Mark S. Leeson

Mark S. Leeson received the degrees of BSc and BEng with First Class Honors in Electrical and Electronic Engineering from the University of Nottingham, UK, in 1986. He then obtained a PhD in Engineering from the University of Cambridge, UK, in 1990. From 1990 to 1992 he worked as a Network Analyst for National Westminster Bank in London. After holding academic posts in London and Manchester, in 2000 he joined the School of Engineering at Warwick, where he is now an Associate Professor. His major research interests are coding and modulation, ad hoc networking, optical communication systems and evolutionary optimization. To date, Dr. Leeson has over 180 publications and has supervised nine successful research students. He is a Senior Member of the IEEE, a Chartered Member of the UK Institute of Physics, and a Fellow of the UK Higher Education Academy.

Publications

Energy Efficiency of Coding Schemes for Underwater Wireless Sensor Networks
Energy Efficiency of Coding Schemes for Underwater Wireless Sensor Networks
Mark S. Leeson, Sahil Patel. © 2015. 29 pages.
Underwater Wireless Sensor Networks (UWSNs) are used in applications such as mineral exploration and environmental monitoring, and must offer reliability and energy efficiency....
Optical Wireless Communications in Vehicular Systems
Optical Wireless Communications in Vehicular Systems
Matthew Higgins, Zeina Rihawi, Zaiton Abdul Mutalip, Roger Green, Mark S. Leeson. © 2013. 14 pages.
This chapter reviews some of the network topologies and technologies within current vehicular systems. This is then followed by a proposal from the authors with initial viability...
Resilient Optical Network Design: Advances in Fault-Tolerant Methodologies
Resilient Optical Network Design: Advances in Fault-Tolerant Methodologies
Yousef S. Kavian, Mark S. Leeson. © 2012. 366 pages.
Dense wavelength division multiplexing (DWDM) optical networks are prone to failure, which can potentially lead to a catastrophic loss of data and revenue. Given this, one of the...
Intelligent Systems for Machine Olfaction: Tools and Methodologies
Intelligent Systems for Machine Olfaction: Tools and Methodologies
Evor L. Hines, Mark S. Leeson. © 2011. 354 pages.
Intelligent systems are systems that, given some data, are able to learn from that data. This makes it possible for complex systems to be modeled and/or for performance to be...
Blind Equalization for Broadband Access using the Constant Modulus Algorithm
Blind Equalization for Broadband Access using the Constant Modulus Algorithm
Mark S. Leeson, Eugene Iwu. © 2011. 26 pages.
The cost of laying optical fiber to the home means that digital transmission using copper twisted pairs is still widely used to provide broadband Internet access via Digital...
Evolutionary Algorithms for Multisensor Data Fusion
Evolutionary Algorithms for Multisensor Data Fusion
Jianhua Yang, Evor L. Hines, John E. Sloper, D. D. Iliescu, Mark S. Leeson. © 2011. 16 pages.
The aim of Multisensor Data Fusion (MDF) is to eliminate redundant, noisy or irrelevant information and thus find an optimal subset from an array of high dimensionality. An...
Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach
Detection of Diseases and Volatile Discrimination of Plants: An Electronic Nose and Self-Organizing Maps Approach
Reza Ghaffari, Fu Zhang, D. D. Iliescu, Evor L. Hines, Mark S. Leeson, Richard Napier. © 2011. 17 pages.
The diagnosis of plant diseases is an important part of commercial greenhouse crop production and can enable continued disease and pest control. A plant subject to infection...
Tomato Plant Health Monitoring: An Electronic Nose Approach
Tomato Plant Health Monitoring: An Electronic Nose Approach
Fu Zhang, D. D. Iliescu, Evor L. Hines, Mark S. Leeson. © 2011. 18 pages.
Electric noses (e-noses), taking their inspiration from the human olfactory system, have been extensively used in food quality control and human disease monitoring. This chapter...
Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree
Enhancing the Classification of Eye Bacteria Using Bagging to Multilayer Perceptron and Decision Tree
Xu-Qin Li, Evor L. Hines, Mark S. Leeson, D. D. Iliescu. © 2011. 17 pages.
Eye bacteria are vital to the diagnosis of eye disease, which makes the classification of such bacteria necessary and important. This chapter aims to classify different kinds of...
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
A Genetic Algorithm-Artificial Neural Network Method for the Prediction of Longitudinal Dispersion Coefficient in Rivers
Jianhua Yang, Evor L. Hines, Ian Guymer, Daciana D. Iliescu, Mark S. Leeson, Gregory P. King, XuQuin Li. © 2009. 17 pages.
In this chapter a novel method, the Genetic Neural Mathematical Method (GNMM), for the prediction of longitudinal dispersion coefficient is presented. This hybrid method utilizes...