Periodic orbit quantization of chaotic systems with strong pruning

The three-disk system, which for many years has served as a paradigm for the usefulness of cycle expansion methods, represents an extremely hard problem to semiclassical quantization when the disks are moved closer and closer together, since (1) pruning of orbits sets in, rendering the symbolic code incomplete, and (2) the number of orbits necessary to obtain accurate semiclassical eigenvalues proliferates exponentially. In this note we show that an alternative method, viz. harmonic inversion, which does not rely on the existence of complete symbolic dynamics or other specific properties of systems, provides a key to solving the problem of semiclassical quantization of systems with strong pruning. For the closed three-disk system we demonstrate how harmonic inversion, augmented by a signal cross-correlation technique, allows one to semiclassically calculate the energies up to the 28th excited state with high accuracy.Comment: 9 pages, 3 figures, submitted to Phys. Lett.

An advanced symbolic analyzer for the automatic generation of analog circuit design equations

A tool for symbolic analysis of analog integrated circuits is presented featuring accurate simplification, pole/zero extraction, and tools for parametric AC circuit characterization. The program, called ASAP, uses signal flowgraph methods and has been written in C for portability. In its current version, ASAP is able to deal with the complexity levels arising in typical analog building blocks when described by device-level models. The ASAP inputs and outputs, the architecture, and the graphical interface are discussed

Methodological considerations concerning manual annotation of musical audio in function of algorithm development

In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1