[agents] International Journal of Adaptive, Resilient and Autonomic Systems: CfP + Contents of Volume 3 Issue 4

[Vincenzo De Florio]

 Dear Sirs, dear Madams,

please find herein 


 the contents of the latest issue
     of the International Journal of Adaptive, Resilient and
     Autonomic Systems (IJARAS), guest edited
     by Prof. Andrew M. Tyrrell and Prof. Tempesti, York University;
 IJARAS’ latest call for paper;
 Information on the yearly book series Advances in Adaptive, Resilient and
     Autonomic Systems, which is to reprint all papers published in IJARAS'
     volumes.


For any questions regarding IJARAS and its Advanced Book Series
please contact me through vincenzo.deflorio at ua.ac.be. Thank you very much.

 Kind regards,

Vincenzo De Florio

 

The contents of the latest issue of:

International
Journal of Adaptive, Resilient and Autonomic Systems (IJARAS)

Official Publication of the Information Resources Management
Association

Volume 3, Issue 4, October - December 2012

Published: Quarterly in Print and
Electronically

ISSN: 1947-9220 EISSN: 1947-9239

Published by IGI Publishing, Hershey-New York, USA

www.igi-global.com/ijaras

 

Guest editors: Prof. Andrew M. Tyrrell and Prof. Tempesti, York
University

Editor-in-Chief: Vincenzo De Florio, University of Antwerp and IBBT,
Belgium

 

PAPER
ONE

 

Investigating
Power Reduction for NoC-Based Spiking Neural Network Platforms using Channel
Encoding

 

Neil
McDonnell (School of Computing and Intelligent Systems, University of Ulster,
Magee Campus, Northern Ireland, UK), Snaider Carrillo (School of Computing and
Intelligent Systems, University of Ulster, Magee Campus, Northern Ireland, UK),
Jim Harkin (School of Computing and Intelligent Systems, University of Ulster,
Magee Campus, Northern Ireland, UK) and Liam McDaid (School of Computing and
Intelligent Systems, University of Ulster, Magee Campus, Northern Ireland, UK)

 

Recent
focus has been placed on exploring the possibility to switch from parallel to
serial data links between NoC routers in order to improve signal integrity in
the communication channel. However, moving streams of data between the parallel
path of the internal router and external serial-channel links between them
consumes additional power. One challenge is encoding the data and minimise the
switching activity of data in the serial links in order to reduce the
additional power dissipation; while under real-time and minimal hardware
constraints. Consequently, proposed is a novel low area/power decision circuit
for NoC channel encoding which identifies in real-time packets for encoding and
extends the existing SILENT encoders/decoders to further minimise power
consumption and demonstrates the power performance savings of the decision
circuit and modified (en)decoders using example test traffic with the EMBRACE
NoC router, a mixed signal spiking neural network (SNNs) embedded platform.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/investigating-power-reduction-noc-based/74363

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=74363&ptid=59552&t=investigating+power+reduction+for+noc-based+spiking+neural+network+platforms+using+channel+encoding

 

PAPER
TWO



Compensating
Resource Fluctuations by Means of Evolvable Hardware: The Run-Time
Reconfigurable Functional Unit Row Classifier Architecture

 

Paul Kaufmann
(Department of Computer Science, University of Paderborn, Paderborn, Germany),
Kyrre Glette (University of Oslo, Norway), Marco Platzner (University of
Paderborn, Paderborn, Germany) and Jim Torresen (University of Oslo, Norway)





The
evolvable hardware (EHW) paradigm facilitates the construction of autonomous
systems that can adapt to environmental changes and degradation of the
computational resources. Extending the EHW principle to architectural
adaptation, the authors study the capability of evolvable hardware classifiers
to adapt to intentional run-time fluctuations in the available resources, i.e.,
chip area, in this work. To that end, the authors leverage the Functional Unit
Row (FUR) architecture, a coarse-grained reconfigurable classifier, and apply
it to two medical benchmarks, the Pima and Thyroid data sets from the UCI
Machine Learning Repository. While quick recovery from architectural changes
was already demonstrated for the FUR architecture, the authors also introduce
two reconfiguration schemes helping to reduce the magnitude of degradation
after architectural reconfiguration.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/compensating-resource-fluctuations-means-evolvable/74364

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=74364&ptid=59552&t=compensating+resource+fluctuations+by+means+of+evolvable+hardware%3a+the+run-time+reconfigurable+functional+unit+row+classifier+architecture

 

PAPER
THREE

 

Automatic Machine Code Generation for a Transport Triggered Architecture
using Cartesian Genetic Programming

James Alfred Walker (Department of Electronics, University of York, York,
UK), Yang Liu (Department of Electronics, University of York, York, UK),
Gianluca Tempesti (Department of Electronics, University of York, York, UK),
Jon Timmis (Departments of Electronics and Computer Science, University of
York, York, UK) and Andy M. Tyrrell (Department of Electronics, University of
York, York, UK) 

Transport
triggered architectures are used for implementing bio-inspired systems due to
their simplicity, modularity and fault-tolerance. However, producing efficient,
optimised machine code for such architectures is extremely difficult, since
computational complexity has moved from the hardware-level to the
software-level. Presented is the application of Cartesian Genetic Programming
(CGP) to the evolution of machine code for a simple implementation of transport
triggered architecture. The effectiveness of the algorithm is demonstrated by
evolving machine code for a 4-bit multiplier with three different levels of
parallelism. The results show that 100% successful solutions were found by CGP
and by further optimising the size of the solutions, it’s possible to find
efficient implementations of the 4-bit multiplier. Further analysis of the
solutions showed that use of loops within the CGP function set could be
beneficial and was demonstrated by repeating the earlier 4-bit multiplier
experiment with the addition of a loop function.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/automatic-machine-code-generation-transport/74365

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=74365&ptid=59552&t=automatic+machine+code+generation+for+a+transport+triggered+architecture+using+cartesian+genetic+programming

 

PAPER
FOUR

 

Multi-View Human Body Pose Estimation with CUDA-PSO

 

Luca Mussi (Henesis s.r.l., Parma, Italy), Spela Ivekovic (Department of
Mechanical & Aerospace Engineering, University of Strathclyde, Glasgow,
UK), Youssef S.G. Nashed (Department of Information Engineering, University of
Parma, Parma, Italy) and Stefano Cagnoni (Department of Information Engineering,
University of Parma, Parma, Italy)

 

The
authors formulate the body pose estimation as a multi-dimensional nonlinear
optimization problem, suitable to be approximately solved by a meta-heuristic,
specifically, the particle swarm optimization (PSO). Starting from multi-view
video sequences acquired in a studio environment, a full skeletal configuration
of the human body is retrieved. They use a generic subdivision-surface body
model in 3-D to generate solutions for the optimization problem. PSO then looks
for the best match between the silhouettes generated by the projection of the
model in a candidate pose and the silhouettes extracted from the original video
sequence. The optimization method, in this case PSO, is run in parallel on the
Graphics Processing Unit (GPU) and is implemented in Cuda-C™ on the nVidia
CUDA™ architecture. The authors compare the results obtained by different
configurations of the camera setup, fitness function, and PSO neighborhood
topologies.

 

To obtain a copy of the entire article, click on the link below.

http://www.igi-global.com/article/multi-view-human-body-pose/74366

 

To read a PDF sample of this article, click on the link below.

http://www.igi-global.com/viewtitlesample.aspx?id=74366&ptid=59552&t=multi-view+human+body+pose+estimation+with+cuda-pso

 

*****************************************************

For full copies of the above articles, check for this issue of the International Journal of Adaptive,
Resilient and Autonomic Systems (IJARAS) in your institution's library. This journal is also included in
the IGI Global aggregated "InfoSci-Journals"
database: http://www.igi-global.com/eresources/infosci-journals.aspx.

*****************************************************

 

CALL
FOR PAPERS

 

The International Journal of Adaptive, Resilient and Autonomic
Systems (IJARAS) examines systems and organizations characterized by the
following two properties: the ability to self-adapt to the characteristics
of rapidly changing and turbulent environments by adopting complex
individual and social strategies and the ability to control their
changes to prevent the invalidation of their original mission statements.
The central focus of IJARAS is on modeling, simulating, designing,
developing, maintaining, evaluating, and benchmarking such “entelechial
systems”. Perception, awareness, and the planning and execution of
resilient adaptation behaviors in systems and organizations are central topics of
the journal. Such systems range from individual and simple embedded systems
with limited perception and predefined specialized behaviors to complex
hybrid social organizations like cyber-physical societies or
service-oriented communities, whose emerging behaviors are many and, in
some cases, difficult to predict. IJARAS focuses on the full spectrum of
these problems providing academicians, practitioners, and researchers with
awareness and insight on conceptual models, applied and theoretical approaches,
paradigms, and other technological innovations on self-adaptive and/or
self-resilient systems and organizations of any scale and nature.

Mission

Society is currently
experiencing the increasing population of “things,” able to autonomously
link with each other and enact complex strategies to achieve tasks.
The emergence of the Semantic Web, the Internet-of-Things, Ambient
Intelligence, and cyber-physical societies make it impossible to capture the
intricacies of the future highly dynamic and turbulent networks of
interrelated computer-based and hybrid components. As such, it is
important that systems are designed to self-adapt to changes without
diverging from their intended functions as prescribed in their
specifications. The mission of the International Journal of Adaptive,
Resilient and Autonomic Systems (IJARAS) is to offer awareness and
visibility to novel techniques and methods to achieve self-adaptability
and self-resilience when systems and organizations are deployed in environments
where change is the rule rather than the exception. IJARAS is also a tool
to enhance the awareness of the key role played by said techniques and
methods: engineering self-adaptive and self-resilient systems and organizations
is an urgent necessity to keep society resilient in the face of the
technology that sustains it. The journal pursues its mission by
addressing researchers, practitioners, engineers, educators, and
professionals and by publishing novel results on each of the diverse
components of such a complex and multi-disciplinary research problem.

Topics
Covered

Topics to be discussed in
this journal include (but are not limited to) the following:

- Adaptive data integrity

- Adaptive fault-tolerance

- Analytical and
simulation tools to measure a system’s ability to withstand faults and   

  optimally re-adjust
to new environments

- Architecture-based
adaptation

- Autonomic applications

- Autonomous and adaptive
systems in robotics

- Biologically inspired
mechanisms to enact complex adaptation strategies

- Collective strategies
for adaptation and resilience, including cooperation, competition,  

  co-opetition,
co-innovation, and co-evolution

- Complex
adaptive-and-resilient systems and organizations

- Context- and
situation-awareness

- Design-time/run-time
methods and tools to identify and enforce optimal trade-offs between 

  energy consumption,
performance, safety, and security

- Dynamics of complex
adaptive and resilient systems and organizations

- Human aspects

- Evolutionary approaches
to autonomic computing, resilience, and adaptive systems

- Mechanisms to model,
design, express, and develop adaptive, autonomic, and resilient   

  systems

- Methods to express
resilience (e.g., resilience policies and contracts)

- Perception and
introspection capabilities

- Personalization

- Quality of experience

- Recovery-oriented
computing

- Resilient adaptation
behavior composition

- Resilient adaptation
planning

- Resilience and
adaptation in management science

- Resilience engineering

- Role of diversity in the
emergence of survivability, innovability, value capture, etc.

- Role of organizations on
the emergence of adaptation and resilience: heterarchies,   

  holarchies, fractal
social organizations, etc.

- Scalable, maintainable,
and cost-effective provisions located at all system levels  

  to achieve
adaptability and dependability

- Self-adaptive and
self-resilient systems: models, design, development, maintenance,     

  evaluation, and
benchmarking issues

- Software elasticity:
techniques, tools, and approaches to absorb and tolerate the     

  consequences of
failures, attacks, and changes within and without system boundaries

Submissions
and enquiries:

All inquiries and submissions should
be sent to:

Editor-in-Chief: Vincenzo De Florio at vincenzo.deflorio at gmail.com; vincenzo.deflorio at ua.ac.be

For enquiries please contact the editor in chief 

 

Advanced
Book Series:

 - An Advances Book Series is now
associated with IJARAS: Advances in Adaptive, Resilient and Autonomic
Systems (AARAS). All papers published in IJARAS will also appear (possibly
extended) as chapters in the volumes in this series. The first volume of
this series is available from March 2012 as "Technological
Innovations in Adaptive and DependableSystems: Advancing Models and
Concepts" and may be ordered fromhttp://www.igi-global.com/book/technological-innovations-adaptive-dependable-systems/59728. A second volume, entitled "Innovations and Approaches for
Resilient and Adaptive Systems," is available from September 2012 and may
be ordered from http://www.igi-global.com/book/innovations-approaches-resilient-adaptive-systems/66376

For enquiries please
contact the editor in chief through vincenzo.deflorio at ua.ac.be.