Wavelet transforms have been one of the important signal processing developments in the last decade, especially for applications such as time-frequency analysis, data compression, segmentation and vision. Although several efficient implementations of wavelet transforms have been derived, their computational burden is still considerable. This paper describes two generic parallel implementations of wavelet transforms based on the pipeline processor farming methodology which have the potential to achieve real-time performance. Results show that the parallel implementation of the over-sampled Wavelet Transform achieves virtually linear speedup, while the parallel implementation of the Discrete Wavelet Transform (DWT) also out-performs the sequential version, provided that the filter order is large. The DWT parallelisation performance improves with increasing data length and filter order while the frequency domain implementation performance is independent of wavelet filter order. Parallel p...

The recent availability of heterogeneous high-throughput data types has increased the need for scalable in silico methods with which to integrate data related to the processes of regulation, protein synthesis, and metabolism. A sequence-based framework for modeling transcription and translation in prokaryotes has been established and has been extended to study the expression state of the entire Escherichia coli genome. The resulting in silico analysis of the expression state highlighted three facets of gene expression in E. coli: (i) the metabolic resources required for genome expression and protein synthesis were found to be relatively invariant under the conditions tested; (ii) effective promoter strengths were estimated at the genome scale by using global mRNA abundance and half-life data, revealing genes subject to regulation under the experimental conditions tested; and (iii) large-scale genome location-dependent expression patterns with approximately 600-kb periodicity were detected in the E. coli genome based on the 49 expression data sets analyzed. These results support the notion that a structured model-driven analysis of expression data yields additional information that can be subjected to commonly used statistical analyses. The integration of heterogeneous genome-scale data (i.e., sequence, expression data, and mRNA half-life data) is readily achieved in the context of an in silico model. The increasing availability of complete genome sequences has ushered in an era of genome-enabled science that permits

The genome-wide location of RNA polymerase binding sites was determined in Escherichia coli using chromatin immunoprecipitation and microarrays (chIP-chip). Cross-linked chromatin was isolated in triplicate from rifampin-treated cells, and DNA bound to RNA polymerase was precipitated with an antibody specific for the � � subunit. The DNA was amplified and hybridized to “tiled ” oligonucleotide microarrays representing the whole genome at 25-bp resolution. A total of 1,139 binding sites were detected and evaluated by comparison to gene expression data from identical conditions and to 961 promoters previously identified by established methods. Of the detected binding sites, 418 were located within 1,000 bp of a known promoter, leaving 721 previously unknown RNA polymerase binding sites. Within 200 bp, we were able to detect 51% (189/368) of the known �70-specific promoters occurring upstream of an expressed open reading frame and 74 % (273/368) within 1,000 bp. Conversely, many known promoters were not detected by chIP-chip, leading to an estimated 26 % negative-detection rate. Most of the detected binding sites could be associated with expressed transcription units, but 299 binding sites occurred near inactive transcription units. This map of RNA polymerase binding sites represents a foundation for studies of transcription factors in E. coli and an important evaluation of the chIP-chip technique. RNA polymerase (RNAP) in Escherichia coli is a key factor

this paper suggests the use of parallel processing based on the pipeline processor farming (PPF) methodology. The paper is mainly focussed on parallel implementation of the discrete wavelet transform (DWT), which is extensively used in image processing applications. The parallel environment in which the algorithms were implemented comprised two TMS320C40 boards with a total of six processors. Discrete Wavelet Transform (DWT)

a tutorial

In this thesis a new signal processing technique is presented. This technique exploits the use of pulses as the signalling mechanism. This Palmo 1 signalling method applied to signal processing is novel, combining the advantages of both digital and analogue techniques. Pulsed signals are robust, inherently low-power, easily regenerated, and easily distributed across and between chips. The Palmo cells used to perform analogue operations on the pulsed signals are compact, fast, simple and programmable.