Dr Benjamin Passow

Job: Senior Lecturer in Computational Intelligence

Faculty: Technology

School/department: School of Engineering and Sustainable Development

Research group(s): DIGITS - DMU Interdisciplinary Group in Intelligent Transport Systems - Centre for Computational Intelligence (CCI)

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 255 1551 ext. 2650

E: benpassow@ieee.org

W: www.tech.dmu.ac.uk/~benpassow

 

Personal profile

Benjamin N. Passow completed his PhD and MSc at the Centre for Computational Intelligence in 2011 and 2007 respectively. His PhD thesis is entitled "Fusion of Intelligent Control and Acoustic Sensing for an Autonomous Helicopter". More information about this research can be found here.

Ben currently works as a Research Fellow in the DIGITS group on the European Space Agency funded project iTRAQ - Integrated Traffic Management and Air Quality Control Using Space Services. This research aims at developing a dynamic traffic management system to optimise the use of the existing road network whilst meeting growing demands to sustain high standards of air quality in urban environments. iTRAQ is being developed in a partnership with the University of Leicester, Leicester City Council, Astrium, and De Montfort University.

Research group affiliations

DIGITS - DMU Interdisciplinary Group in Intelligent Transport Systems

CCI – Centre for Computational Intelligence  

Publications and outputs 

  • A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing
    A loosely coupled hybrid meta-heuristic algorithm for the static independent task scheduling problem in grid computing Younis, Muhanad Tahrir; Yang, Shengxiang; Passow, Benjamin N. Task scheduling is one of the most difficult problems in grid computing systems. Therefore, various studies have been proposed to present methods which provide efficient schedules. Meta-heuristic approaches are among the methods which have proven their efficiency in this domain. However, the literature shows that hybridizing two or more meta-heuristics can improve performance to a greater extent than stand-alone algorithms as the new high-level algorithm will inherit the best features of the hybridized algorithms. In this paper, a loosely coupled hybrid meta-heuristic algorithm is proposed for solving the static independent task scheduling problem in grid computing. It combines ant colony optimization and variable neighborhood search, where the former operates first and whose output is subsequently improved by the latter. The experimental results show that the proposed algorithm achieves better task-machine mapping in terms of minimizing makespan than other selected approaches from the literature.
  • Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing
    Meta-heuristically seeded genetic algorithm for independent job scheduling in grid computing Younis, Muhanad Tahrir; Yang, Shengxiang; Passow, Benjamin N. Grid computing is an infrastructure which connects geographically distributed computers owned by various organizations allowing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic algorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques.
  • Neighbouring Link Travel Time Inference Method Using Artificial Neural Network
    Neighbouring Link Travel Time Inference Method Using Artificial Neural Network Luong H. Vu; Passow, Benjamin N.; Paluszczyszyn, D.; Deka, Lipika; Goodyer, E. This paper presents a method for modelling relationship between road segments using feed forward back-propagation neural networks. Unlike most previous papers that focus on travel time estimation of a road based on its traffic information, we proposed the Neighbouring Link Inference Method (NLIM) that can infer travel time of a road segment (link) from travel time its neighbouring segments. It is valuable for links which do not have recent traffic information. The proposed method learns the relationship between travel time of a link and traffic parameters of its nearby links based on sparse historical travel time data. A travel time data outlier detection based on Gaussian mixture model is also proposed in order to reduce the noise of data before they are applied to build NLIM. Results show that the proposed method is capable of estimating the travel time on all traffic link categories. 75% of models can produce travel time data with mean absolute percentage error less than 22%. The proposed method performs better on major than minor links. Performance of the proposed method always dominates performance of traditional methods such as statistic-based and linear least square estimate methods. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search
    Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search Nguyen, P. T. M.; Passow, Benjamin N.; Yang, Yingjie Multi-Objective Evolutionary Algorithms (MOEAs) and transport simulators have been widely utilized to optimise traffic signal timings with multiple objectives. However, traffic simulations require much processing time and need to be called repeatedly in iterations of MOEAs. As a result, traffic signal timing optimisation process is time-consuming. Anytime behaviour of an algorithm indicates its ability to return as good solutions as possible at any time during its implementation. Therefore, anytime behavior is desirable in traffic signal timing optimisation algorithms. In this study, we propose an optimisation strategy (NSGA-II-LS) to improve anytime behaviour based on NSGAII and local search. To evaluate the validity of the proposed algorithm, the NSGA-II-LS, NSGA-II and MODEA are used to optimize signal durations of an intersection in Andrea Costa scenario. Results of the experiment show that the optimization method proposed in this study has good anytime behaviour in the traffic signal timings optimization problem. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Adaptive-mutation compact genetic algorithm for dynamic environments
    Adaptive-mutation compact genetic algorithm for dynamic environments Gongora, Mario Augusto; Coupland, Simon; Passow, Benjamin N.; Uzor, C. J. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
  • Encouraging Active Commuting through monitoring and analysis of commuter travel method habits
    Encouraging Active Commuting through monitoring and analysis of commuter travel method habits Hasshu, Salim; Chiclana, Francisco; Passow, Benjamin N.; Elizondo, David The aim of this research is to understand and encourage healthier commuter travel method habits. Commuters who choose healthier travel, such as; walking, cycling or public transport methods, are known as Active Commuters (AC). However current literature suggests private car use is still the dominant method of transport. Additionally, there are very few AC monitoring and analysis applications for mobile devices, which lead to the following research question; “If commuters are able to monitor and analyse their travel habits, would this encourage them to choose AC methods?” In this work we propose a novel methodology that investigates this question. This technique was implemented and tested as an Android mobile application, giving valuable insights into AC habits. The Active Commute Tracker (ACT) mobile application was developed to include the following three components: (1) Commute Transport Method Calculation component, (2) Health component and (3) Sharing component. ACT allows users to monitor and record their commute method, distance travelled in total and commute health analysis. A basic version of this data can be shared on Facebook. Users tested the application for a set number of days and provided feedback of functionality, but more importantly whether or not it encouraged AC. Feedback from users confirmed that there is a demand for an application of this nature. No user was discouraged as a direct result of ACT.
  • Range extended for electric vehicle based on driver behaviour
    Range extended for electric vehicle based on driver behaviour Al-Doori, Moath; Paluszczyszyn, D.; Elizondo, David; Passow, Benjamin N.; Goodyer, E. N. Driver behaviour has been considered one of the main factors that contribute to increase fuel consumption, CO2 emissions, traffic accidents and causalities. Thus, the concept of detecting and classifying driver behaviour i s vital when tackling these challenges. Recognition of the driver behaviour is a difficult task as in the real-world, the driving behaviour is effected by many factors e.g. traffic, road conditions, duration of the journey etc. Many approaches have considered the use of Computational Intelligence techniques, to develop a driver behaviour detection system. In this paper we concentrate on the impact of driver behaviour on the energy consumption and thereby on the range of electric vehicles. A new architecture is proposed to show how computational intelligence techniques could interact with the contextual information collected from the vehicle, the driver and external environment. A neural network model is used to classify the driver behaviour, and then this classification is used in a fuzzy logic controller to make balanced managements to the range extender operation.
  • Multiple sensor outputs and computational intelligence towards estimating state and speed for control of lower limb prostheses
    Multiple sensor outputs and computational intelligence towards estimating state and speed for control of lower limb prostheses Hardaker, Pamela; Passow, Benjamin N.; Elizondo, David For as long as people have been able to survive limb threatening injuries prostheses have been created. Modern lower limb prostheses are primarily controlled by adjusting the amount of damping in the knee to bend in a suitable manner for walking and running. Often the choice of walking state or running state has to be controlled manually by pressing a button. This paper examines how this control could be improved using sensors attached tofa the limbs of two volunteers. The signals from the sensors had features extracted which were passed through a computational intelligence system. The system was used to determine whether the volunteer was walking or running and their movement speed. Two new features are presented which identify the movement states of standing, walking and running and the movement speed of the volunteer. The results suggest that the control of the prosthetic limb could be improved.
  • Logan's run: Lane optimisation using genetic algorithms based on nsga-ii
    Logan's run: Lane optimisation using genetic algorithms based on nsga-ii Witheridge, S.; Passow, Benjamin N.; Shell, Jethro Whilst bus lanes are an important tool to ensure bus time reliability their presence can be detrimental to urban traffic. In this paper a Non-dominated Sorting Genetic Algorithm (NSGA-II) has been adopted to study the effect of bus lanes on urban traffic in terms of location and time of operation. Due to the complex nature of this problem traditional search would not be feasible. An artificial arterial route has been modelled from real data to evaluate candidate solutions. The results confirm this methodology for the purpose of studying and identifying bus lane locations and times of operation. Additionally it is shown that bus lanes can exist on an arterial link without exclusively occupying a continuous lane for large periods of time. Furthermore results indicate a use for this methodology over a larger scale and potential near real-time operation.
  • Adapting Traffic Simulation for Traffic Management: A Neural Network Approach
    Adapting Traffic Simulation for Traffic Management: A Neural Network Approach Passow, Benjamin N.; Elizondo, David; Chiclana, Francisco; Witheridge, S.; Goodyer, E. N. Static models and simulations are commonly used in urban traffic management but none feature a dynamic element for near real-time traffic control. This work presents an artificial neural network forecaster methodology applied to traffic flow condition prediction. The spatially distributed architecture uses life-long learning with a novel adaptive Artificial Neural Network based filter to detect and remove outliers from training data. The system has been designed to support traffic engineers in their decision making to react to traffic conditions before they get out of control. We performed experiments using feed-forward backpropagation, cascade-forward back-propagation, radial basis, and generalized regression Artificial Neural Networks for this purpose. Test results on actual data collected from the city of Leicester, UK, confirm our approach to deliver suitable forecasts.

Click here to view a full listing of Benjamin Passow's publications and outputs.

Research interests/expertise

Intelligent Traffic and Air Quality Management

Mobile Robotics

Evolutionary Computing

Control Engineering

Intelligent and Traditional Signal Processing

Embedded System Development

Theory and Application of Computational Intelligence 

Areas of teaching

CSCI2405: Introduction to Artificial Intelligence and Mobile Robotics
COMP5121: Mobile Robots
CSCI1412: Computer Technologies
Tutorial to Matlab (MSc induction)

Robotics Club (voluntary work) 

Qualifications

PhD 2011

MSc 2007

BSc (Hons) 2005

Dipl.-Ing. (BA) 2005 

Honours and awards

Machine Intelligence Award, British Computer Society, SGAI, Cambridge (Dec 2009)

Best Presentation Award, British Computer Society, Leicester Branch (Mar 2009)

Nominated finalist at the Vitae Midlands Hub Regional Poster Competition (Apr 2008)

Membership of professional associations and societies

Member of the Institute of Electrical & Electronics Engineers (IEEE) since 2007

Founding member of the IEEE student branch, De Montfort University, 2011

Externally funded research grants information

iTRAQ – Integrated Traffic Management and Air Quality Control Using Space Services is a European Space Agency (ESA) funded project to develop and test a dynamic traffic management system to optimise the use of the existing road network whilst meeting growing demands to sustain high standards of air quality in urban environments. The project ran from Feb. 2011 to Jan. 2012.

iTRAQ-X – This is an extension to the iTRAQ project, funded by the East Midlands Development Agency - Transport iNet ERDF, to support further work integrating GNSS data into the traffic model and to ensure continuation of the development work whilst a full scale development programme is launched. This project started in Jan. 2012 and will end in June 2012.

Internally funded research project information

Full PhD research scholarship to cover both fees and stipend for one student to work on "Intelligent Transport Systems: Integrated Traffic Management and Air Quality Control", funded under the DMU Research Scholarship scheme 2012.

Exploring aspects of helicopter flight synchronisation and acoustic sensing, Institute of Creative Technologies, PhD scholarship, 2007 – 2010, PhD student under supervision by Dr. Mario A. Gongora, Dr. Sophy Smith, and Prof. Adrian A. Hopgood

Travel grant to present the paper “Mitigating the Effect of Background Noise in Sound Based Helicopter Control” at the World Congress on Computational Intelligence, Barcelona, Spain, 2010, funded by the J F Laxton Scholarship.

ben-passow

Search Who's Who

 

 
News target area image
News

DMU is a dynamic university, read about what we have been up to in our latest news section.

Events target area image
Events

At DMU there is always something to do or see, check out our events for yourself.

Mission and vision target area image
Mission and vision

Read about our mission and vision and how these create a supportive and exciting learning environment.