Idl Signal Processing Library 1.0

We make available a library of documented IDL .pro files as well as a shareable object library that allows IDL to call routines from LAPACK. The routines are for use in the spectral analysis of time series data. The primary focus of these routines are David Thomson's multitaper methods but a whole range of functions will be made available in future revisions of the submission. At present routines are provided to carry out the following operations: calculate prolate spheroidal sequences and eigenvalues, project time-series into frequency bands, calculate spectral estimates with or without moving windows, and calculate the cross-coherence between two time series as a function of frequency as well as the coherence between frequencies for a single time series.Comment: 13 IDL .pro files, 1 .html file, 1 .ps file, 1 license file. Download the source for the IDL files (save as .tar.gz) Read idl_lib.ps for instructions on use. Originally submitted to the neuro-sys archive which was never publicly announced (was 9801001

Sphere-constrained ML detection for frequency-selective channels

The maximum-likelihood (ML) sequence detection problem for channels with memory is investigated. The Viterbi algorithm (VA) provides an exact solution. Its computational complexity is linear in the length of the transmitted sequence, but exponential in the channel memory length. On the other hand, the sphere decoding (SD) algorithm also solves the ML detection problem exactly, and has expected complexity which is a low-degree polynomial (often cubic) in the length of the transmitted sequence over a wide range of signal-to-noise ratios. We combine the sphere-constrained search strategy of SD with the dynamic programming principles of the VA. The resulting algorithm has the worst-case complexity determined by the VA, but often significantly lower expected complexity

Nonlinear limits to the information capacity of optical fiber communications

The exponential growth in the rate at which information can be communicated through an optical fiber is a key element in the so called information revolution. However, like all exponential growth laws, there are physical limits to be considered. The nonlinear nature of the propagation of light in optical fiber has made these limits difficult to elucidate. Here we obtain basic insights into the limits to the information capacity of an optical fiber arising from these nonlinearities. The key simplification lies in relating the nonlinear channel to a linear channel with multiplicative noise, for which we are able to obtain analytical results. In fundamental distinction to the linear additive noise case, the capacity does not grow indefinitely with increasing signal power, but has a maximal value. The ideas presented here have broader implications for other nonlinear information channels, such as those involved in sensory transduction in neurobiology. These have been often examined using additive noise linear channel models, and as we show here, nonlinearities can change the picture qualitatively.Comment: 1 figure, 7 pages, submitted to Natur

Sustainability and Discounted Utilitarianism in Models of Economic Growth

Discounted utilitarianism treats generations unequally and leads to seemingly unappealing consequences in some models of economic growth. Instead, this paper presents and applies sustainable discounted utilitarianism (SDU). SDU respects the interests of future generations and resolves intergenerational conflicts by imposing on discounted utilitarianism that the evaluation be insensitive to the interests of the present generation if the present is better off than the future. An SDU social welfare function always exists. We provide a convenient sufficient condition to identify SDU optima and apply SDU to two well-known models of economic growth. We also investigate the axiomatic basis for SDU.intergenerational equity, sustainability, discounted utilitarianism, egalitarian consumption streams, efficiency, exhaustible resources

Introducing new constructs for data modelling and column generation in LP modelling languages

Through popular implementation of structured query language (SQL) and query-by-example(QBE) relational databases have become the de-facto industry standard for data modelling.We consider the indices, sets, and the declarative form of Linear Programming (LP) modelling languages and introduce new constructs which provide direct link to the database systems. The models constructed in this way are data driven and display a dynamicstructure. We then show how this approach can be naturally extended to include column generation features stated in procedural forms within an otherwise declarative modelling paradigm

Sets and indices in linear programming modelling and their integration with relational data models

LP models are usually constructed using index sets and data tables which are closely related to the attributes and relations of relational database (RDB) systems. We extend the syntax of MPL, an existing LP modelling language, in order to connect it to a given RDB system. This approach reuses existing modelling and database software, provides a rich modelling environment and achieves model and data independence. This integrated software enables Mathematical Programming to be widely used as a decision support tool by unlocking the data residing in corporate databases