Call for Papers
IEEE TRANSACTIONS ON NEURAL NETWORKS
Special Issue on Adaptive Learning Systems in Communication Networks

    The recent years have seen an explosive growth in the progress and
adoption of communication networks for data and telecommunication
applications. In particular, the emergence of the Internet as a new medium
for business transactions, government services, information acquisition,
computing and communication has created a vast array of problems unforeseen
just a few years ago. As the capabilities of the available networking
infrastructure improve many foresee convergence of data, voice and video
transport over what is currently known as the Internet or its derivatives.

    Communication networks and internetworks, and in particular the
Internet, have been characterized as the ultimate data-rich environments,
dynamically evolving and expanding practically without any centralized
control. Such data-rich, unstructured environments present a particular
challenge for traditional methods of analysis and design. Adaptive learning
methods, in general, including adaptive signal processing, neural networks,
fuzzy logic and other data-driven methods and algorithms are in the unique
position to offer credible alternatives. Such approaches have the potential
for solving and improving the available solutions for some of the toughest
problems faced in this newly emerging set of interrelated information
technologies.

    The goal of the proposed special issue is two-fold: 
·  to highlight the on-going research in the field of adaptive
learning systems, and in particular adaptive signal processing
and neural networks, as it is applicable to computer and
communication networks, and,
·  to present to the neural networks community and to others
interested in adaptive learning systems, in general, a variety of
new and challenging problems and their proposed solutions,
originating from the rapidly expanding universe of computer
and communication networks.

As the use of these technologies spreads, numerous modeling,
estimation, control, classification, clustering and signal processing
problems are emerging. Many of these problems currently have no
satisfactory solutions and some have been addressed with ad-hoc
solutions with much room remaining for improvements. A common
underlying theme of these problems is that they are
·  very data-rich,
·  represent a dynamically changing environment where the lack
   of valid mathematical models is predominant, and,
·  representative of systems with minimal or no centralized
   control.

        These problems appear amenable to data-driven methods and
algorithms, such as adaptive learning methods, including neural networks and
other non-parametric or semi-parametric approaches. This special issue will
welcome contributions with proposed approaches to existing problems, either
with currently known or unknown solutions, and to new problems in the
subject areas of computer and communication networks. The focus of the
proposed solutions will be on data-driven or the so-called measurement-based
methods and algorithms, rooted in the general areas of adaptive learning
methods.
 
   The Special Issue papers will cover topics of interest that include a
broad range of underlying communication network infrastructure technologies.
Papers are solicited from, but not limited to, the following topics:

Network Management Topics
·  Methods and algorithms for network traffic analysis, modeling
   and characterization
·  Network performance measurement and analysis techniques
·  Network fault monitoring and diagnosis methods
·  Network security and privacy, including intrusion detection methods
·  Approaches and methods for Quality of Service in IP networks
·  Scalable routing algorithms
·  Decentralized congestion control algorithms
·  Novel admission control algorithms
·  Control algorithms for high-speed network access technologies
·  Application of "new approaches" in adaptive learning systems
   to data-intensive tasks in complex networks

Content Management Topics
·  Approaches for scalable Web caching and related optimization methods
·  Novel solutions to operational problems in content delivery and
   distribution networks
·  Web data mining and knowledge discovery - scalability and
   comparison of methods
·  Web personalization methods
·  Information hiding techniques and digital rights management
·  Novel solutions to information access and retrieval for dynamic
   Web content
·  Efficient compression algorithms and coding for continuous
   digital media - multimedia content
·  Architectures for Quality of Service guarantees in real-time
   distributed applications
·  Uncertainty management in real-time distributed applications
·  Concepts in real-time distributed applications enabled by new
   communication network technologies
 

Guest Editors:
Alexander G. Parlos, Texas A&M University, College Station, 
Texas, USA (Coordinator)
Chuanyi Ji, Georgia Institute of Technology, Atlanta, Georgia, USA
K. Claffy, San Diego Supercomputer Center, University of
California, San Diego, California, USA
Thomas Parisini, University of Trieste, Trieste, Italy
Marco Baglietto, University of Genoa, Genoa, Italy
    
    Manuscripts will be screened for topical relevance, and those that pass
the screening process will undergo the standard review process of the IEEE
Transactions on Neural Networks (see the instructions for authors in the
IEEE Transactions on Neural Networks). Paper submission deadline is November
1, 2003. Prospective authors are encouraged to submit an abstract by
September 1, 2003. This will help in the planning and review process. The
final Special Issue will be published in the Fall of 2004. Electronic
manuscript submission is mandatory and only papers in pdf format will be
considered for review. All manuscripts should be sent to the Coordinator of
the guest editorial team at a-parlos@tamu.edu.

    Researchers interested in reviewing manuscripts for the Special Issue
should contact the Guest Editors via e-mail and provide a brief description
of expertise.