CASA, the Common Astronomy Software Applications, is the primary data processing software for the Atacama Large Millimeter/submillimeter Array (ALMA) and the Karl G. Jansky Very Large Array (VLA), and is frequently used also for other radio telescopes. The CASA software can handle data from single-dish, aperture-synthesis, and Very Long Baseline Interferometery (VLBI) telescopes. One of its core functionalities is to support the calibration and imaging pipelines for ALMA, VLA, VLA Sky Survey, and the Nobeyama 45 m telescope. This paper presents a high-level overview of the basic structure of the CASA software, as well as procedures for calibrating and imaging astronomical radio data in CASA. CASA is being developed by an international consortium of scientists and software engineers based at the National Radio Astronomy Observatory (NRAO), the European Southern Observatory, the National Astronomical Observatory of Japan, and the Joint Institute for VLBI European Research Infrastructure Consortium (JIV-ERIC), under the guidance of NRAO.

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Abstract-End-to-end Internet packet dynamics is a complex problem for which models available to date are at best incomplete. A major research problem in Internet transport layer protocols is the development of rate control mechanisms that can cope with the requirements of a growing diversity of technologies, applications and services. This paper describes novel mechanisms for intelligent end-to-end traffic rate control in Internet by means of fuzzy systems. We first outline a fuzzy logic based generalization of TCP (Transport Control Protocol) rate control principles. The design of a fuzzy TCP-like windowbased rate controller is then described. A systematic fuzzy systems design methodology is used in order to simulate and implement the system as an experimental tool. A comparative evaluation of simulation an implementation results from the fuzzy rate controller as compared to that of traditional controllers is outlined. Besides being a useful modelling approach, the fuzzy rule based rate controller is shown to outperform other approaches with regards to a number of criteria.

Abstract-A new software tool for time series prediction by means of fuzzy inference systems is reported. This tool, named xftsp, implements a novel methodology for time series prediction based on methods for automatic fuzzy systems identification and supervised learning combined with statistical methods for nonparametric residual variance estimation. xftsp is designed as a tool integrated in the Xfuzzy development environment for fuzzy systems. Experiments carried out on a number of time series benchmarks show the advantages of xftsp in terms of both accuracy and computational requirements as compared against Least-Squared Support Vector Machines, an established technique in the field of time series prediction.

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