evolutionary algorithms to select filters DOI: 10.13140/RG.2.1.3654.3204

I was searching around and came across an online copy of my PhD thesis. So I am providing links below to the full text version of it.

Use of evolutionary algorithms to select filters for evoked potential enhancement

Scott Turner

University of Leicester

Published: 2000

http://hdl.handle.net/2381/29366
DOI: 10.13140/RG.2.1.3654.3204

Abstract
Evoked potentials are electrical signals produced by the nervous system in response to a stimulus. In general these signals are noisy with a low signal to noise ratio. The aim was to investigate ways of extracting the evoked response within an evoked potential recording, achieving a similar signal to noise ratio as conventional averaging but with less repetitions per average. In this thesis, evolutionary algorithms were used in three ways to extract the evoked potentials from a noisy background. First, evolutionary algorithms selected the cut-off frequencies for a set of filters. A different filter or filter bank was produced for each data set. The noisy signal was passed through each filter in a bank of filters the filter bank output was a weighted sum of the individual filter outputs. The goal was to use three filters ideally one for each of the three regions (early, middle and late components), but the use of five filters was also investigated. Each signal was split into two time domains: the first 30ms of the signal and the region 30 to 400ms. Filter banks were then developed for these regions separately. Secondly, instead of using a single set of filters applied to the whole signal, different filters (or combinations of filters) were applied at different times. Evolutionary algorithms are used to select the duration of each filter, as well as the frequency parameters and weightings of the filters. Three filtering approaches were investigated. Finally, wavelets in conjunction with an evolutionary algorithm were used to select particular wavelets and wavelet parameters. A comparison of these methods with optimal filtering methods and averaging was made. Averages of 10 signals were found suitable, and time-varying techniques were found to perform better than applying one filter to the whole signal.

Full text versions are available from:

• https://lra.le.ac.uk/handle/2381/29366
• https://www.researchgate.net/publication/235424726_Use_of_evolutionary_algorithms_to_select_filters_for_evoked_potential_enhancement

If you'd like to find out more about Computing at the University of Northampton go to: www.computing.northampton.ac.uk. All views and opinions are the author's and do not necessarily reflected those of any organisation they are associated with

Analytics Enhancements Using Rankings

Recent MSc Computing (Internet Technology and Security) dissertation work within the School of Science and Technology, University of Northampton, UK by  Filiz Camci.

Enhancement Of Web Analytics Using Rankings In Search Engines

Abstract
Today, Internet has become an indispensable tool for people. The demand for Internet marketing has been increasing in the last decade. Web analytics tools are used to understand the needs of the customers, define the gaps of the websites and track effectiveness of marketing campaigns. Search engines are great tools to help internet users to find what they are looking for among billions of websites. Search engines aim to guide people to find the information and products they need in a short time. Companies aim to be appear in the search engine results to reach the customers and increase their profits. In addition, search engine results are great source to identify potential competitors and their strategies. This thesis aims to explore the enhancement of the web analytics by using rankings in search engine results.

The thesis involves three major parts: manual search, search engine simulation, rank tracking with web analytics tools. In each part, the trend in search engine results are analyzed and slope value of trend line representing the trend is calculated. The slope is identified to decide if there is fluctuation or trend in the search engine ranking positions. Two websites have been used to implement these steps. Google Analytics, Statcounter and Market Samurai are used as web analytics tools.

The following statements summarize the outcome of this study: 
1. Ranking positions in search engine results are crucial information for companies.
2. Web analytics tools are able to collect sufficient data to perform website analysis (including trend analysis). 
3. Currently web analytics tools generally are not flexible enough to provide sufficient analysis using the collected data. 
4. Simple trend analysis methods such as slope analysis have potential to be used for trend analysis of ranking positions in search engine results.

Supervisor: Dr Ali Al-Sherbaz and Dr Scott Turner

Computing Courses

BSc and HND Computing Provision (click on the links below for more details of the courses)

• BEng/MEng Computing (Computer Networks Engineering)
• BEng/MEng Computing (Computer Systems Engineering) 

• BSc (Hons) Computing (also available part-time)
• BSc (Hons) Computing (Graphics and Visualisation)
• BSc (Hons) Computing (Internet Technology and Security)
• BSc (Hons) Computing (Mobile Computing)
• BSc (Hons) Computing (Software Engineering)
• BSc (Hons) Computing - One Year Top-Up 
• BSc  Computer Games Development
• HND Computing (Compute​r Net​works Engineering)
• HND Computing (Computer Systems Engineering)
• HND Computing (Graphics and Visualisation)
• HND Computing (Internet Tech​nolog​y and Security)
• HND Computing (Mobile Computing)
• HND Computing (Software Engine​ering) 
• HND Com​puting

The University of Northampton's, Department of Computing and Immersive Technologies offers five courses within the MSc Computing postgraduate provision (shown below) all available either part-time or full-time. 

• MSc Computing
• MSc Computing (Internet Technology and Security)
• MSc Computing (Computer Networks Engineering)
• MSc Computing (Software Engineering)
• MSc Computing (Immersive Technologies)

The contents are the opinion of the author(s) and not necessarily the view of the University of Northampton.