Towards self-ware via swarm-array computing

The work reported in this paper proposes Swarm-Array computing, a novel technique inspired by swarm robotics, and built on the foundations of autonomic and parallel computing. The approach aims to apply autonomic computing constructs to parallel computing systems and in effect achieve the self-ware objectives that describe self-managing systems. The constitution of swarm-array computing comprising four constituents, namely the computing system, the problem/task, the swarm and the landscape is considered. Approaches that bind these constituents together are proposed. Space applications employing FPGAs are identified as a potential area for applying swarm-array computing for building reliable systems. The feasibility of a proposed approach is validated on the SeSAm multi-agent simulator and landscapes are generated using the MATLAB toolkit.

Achieving intelligent agents and its feasibility in swarm-array computing

The work reported in this paper proposes ‘Intelligent Agents’, a Swarm-Array computing approach focused to apply autonomic computing concepts to parallel computing systems and build reliable systems for space applications. Swarm-array computing is a robotics a swarm robotics inspired novel computing approach considered as a path to achieve autonomy in parallel computing systems. In the intelligent agent approach, a task to be executed on parallel computing cores is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and can be seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-* objectives of autonomic computing. The approach is validated on a multi-agent simulator.

Comparison of the computational cost of a Monte Carlo and deterministic algorithm for computing bilinear forms of matrix powers

In this paper we consider bilinear forms of matrix polynomials and show that these polynomials can be used to construct solutions for the problems of solving systems of linear algebraic equations, matrix inversion and finding extremal eigenvalues. An almost Optimal Monte Carlo (MAO) algorithm for computing bilinear forms of matrix polynomials is presented. Results for the computational costs of a balanced algorithm for computing the bilinear form of a matrix power is presented, i.e., an algorithm for which probability and systematic errors are of the same order, and this is compared with the computational cost for a corresponding deterministic method.

A blueprint for affective computing: A sourcebook

A Blueprint for Affective Computing: A sourcebook and manual is the very first attempt to ground affective computing within the disciplines of psychology, affective neuroscience, and philosophy. This book illustrates the contributions of each of these disciplines to the development of the ever-growing field of affective computing. In addition, it demonstrates practical examples of cross-fertilization between disciplines in order to highlight the need for integration of computer science, engineering and the affective sciences.

Redox control of light-induced charge separation in a transition metal cluster: photochemistry of a methyl viologen-substituted [Os-3(CO)10(a-diimine)] cluster

Sub)picosecond transient absorption (TA) and time-resolved infrared (TRIR) spectra of the cluster [OS3(CO)(10-) (AcPy-MV)](2+) (the clication AcPy-MV = Acpy-MV2+ = [2-pyridylacetimine-N-(2-(1'-methyl-4,4'-bipyridine-1,1'-diium-1-yl) ethyl)] (PF6)(2)) (1(2+)) reveal that photoinduced electron transfer to the electron-accepting 4,4'-bipyridine-1,1'diium (MV2+) moiety competes with the fast relaxation of the initially populated sigmapi* excited state of the cluster to the ground state and/or cleavage of an Os-Os bond. The TA spectra of cluster 12 in acetone, obtained by irradiation into its lowest-energy absorption band, show the characteristic absorptions of the one-electron-reduced MV*(+) unit at 400 and 615 nm, in accordance with population of a charge-separated (CS) state in which a cluster-core electron has been transferred to the lowest pi* orbital of the remote MV2+ unit. This assignment is confirmed by picosecond TRIR spectra that show a large shift of the pilot highest-frequency nu(CO) band of 1(2+) by ca. +40 cm(-1), reflecting the photooxidation of the cluster core. The CS state is populated via fast (4.2 x 10(11) s(-1)) and efficient (88%) oxidative quenching of the optically populated sigmapi* excited state and decays biexponentially with lifetimes of 38 and 166 ps (1:2:1 ratio) with a complete regeneration of the parent cluster. About 12% of the cluster molecules in the sigmapi* excited state form long-lived open-core biradicals. In strongly coordinating acetonitrile, however, the cluster core-to-MV2+ electron transfer in cluster 12+ results in the irreversible formation of secondary photoproducts with a photooxidized cluster core. The photochemical behavior of the [Os-3(CO)(10)(alpha-diimine-MV)](2+) (donor-acceptor) dyad can be controlled by an externally applied electronic bias. Electrochemical one-electron reduction of the MV2+ moiety prior to the irradiation reduces its electron-accepting character to such an extent that the photoinduced electron transfer to MV*+ is no longer feasible. Instead, the irradiation of reduced cluster 1(.)+ results in the reversible formation of an open-core zwitterion, the ultimate photoproduct also observed upon irradiation of related nonsubstituted clusters [Os-3(CO)(10)(alpha-diimine)] in strongly coordinating solvents such as acetonitrile.

Implementing intelligent cores using processor virtualization for fault tolerance

Processor virtualization for process migration in distributed parallel computing systems has formed a significant component of research on load balancing. In contrast, the potential of processor virtualization for fault tolerance has been addressed minimally. The work reported in this paper is motivated towards extending concepts of processor virtualization towards ‘intelligent cores’ as a means to achieve fault tolerance in distributed parallel computing systems. Intelligent cores are an abstraction of the hardware processing cores, with the incorporation of cognitive capabilities, on which parallel tasks can be executed and migrated. When a processing core executing a task is predicted to fail the task being executed is proactively transferred onto another core. A parallel reduction algorithm incorporating concepts of intelligent cores is implemented on a computer cluster using Adaptive MPI and Charm ++. Preliminary results confirm the feasibility of the approach.

An MPI-based implementation of intelligent agents on clusters

Recent research in multi-agent systems incorporate fault tolerance concepts, but does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely 'Intelligent Agents'. A task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The feasibility of the approach is validated by implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

The use of trajectory cluster analysis to interpret trace gas measurements at Mace Head, Ireland

A first step in interpreting the wide variation in trace gas concentrations measured over time at a given site is to classify the data according to the prevailing weather conditions. In order to classify measurements made during two intensive field campaigns at Mace Head, on the west coast of Ireland, an objective method of assigning data to different weather types has been developed. Air-mass back trajectories calculated using winds from ECMWF analyses, arriving at the site in 1995–1997, were allocated to clusters based on a statistical analysis of the latitude, longitude and pressure of the trajectory at 12 h intervals over 5 days. The robustness of the analysis was assessed by using an ensemble of back trajectories calculated for four points around Mace Head. Separate analyses were made for each of the 3 years, and for four 3-month periods. The use of these clusters in classifying ground-based ozone measurements at Mace Head is described, including the need to exclude data which have been influenced by local perturbations to the regional flow pattern, for example, by sea breezes. Even with a limited data set, based on 2 months of intensive field measurements in 1996 and 1997, there are statistically significant differences in ozone concentrations in air from the different clusters. The limitations of this type of analysis for classification and interpretation of ground-based chemistry measurements are discussed.

Multivariable cluster analysis for high-speed industrial machinery

The overall operation and internal complexity of a particular production machinery can be depicted in terms of clusters of multidimensional points which describe the process states, the value in each point dimension representing a measured variable from the machinery. The paper describes a new cluster analysis technique for use with manufacturing processes, to illustrate how machine behaviour can be categorised and how regions of good and poor machine behaviour can be identified. The cluster algorithm presented is the novel mean-tracking algorithm, capable of locating N-dimensional clusters in a large data space in which a considerable amount of noise is present. Implementation of the algorithm on a real-world high-speed machinery application is described, with clusters being formed from machinery data to indicate machinery error regions and error-free regions. This analysis is seen to provide a promising step ahead in the field of multivariable control of manufacturing systems.