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HomeAcademic staffDr Maitreyee Dey
Dr Maitreyee Dey

Dr Maitreyee Dey

deym5@lsbu.ac.uk

Mechanical Engineering and Design

https://orcid.org/0000-0002-6862-7032

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I am a Post-doctoral research fellow in the Department of Electrical and Electronics Engineering, London South Bank University and hold the post of Data Mining Lead of Neuville Grid Data Management Limited. I am working on Solar farm data analysis using machine learning and data mining methods for the Access to Innovation (A2i) and Low Carbon London (LCLDN) project funded by the EU commission through its regional development Fund in collaboration with London South Bank University and Neuville Grid Data Management Limited.

My expertise falls in the pivotal area of machine learning/artificial intelligence and low carbon technologies, crucial areas where the UK can lead in the sustainability of the world energy and automation systems. My other research interest includes pattern recognition, big data analysis, deep learning, image processing, energy optimization in smart buildings, renewable energy technology, and Computer-aided Diagnosis using Machine Learning/AI for smart health.

I have more than 30 publications including high-impact journals, conferences, book chapters, and patent. Also, in 2020 I won Best Paper Award at the CUE conference, Elsevier.

I am endorsed by the Royal Academy of Engineering. I am an active member of IEEE, CIGRE NGN member, Energy Institute and many more. Additionally, I am serving as an Editorial board member in MDPI journals, and an Adhoc reviewer in many high-impact journals including, IEEE transactions, Nature Scientific Data, and many more

MTech

West Bengal University of Technology

2010
2012
PhD

London South Bank University

2016
2020
CIGRE
2022
IEEE
2016
Energy Institute
2021

Detecting Power Grid Frequency Events from μPMU Voltage Phasor Data Using Machine Learning
Dey, M., Rana, S., Wylie, J., Simmons, C. V. and Dudley-Mcevoy, S. (2022). Detecting Power Grid Frequency Events from μPMU Voltage Phasor Data Using Machine Learning. The IET 11th International Conference on Renewable Power Generation. IET London: Savoy Place. 22 - 23 Sep 2022 Institute of Engineering and Technology (IET).

Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2022). Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. PLoS ONE. https://doi.org/10.1371/journal.pone.0271377

Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing
Rana, S., Dey, M., Ghavami, M. and Dudley-Mcevoy, S. (2022). Markerless Gait Classification Employing 3D IR-UWB Physiological Motion Sensing. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2022.3154092

Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2021). Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. London South Bank University. https://doi.org/10.18744/lsbu.8xz49

Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data
Rana, S., Dey, M., Riccardo Loretoni, Michele Duranti, Lorenzo Sani, Alessandro Vispa, Ghavami, M., Sandra Dudley and Gianluigi Tiberi (2021). Radial Basis Function for Breast Lesion Detection from MammoWave Clinical Data. Diagnostics. 11 (10). https://doi.org/10.3390/diagnostics11101930

Solar farm voltage anomaly detection using high-resolution μ PMU data-driven unsupervised machine learning
Dey, M., Rana, S., Simmons, Clarke V. and Dudley-Mcevoy, S. (2021). Solar farm voltage anomaly detection using high-resolution μ PMU data-driven unsupervised machine learning. Applied Energy. 303, p. 117656. https://doi.org/10.1016/j.apenergy.2021.117656

Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach
Dey, M., Rana, S. and Dudley-Mcevoy, S. (2021). Automated terminal unit performance analysis employing x-RBF neural network and associated energy optimisation – A case study based approach. Applied Energy. 298, p. 117103. https://doi.org/10.1016/j.apenergy.2021.117103

3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon
Rana, S., Dey, M., Ghavami, M. and Dudley-McEvoy, S. (2021). 3D Gait Abnormality Detection Employing Contactless IR-UWB Sensing Phenomenon. IEEE Transactions on Instrumentation and Measurement. 70. https://doi.org/10.1109/TIM.2021.3069044

A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building
Dey, M, Rana, SP and Dudley, S (2020). A Case Study Based Approach for Remote Fault Detection Using Multi-Level Machine Learning in A Smart Building. Smart Cities. 3 (2), pp. 401-419. https://doi.org/10.3390/smartcities3020021

ITERATOR: A 3D Gait Identification from IR-UWB Technology
Rana, S., Dey, M, Ghavami, M and Dudley, S (2019). ITERATOR: A 3D Gait Identification from IR-UWB Technology. International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (EMBC 2019). Berlin, Germany 23 - 27 Jul 2019

Non-Contact Human Gait Identification through IR-UWB Edge Based Monitoring Sensor
Rana, S., Dey, M, Ghavami, M and Dudley-McEvoy, S (2019). Non-Contact Human Gait Identification through IR-UWB Edge Based Monitoring Sensor. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2019.2926238

A robust FLIR target detection employing an auto-convergent pulse coupled neural network
Dey, M., Rana, S.P. and Siarry, P. (2019). A robust FLIR target detection employing an auto-convergent pulse coupled neural network. Remote Sensing Letters. 10 (7), pp. 639-648. https://doi.org/10.1080/2150704x.2019.1597296

Signature Inspired Home Environments Monitoring System Using IR-UWB Technology
Rana, S., Dey, M., Ghavami, M. and Dudley-Mcevoy, S. (2019). Signature Inspired Home Environments Monitoring System Using IR-UWB Technology. Sensors. 19 (2), p. 385. https://doi.org/10.3390/s19020385

Semi-supervised learning techniques for automated fault detection and diagnosis of HVAC systems
Dey, M., Rana, S. and Dudley, S. (2018). Semi-supervised learning techniques for automated fault detection and diagnosis of HVAC systems. IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2018). Volos, Greece 05 - 07 Nov 2018 IEEE. https://doi.org/10.1109/ictai.2018.00136

Boosting content based image retrieval performance through integration of parametric & nonparametric approaches
Rana, S., Dey, M. and Siarry, P. (2019). Boosting content based image retrieval performance through integration of parametric & nonparametric approaches. Journal of Visual Communication and Image Representation. 58, pp. 25-219. https://doi.org/10.1016/j.jvcir.2018.11.015

Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC System
Dudley, S, Dey, M and Rana, S. (2018). Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC System. IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2018). Volos, Greece 05 - 07 Nov 2018

A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building
Rana, S., Dey, M and Dudley, S (2018). A Self Regulating and Crowdsourced Indoor Positioning System through Wi-Fi Fingerprinting for Multi Storey Building. Sensors. 18 (11), pp. 1-15. https://doi.org/10.3390/s18113766

Remote Vital Sign Recognition Through Machine Learning Augmented UWB
Dudley, S, Rana, S., Dey, M, Brown, R and Siddiqui, H (2018). Remote Vital Sign Recognition Through Machine Learning Augmented UWB. European Conference on Antennas and Propagation. Excel London, Docklands 09 - 13 Apr 2018 London South Bank University. https://doi.org/10.1049/cp.2018.0978

A PID inspired feature extraction method for HVAC terminal units
Dey, M., Gupta, M., Rana, S., Turkey, M. and Dudley-Mcevoy, S. (2017). A PID inspired feature extraction method for HVAC terminal units. IEEE Conference on Technologies for Sustainability (SusTech 2017). Phoenix, Arizona, USA 12 - 14 Nov 2017 IEEE. https://doi.org/10.1109/sustech.2017.8333470

Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis
Dudley, S, Dey, M and Rana, S. (2018). Smart Building Creation in Large Scale HVAC Environments through Automated Fault Detection and Diagnosis. Future Generation Computer Systems. 108, pp. 950-966. https://doi.org/10.1016/j.future.2018.02.019

A PID Inspired Feature Extraction for HVAC Terminal Units
Dey, M, Gupta, M, Rana, S., Turkey, M and Dudley, S (2017). A PID Inspired Feature Extraction for HVAC Terminal Units. IEEE Conference on Technologies for Sustainability (SusTech 2017). Phoenix, Arizona, USA 12 - 14 Nov 2017 Institute of Electrical and Electronics Engineers (IEEE).

UWB Localization Employing Supervised Learning Method
Rana, S., Dey, M., Siddiqui, H., Tiberi, G., Ghavami, M. and Dudley, S (2017). UWB Localization Employing Supervised Learning Method. IEEE International Conference on Ubiquitous Wireless Broadband 2017. Salamanca, Spain 12 - 15 Sep 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICUWB.2017.8250971

Unsupervised Learning Techniques for HVAC Terminal Unit Behaviour Analysis
Dey, M, Gupta, M, Turkey, M and Dudley, S (2017). Unsupervised Learning Techniques for HVAC Terminal Unit Behaviour Analysis. IEEE International Conference on Smart City Innovations. Fremont, California, USA 04 - 08 Aug 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/UIC-ATC.2017.8397584