS Using ANNs and Genetic Fuzzy Approach

One of the disadvantages in Connection of wind energy conversion systems (WECSs) to transmission networks is plentiful turbulence of wind speed. Therefore effects of this problem must be controlled. Nowadays, pitch-controlled WECSs are increasingly used for variable speed and pitch wind turbines. Megawatt class wind turbines generally turn at variable speed in wind farm. Thus turbine operation must be controlled in order to maximize the conversion efficiency below rated power and reduce loading on the drive-train. Due to random and non-linear nature of the wind turbulence and the ability of Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) Artificial Neural Networks (ANNs) in the modeling and control of this turbulence, in this study, widespread changes of wind have been perused using MLP and RBF artificial NNs. In addition in this study, a new genetic fuzzy system has been successfully applied to identify disturbance wind in turbine input. Thus output power has been regulated in optimal and nominal range by pitch angle regulation. Consequently, our proposed approaches have regulated output aerodynamic power and torque in the nominal rang.Comment: International journal of soft computing & soft engineering 201

Mathematical Software: Past, Present, and Future

This paper provides some reflections on the field of mathematical software on the occasion of John Rice's 65th birthday. I describe some of the common themes of research in this field and recall some significant events in its evolution. Finally, I raise a number of issues that are of concern to future developments.Comment: To appear in the Proceedings of the International Symposium on Computational Sciences, Purdue University, May 21-22, 1999. 20 page

Application of Visual Clustering Properties of Self Organizing Map in Machine-part Cell Formation

Cellular manufacturing (CM) is an approach that includes both flexibility of job shops and high production rate of flow lines. Although CM provides many benefits in reducing throughput times, setup times, work-in-process inventories but the design of CM is complex and NP complete problem. The cell formation problem based on operation sequence (ordinal data) is rarely reported in the literature. The objective of the present paper is to propose a visual clustering approach for machine-part cell formation using Self Organizing Map (SOM) algorithm an unsupervised neural network to achieve better group technology efficiency measure of cell formation as well as measure of SOM quality. The work also has established the criteria of choosing an optimum SOM map size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology. To evaluate the performance of the proposed algorithm, we tested the several benchmark problems available in the literature. The results show that the proposed approach not only generates the best and accurate solution as any of the results reported, so far, in literature but also, in some instances the results produced are even better than the previously reported results. The effectiveness of the proposed approach is also statistically verified.Comment: 33 pages, 7 figures, 7 table

An Empirical Study on the Procedure to Derive Software Quality Estimation Models

Software quality assurance has been a heated topic for several decades. If factors that influence software quality can be identified, they may provide more insight for better software development management. More precise quality assurance can be achieved by employing resources according to accurate quality estimation at the early stages of a project. In this paper, a general procedure is proposed to derive software quality estimation models and various techniques are presented to accomplish the tasks in respective steps. Several statistical techniques together with machine learning method are utilized to verify the effectiveness of software metrics. Moreover, a neuro-fuzzy approach is adopted to improve the accuracy of the estimation model. This procedure is carried out based on data from the ISBSG repository to present its empirical value

Comparison of Flow Scheduling Policies for Mix of Regular and Deadline Traffic in Datacenter Environments

Datacenters are the main infrastructure on top of which cloud computing services are offered. Such infrastructure may be shared by a large number of tenants and applications generating a spectrum of datacenter traffic. Delay sensitive applications and applications with specific Service Level Agreements (SLAs), generate deadline constrained flows, while other applications initiate flows that are desired to be delivered as early as possible. As a result, datacenter traffic is a mix of two types of flows: deadline and regular. There are several scheduling policies for either traffic type with focus on minimizing completion times or deadline miss rate. In this report, we apply several scheduling policies to mix traffic scenario while varying the ratio of regular to deadline traffic. We consider FCFS (First Come First Serve), SRPT (Shortest Remaining Processing Time) and Fair Sharing as deadline agnostic approaches and a combination of Earliest Deadline First (EDF) with either FCFS or SRPT as deadline-aware schemes. In addition, for the latter, we consider both cases of prioritizing deadline traffic (Deadline First) and prioritizing regular traffic (Deadline Last). We study both light-tailed and heavy-tailed flow size distributions and measure mean, median and tail flow completion times (FCT) for regular flows along with Deadline Miss Rate (DMR) and average lateness for deadline flows. We also consider two operation regimes of lightly-loaded (low utilization) and heavily-loaded (high utilization). We find that performance of deadline-aware schemes is highly dependent on fraction of deadline traffic. With light-tailed flow sizes, we find that FCFS performs better in terms of tail times and average lateness while SRPT performs better in average times and deadline miss rate. For heavy-tailed flow sizes, except for tail times, SRPT performs better in all other metrics.Comment: Technical Repor

Survey of state-of-the-art mixed data clustering algorithms

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data is challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present a state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. Lastly, we present an in-depth analysis of the overall challenges in this field, highlight open research questions and discuss guidelines to make progress in the field.Comment: 20 Pages, 2 columns, 6 Tables, 209 Reference

Towards Cytoskeleton Computers. A proposal

We propose a road-map to experimental implementation of cytoskeleton-based computing devices. An overall concept is described in the following. Collision-based cytoskeleton computers implement logical gates via interactions between travelling localisation (voltage solitons on AF/MT chains and AF/MT polymerisation wave fronts). Cytoskeleton networks are grown via programmable polymerisation. Data are fed into the AF/MT computing networks via electrical and optical means. Data signals are travelling localisations (solitons, conformational defects) at the network terminals. The computation is implemented via collisions between the localisations at structural gates (branching sites) of the AF/MT network. The results of the computation are recorded electrically and/or optically at the output terminals of the protein networks. As additional options, optical I/O elements are envisaged via direct excitation of the protein network and by coupling to fluorescent molecules.Comment: To be published as a chapter in the book Adamatzky A., Akl S., Sirakoulis G., Editors. From Parallel to Emergent Computing, CRC Press/Taylor & Francis, 201

A Multi-Dimensional approach towards Intrusion Detection System

In this paper, we suggest a multi-dimensional approach towards intrusion detection. Network and system usage parameters like source and destination IP addresses; source and destination ports; incoming and outgoing network traffic data rate and number of CPU cycles per request are divided into multiple dimensions. Rather than analyzing raw bytes of data corresponding to the values of the network parameters, a mature function is inferred during the training phase for each dimension. This mature function takes a dimension value as an input and returns a value that represents the level of abnormality in the system usage with respect to that dimension. This mature function is referred to as Individual Anomaly Indicator. Individual Anomaly Indicators recorded for each of the dimensions are then used to generate a Global Anomaly Indicator, a function with n variables (n is the number of dimensions) that provides the Global Anomaly Factor, an indicator of anomaly in the system usage based on all the dimensions considered together. The Global Anomaly Indicator inferred during the training phase is then used to detect anomaly in the network traffic during the detection phase. Network traffic data encountered during the detection phase is fed back to the system to improve the maturity of the Individual Anomaly Indicators and hence the Global Anomaly Indicator.Comment: 8 pages, 3 Figures, 4 Table

Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.Comment: 34 pages, 7 table