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Dr Armaghan Moemeni

Job: Principal Lecturer

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Centre for Computational Intelligence (CCI)

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

T: +44 (0)116 257 7482




Publications and outputs 

  • Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data
    Intersection SPaT Estimation by means of Single-Source Connected Vehicle Data Rostami-Shahrbabaki, M; Bogenberger, K.; Safavi, A.A.; Moemeni, A. Current traffic management systems in urban networks require real-time estimation of the traffic states. With the development of in-vehicle and communication technologies, connected vehicle data has emerged as a new data source for traffic measurement and estimation. In this work, a machine learning-based methodology for signal phase and timing information (SPaT) which is highly valuable for many applications such as green light optimal advisory systems and real-time vehicle navigation is proposed. The proposed methodology utilizes data from connected vehicles travelling within urban signalized links to estimate the queue tail location, vehicle accumulation, and subsequently, link outflow. Based on the produced high-resolution outflow estimates and data from crossing connected vehicles, SPaT information is estimated via correlation analysis and a machine learning approach. The main contribution is that the single-source proposed approach relies merely on connected vehicle data and requires neither prior information such as intersection cycle time nor data from other sources such as conventional traffic measuring tools. A sample four-leg intersection where each link comprises different number of lanes and experiences different traffic condition is considered as a testbed. The validation of the developed approach has been undertaken by comparing the produced estimates with realistic micro-simulation results as ground truth, and the achieved simulation results are promising even at low penetration rates of connected vehicles. The file attached to this record is the author's final peer reviewed version.
  • Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities
    Revising ICT Programmes Through Learning Outcome Alignment: A Practical Exercise in Belarusian Universities Moemeni, A.; Gatward, Richard; Kankeviciene, Lina; Pyko, Alexander EU-Funded ERASMUS Capacity Building in Higher Education project called ‘Innovative ICT Education for Socio-Economic Development (IESED 2017–2019)’ has been established in the consortium of five Belarusian (BY) Higher Education Institutions (HEIs) as well as four HE partners across Europe. The goal of this project has been to enhance the competencies of ICT specialists and to improve the quality of ICT education across BY HEIs to meet the challenging needs of the social-economic development programme in the Republic of Belarus considering the Bologna process. In order to address this, the HEI partners worked towards updating selected study programmes in Information Resources Management, Mathematics and IT, Management with IT specialisation, Information Systems and Technologies, Informatics, compliance with the priorities of National Higher Education Strategy of Belarus. During the course of this project, some issues became apparent such as difficulties in developing generic course templates that could be adopted for years to come; limiting the reusability of course design, both level distinction and programme function of proposed courses which were not easy to identify when the focus was on competencies rather than mapping appropriate learning outcomes; and complications with evaluating credits especially with no clear fixed translation of course hours into credits. Finally as the Belarusian partners were required to follow the national ministry of education’s restrict guidelines, the recommended modifications by EU exert partners normally took longer to be approved and implemented. In this article, the authors (IESED project managers) reviewed this revision and implementation practice in order to achieve the IESED project goals in by resolving raised issues. They then moved on to discuss the formal methods that the project partners employed in order to revise and update the mentioned study programmes through learning outcome’s alignment.
  • Enhancing the Traffic Delay Model (TDM) in Signalised Intersections using GPS Data
    Enhancing the Traffic Delay Model (TDM) in Signalised Intersections using GPS Data Hafezi, Z.; Safavi, A.; Moemeni, A. Signalised Intersection Delay Model is one of the important components used in urban traffic for capacity analysis and simulations. The efficiency and quality of traffic operation at signal controlled intersections are assessed in terms of delay caused to an individual vehicle as a meaning of Level of Service (LOS). Nevertheless, with the advancement of technology and availability of various mobile data like GPS, one easy way to evaluate such delays is to use the most common model but adapt it with some typical one-line data. According to the research carried out at the Traffic Management and Traffic Control Centre of the Shiraz University to present a reasonable delay model, seven significant intersections were selected in Shiraz City to collect the suitable dataset. Twelve drivers were selected to collect data. The collected data were used to adopt the model more effectively. A low cost scenario and a least squares optimisation algorithm have been provided to optimise each intersection delay function and update the formula based on traffic conditions. The results showed that compared with different studies for achieving a suitable traffic delay formula especially for complex traffic conditions which exist in developing and under developing countries, the proposed scenario is one of the most effective and low cost methods for municipalities to achieve an improved TDM.
  • An Outcome based approach to developing a Belarusian Qualification Framework
    An Outcome based approach to developing a Belarusian Qualification Framework Gatward, R.; Moemeni, A.; Ayesh, Aladdin, 1972-; Lebegue, P.; Caillier, A.; Rudniewski, J.; Repca, M. The Higher Education landscape of Belarus is characterised by high quality institutions offering world class expertise and facilities, and a very high participation rate in higher education. However, it has also been recognised by the state that the degree of individuality and autonomy prevalent in these institutions works against the current mood of globalisation in Higher Education. An obvious example is international exchanges. It is particularly difficult in the case of students since the programmes are usually organised in an insular way and lack a precise specification of the level at which any contributory course is delivered. A stated objective of the Belarusian Ministry of Education is to seek membership of the European Higher Education Area (EHEA). To this end a road map (Eastern Partnership Civil Society Forum, 2017), designed to afford increased international compatibility of the Belarusian Higher Education Framework, has been defined and is being implemented by the Belarusian Ministry of Education. This paper considers how the EU funded project IESED could directly contribute to the realisation of this Road Map.
  • Low-Cost Automatic Ambient Assisted Living system
    Low-Cost Automatic Ambient Assisted Living system Malekmohamadi, Hossein; Moemeni, A.; Orun, A.; Kumar, J. The recent increase in ageing population in countries around the world has brought a lot of attention toward research and development of ambient assisted living (AAL) systems. These systems should be inexpensive to be installed in elderly homes, protecting their privacy and more importantly being non-invasive and smart. In this paper, we introduce an inexpensive system that utilises off-the-shelf sensor to grab RGB-D data. This data is then fed into different learning algorithms for classification different activity types. We achieve a very good success rate (99.9%) for human activity recognition (HAR) with the help of light-weighted and fast random forests (RF). The file attached to this record is the author's final peer reviewed version.
  • A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models
    A Quantisation of Cognitive Learning Process by Computer Graphics-Games: Towards More Efficient Learning Models Orun, A.; Seker, Huseyin; Rose, John; Moemeni, A.; Fidan, M. With the latest developments in computer technologies and artificial intelligence (AI) techniques, more opportunities of cognitive data acquisition and stimulation via game-based systems have become available for computer scientists and psychologists. This may lead to more efficient cognitive learning model developments to be used in different fields of cognitive psychology than in the past. The increasing popularity of computer games among a broad range of age groups leads scientists and experts to seek game domain solutions to cognitive based learning abnormalities, especially for younger age groups and children. One of the major advantages of computer graphics and using game-based techniques over the traditional face-to-face therapies is that individuals, especially children immerse in the game’s virtual environment and consequently feel more open to share their cognitive behavioural characteristics naturally. The aim of this work is to investigate the effects of graphical agents on cognitive behaviours to generate more efficient cognitive models. Research group of Computer Sciences at DMU, Psychology Research Group at University of Birmingham and Reseach Group of Computer Science at University of Northumbria.
  • Inertial-Visual Pose Tracking Using Optical Flowaided Particle Filtering
    Inertial-Visual Pose Tracking Using Optical Flowaided Particle Filtering Moemeni, A.; Tatham, E. This paper proposes an algorithm for visual-inertial camera pose tracking, using adaptive recursive particle filtering. The method benefits from the agility of inertial-based and robustness of vision-based tracking. A proposal distribution has been developed for the selection of the particles, which takes into account the characteristics of the Inertial Measurement Unit (IMU) and the motion kinematics of the moving camera. A set of state-space equations are formulated, particles are selected and then evaluated using the corresponding features tracked by optical flow. The system state is estimated using the weighted particles through an iterative sequential importance resampling algorithm. For the particle assessment, epipolar geometry, and the characteristics of focus of expansion (FoE) are considered. In the proposed system the computational cost is reduced by excluding the rotation matrix from the process of recursive state estimations. This system implements an intelligent decision making process, which decides on the best source of tracking whether IMU only, hybrid only or hybrid with past state correction. The results show a stable tracking performance with an average location error of a few centimeters in 3D space.
  • Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion
    Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion Moemeni, A. This thesis presents a framework for a hybrid model-free marker-less inertial-visual camera pose tracking with an integrated sensor fusion mechanism. The proposed solution addresses the fundamental problem of pose recovery in computer vision and robotics and provides an improved solution for wide-area pose tracking that can be used on mobile platforms and in real-time applications. In order to arrive at a suitable pose tracking algorithm, an in-depth investigation was conducted into current methods and sensors used for pose tracking. Preliminary experiments were then carried out on hybrid GPS-Visual as well as wireless micro-location tracking in order to evaluate their suitability for camera tracking in wide-area or GPS-denied environments. As a result of this investigation a combination of an inertial measurement unit and a camera was chosen as the primary sensory inputs for a hybrid camera tracking system. After following a thorough modelling and mathematical formulation process, a novel and improved hybrid tracking framework was designed, developed and evaluated. The resulting system incorporates an inertial system, a vision-based system and a recursive particle filtering-based stochastic data fusion and state estimation algorithm. The core of the algorithm is a state-space model for motion kinematics which, combined with the principles of multi-view camera geometry and the properties of optical flow and focus of expansion, form the main components of the proposed framework. The proposed solution incorporates a monitoring system, which decides on the best method of tracking at any given time based on the reliability of the fresh vision data provided by the vision-based system, and automatically switches between visual and inertial tracking as and when necessary. The system also includes a novel and effective self-adjusting mechanism, which detects when the newly captured sensory data can be reliably used to correct the past pose estimates. The corrected state is then propagated through to the current time in order to prevent sudden pose estimation errors manifesting as a permanent drift in the tracking output. Following the design stage, the complete system was fully developed and then evaluated using both synthetic and real data. The outcome shows an improved performance compared to existing techniques, such as PTAM and SLAM. The low computational cost of the algorithm enables its application on mobile devices, while the integrated self-monitoring, self-adjusting mechanisms allow for its potential use in wide-area tracking applications.
  • A framework for camera pose tracking using stochastic data fusion.
    A framework for camera pose tracking using stochastic data fusion. Moemeni, A.; Tatham, E.
  • Wavelet and multiwavelet watermarking.
    Wavelet and multiwavelet watermarking. Serdean, C. V.; Ibrahim, M. K.; Moemeni, A.; Al-Akaidi, Marwan, 1959-

Click here to view a full listing of Armaghan Moemeni's publications and outputs

Research interests/expertise

Computer Vision and Image Processing
Bayesian Inference Techniques 
Recursive Filtering Techniques for Sensor Fusion 
Applied Artificial Intelligence
Cognitive AI 
Motion Tracking Technology
Augmented/Mixed Reality
Mobile Augmented Reality
Human Computer Interaction (HCI)
Context-Aware systems
Activity Learning
Human Behaviour Understanding and Analysis

Areas of teaching

  • Programme Leader BSc(Hons) Computer Games Programming : Current
  • Faculty of Technology's Academic ERASMUS Coordinator : Current
  • Programme Leader of BSc(Hons) Games Technology  : Current 
  • Industrial Collaborative Manager (Link Programme Tutor for Games Technology course at Confetti Institute of Creative Technologies) : Current
  • Programme Leader of BSc (Hons) Multimedia Computing : 2005 - 2013

I have taught and supervised undergraduate/postgraduate projects in the following areas : 

Multimedia Computing 
Human Computer Interaction (HCI)
Digital Media Production 
Computer Programming - Java, C++, MATLAB
Computer Games Design and Development
Computer Networks
Internet Software Development
Intelligent Systems 
Electronics for Sound and Vision Systems
Media Electronics


PhD (Electrical and Computing Engineering) - De Montfort University
Thesis Title : Hybrid Marker-less Camera Pose Tracking with Integrated Sensor Fusion

MSc Multimedia, - Gloucestershire University
Thesis Title : In a "Diagnostic Help System” does metaphor approach enhance or impede the task retrieval

BEng (Hons) Electrical Engineering (1st Class) - Electronics, Shiraz University


Membership of external committees

Programme Committee Member Games Innovations Conference (ICE-GIC), International IEEE Consumer Electronics Society, 2009 and 2010.

Programme Committee Member / Paper Refereeing Committee,
International Conference on Imaging for Crime Detection and Prevention (ICDP) 2009 - on-going.

Membership of professional associations and societies

Member of IEEE - The Institute of Electrical and Electronics Engineers 
Member of the IET - The Institution of Engineering and Technology 
Member of the BMVA – British Machine Vision Association 

External Examiner at University of Bedfordshire,

External examiner at University of Bedfordshire, Department of Computer Sciences and Technology. September 2012 - Ongoing

Internally funded research project information

DMU Frontrunner Project - January 2014 : July 2014 

Project Title : Motion Sensing Dataset Creation - using a Multisensory Platform and Vicon MoCap
Role in the project : Project Manager

DMU Frontrunner Project - June 2013 : December 2013 

Title of the project : Mobile Augmented Reality App Development 
Role in the project : Project Manager

Armaghan Moemeni