John Gow

Dr John Gow

Job: Associate Professor of Electronic Engineering

Faculty: Computing, Engineering and Media

School/department: School of Engineering and Sustainable Development

Research group(s): Centre for Electronic and Communications Engineering (CECE)

Address: De 51吃瓜app University, The Gateway, Leicester, LE1 9BH

T: +44 (0)116 257 7085

E: jgow@dmu.ac.uk

Close all sections

Open all sections

Personal profile

Dr John Gow MEng PhD CEng MIET received an MEng in Electronic and Electrical Engineering from Loughborough University in 1993, and a Ph.D in power electronics from the same institution in 1998. He subsequently continued research in the area of power conversion systems for building-integrated and large scale solar photovoltaic installations.Subsequent industrial opportunities in power electronics and embedded control design led to him acting as a senior design engineer developing hardware and software for high speed DSP and microcontroller based embedded control systems and power chains for inverters, industrial drives and uninterruptible power supplies.

He now works as Associate Professor of Electronic Engineering at De 51吃瓜app University with research and commercial interests in analog/digital electronics, power conversion, power electronics and embedded systems.

Research group affiliations

Institute of Engineering Sciences (IES)

Publications and outputs

dc.title: Output Feedback Stabilization for Dynamic MIMO Semi-linear Stochastic Systems with Output Randomness Attenuation dc.contributor.author: Zhang, Qichun; Hu, Liang; Gow, J. A. dc.description.abstract: In this paper, the problem of randomness attenuation is investigated for a class of MIMO semi-linear stochastic systems. To achieve this control objective, a m-block backstepping controller is designed to stabilize the closed-loop systems in probability sense. In addition, the output randomness attenuation can be achieved by optimising the design parameters using minimum entropy criterion. The effectiveness of this presented control algorithm can be verified by a given numerical example. In summary, the main contributions of this paper are characterized as follows: (1) an output feedback design method is adapted to stabilise the dynamic multi-variable semi-linear stochastic systems by block backstepping; (2) randomness of the system output is attenuated by searching the optimal design parameter based on the entropy criterion; (3) a framework of performance enhancement for stochastic systems is developed.
dc.title: Application of Artificial Neural Network and Support Vector Regression in Cognitive Radio Networks for RF Power Prediction Using Compact Differential Evolution Algorithm dc.contributor.author: Iliya, Sunday; Gongora, Mario Augusto; Goodyer, E. N.; Gow, J. A.; Shell, Jethro dc.description.abstract: Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. To enhance the selection of channel with less noise among the white spaces (idle channels), the a priory knowledge of Radio Frequency (RF) power is very important. Computational Intelligence (CI) techniques cans be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) and Support Vector Regression (SVR) models for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) FM and TV bands. Sensitivity analysis was used to reduce the input vector of the prediction models. The inputs of the ANN and SVR consist of only time domain data and past RF power without using any RF power related parameters, thus forming a nonlinear time series prediction model. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. Since CR are embedded communication devices with memory constrain limitation, the models used, implemented a novel and innovative initial weight optimization of the ANN鈥檚 through the use of compact differential evolutionary (cDE) algorithm variants which are memory efficient. This was found to enhance the accuracy and generalization of the ANN model
dc.title: Spectrum Occupancy Survey in Leicester, UK, For Cognitive Radio Application dc.contributor.author: Iliya, Sunday; Goodyer, E. N.; Gow, J. A.; Gongora, Mario Augusto dc.description.abstract: Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. Knowing the current state of spectrum utilization in frequency, time and spatial domain will enhance the implementation of CR network. In this paper, we evaluate the spectrum utilization of some selected bands in Leicester city, UK; based on long time spectrum measurements using energy detection method. This study provides evidence of gross underutilization of some licenses spectrum which can be exploited by CR for efficient spectrum utilization.
dc.title: Spectrum Hole Prediction And White Space Ranking For Cognitive Radio Network Using An Artificial Neural Network dc.contributor.author: Iliya, Sunday; Goodyer, E. N.; Gongora, Mario Augusto; Gow, J. A. dc.description.abstract: With spectrum becoming an ever scarcer resource, it is critical that new communication systems utilize all the available frequency bands as efficiently as possible in time, frequency and spatial domain. rHowever, spectrum allocation policies most of the licensed spectrums grossly underutilized while the unlicensed spectrums are overcrowded. Hence, all future wireless communication devices beequipped with cognitive capability to maximize quality of service (QoS); require a lot of time and energartificial intelligence and machine learning in cognitive radio deliver optimum performance. In this paper, we proposed a novel way of spectrum holes prediction using artificial neural network (ANN). The ANN was trained to adapt to the radio spectrum traffic of 20 channels and the trained network was used for prediction of future spectrum holes. The input of the neural network consist of a time domain vector of length six i.e. minute, hour, date, day, week and month. The output is a vector of length 20 each representing the probability of the channel being idle. The channels are ranked in order of decreasing probability of being idleminimizing We assumed that all the channels have the same noise and quality of service; and only one vacant channel is needed for communication. The result of the spectrum holes search using ANN was compared with that of blind linear and blind stochastic search and was found to be superior. The performance of the ANN that was trained to predict the probability of the channels being idle outperformed the ANN that will predict the exact channel states (busy or idle). In the ANN that was trained to predict the exact channels states, all channels predicted to be idle are randomly searched until the first spectrum hole was found; no information about search direction regarding which channel should be sensed first.
dc.title: Optimized Artificial Neural Network Using Differential Evolution for Prediction of RF Power in VHF/UHF TV and GSM 900 Bands for Cognitive Radio Networks dc.contributor.author: Iliya, Sunday; Goodyer, E. N.; Gongora, Mario Augusto; Gow, J. A.; Shell, Jethro dc.description.abstract: Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance, not just the existence or absence of primary users. If a channel is known to be noisy, even in the absence of primary users, using such channels will demand large quantities of radio resources (transmission power, bandwidth, etc) in order to deliver an acceptable quality of service to users. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). While most of the prediction schemes are based on the determination of spectrum holes, those designed for power prediction use known radio parameters such as signal to noise ratio (SNR), bandwidth, and bit error rate. Some of these parameters may not be available or known to cognitive users. In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters. The models used implemented a novel and innovative initial weight optimization of the ANN鈥檚 through the use of differential evolutionary algorithms. This was found to enhance the accuracy and generalization of the approach
dc.title: Scattering parameter approach to insertion loss prediction for 40GBASE-T systems over structured cabling dc.contributor.author: Ogundapo, O.; Duffy, A. P.; Nche, C.; Gow, J. A.
dc.title: Optimized Neural Network Using Differential Evolutionary and Swarm Intelligence Optimization Algorithms for RF Power Prediction in Cognitive Radio Network: A Comparative study dc.contributor.author: Iliya, Sunday; Goodyer, E. N.; Shell, Jethro; Gow, J. A.; Gongora, Mario Augusto dc.description.abstract: Cognitive radio (CR) technology has emerged as a promising solution to many wireless communication problems including spectrum scarcity and underutilization. The a priory knowledge of Radio Frequency (RF) power (primary signals and/ or interfering signals plus noise) in the channels to be exploited by CR is of paramount importance. This will enable the selection of channel with less noise among idle (free) channels. Computational Intelligence (CI) techniques can be applied to these scenarios to predict the required RF power in the available channels to achieve optimum Quality of Service (QoS). In this paper, we developed a time domain based optimized Artificial Neural Network (ANN) model for the prediction of real world RF power within the GSM 900, Very High Frequency (VHF) and Ultra High Frequency (UHF) TV bands. The application of the models produced was found to increase the robustness of CR applications, specifically where the CR had no prior knowledge of the RF power related parameters such as signal to noise ratio, bandwidth and bit error rate. The models used, implemented a novel and innovative initial weight optimization of the ANN鈥檚 through the use of differential evolutionary and swarm intelligence algorithms. This was found to enhance the accuracy and generalization of the ANN model. For this problem, DE/best/1/bin was found to yield a better performance as compared with the other algorithms implemented.
dc.title: Simple procedure for optimal sizing and location of a single photovoltaic generator on radial distribution feeder dc.contributor.author: Al-Sabounchi, Ammar M. Munir; Gow, J. A.; Al-Akaidi, Marwan, 1959-
dc.title: Low cost multi constellation front end for GNSS software defined receivers dc.contributor.author: Adane, Y.; Bavaro, M.; Dumville, M.; Goodyer, E. N.; Gow, J. A.
dc.title: Optimal Sizing and Location of PVDG Unit on Radial Distribution Feeder allowing no Reverse Current Flow dc.contributor.author: Al-Sabounchi, Ammar M. Munir; Gow, J. A.; Al-Akaidi, Marwan, 1959-; Al-Thani, H.

for a full listing of John Gow's publications and outputs.

Research interests/expertise

Digital control of power electronics
Power electronic systems
Renewable power generation
Power conversion for solar photovoltaics
Power electronics for audio power amplification.

Areas of teaching

Analog/digital design, embedded systems, physics of flight

Qualifications

MEng, PhD

Courses taught

Prinicples of Engineering Design
Power Electronics (undergraduate)
Power Electronics (postgraduate)

Membership of professional associations and societies

IET, CEng

Professional licences and certificates

Chartered Engineer.

Consultancy work

General analog and digital design, embedded system and high speed logic design (VHDL/Verilog), C/C++ development for embedded control, power supply design.

Current research students

Dr Ammar M Munir, completed PhD 2011, 1st supervisor
Jasim Al Sultani, intending to complete in 2012, 1st supervisor
Waleed Al-Azzawi, 2nd supervisor.

Externally funded research grants information

Knowledge Transfer Partnership to develop a next generation of global satellite navigation system. This project won the Lord Stafford award in May 2009.

Knowledge Transfer Partnership to develop embedded systems for security applications: end date 2011.

Professional esteem indicators

IEEE Transactions on Energy Conversion, reviewer, current
IEEE Transactions on Electromagnetic Compatibility, reviewer, current
IET Power Electronics, reviewer, current.

ORCID number

0000-0002-7288-2060