Dr Lina Stankovic

Reader

Electronic and Electrical Engineering

Personal statement

My research expertise is in Signal and Information Processing, with focus on representation, processing, analysis, communications and data management of information in a range of signals including electrical signals, video, seismic/geoscience, health/biological and other environmental sensor data. Besides the Energy dimension of the work, the research insights gained are also applicable to appliance manufacturing and food systems, especially in relation to environmental impact assessment and net zero GHG emissions. Overall my research interests revolve around sustainability. My work is particularly aligned with the following university strategic research themes (Measurement Science & Enabling Technologies, Energy, Health & Well-Being)

I am also 1st year advisor of studies for the Computer and Electronic Systems degree at Strathclyde.

Expertise

Has expertise in:

Signal information processing with application to the following problems:

Non-intrusive load monitoring, load profiling and disaggregation, activity recognition in smart buildings (Energy and environment)
Motion capture and person-centric kinematrics analysis using portable depth and infrared camera systems (Health & Wellbeing)
Micro-seismic event analysis, inc. earthquakes (Geosciences)
Wireless sensor network solutions for civil infrastructure, such as predicting failure in earthworks (embankments and cuttings) and bridge/scour monitoring
Water consumption, treatment and infrastructure monitoring

Prizes and awards

Finalist of the World first 10K Best paper Award (Top 3%): Recipient; 12/7/2017
Paper awarded top 5% by reviewers in Eusipco-2010 conference and invited for journal publication.: Recipient; 8/2010

more prizes and awards

Qualifications

Senior Member of the IEEE

Publications

Deep-based conditional probability density function forecasting of residential loads: Afrasiabi Mousa, Mohammadi Mohammad, Rastegar Mohammad, Stankovic Lina, Afrasiabi Shahabodin, Khazaei Mohammad; IEEE Transactions on Smart Grid Vol 11, pp. 3746-3757 (2020); https://doi.org/10.1109/TSG.2020.2972513
Graph-based micro-seismic signal classification with an optimised feature space: Li Jiangfeng, Yang Cheng, Stankovic Vladimir, Stankovic Lina, Pytharouli Stella; 2020 IEEE International Geoscience and Remote Sensing Symposium (2020)
Non-intrusive load disaggregation solutions for very low-rate smart meter data: Zhao Bochao, Ye Minxiang, Stankovic Lina, Stankovic Vladimir; Applied Energy Vol 268 (2020); https://doi.org/10.1016/j.apenergy.2020.114949
The risks and benefits of AI smart meters: Stankovic Lina, Stankovic Vladimir; (2020)
High-accuracy real-time microseismic analysis platform : case study based on the super-sauze mud-based landslide: Li J, Stankovic L, Pytharouli S, Stankovic V; Geoconvention (2020)
Smart on-chip Fourier-transform spectrometers harnessing machine learning algorithms: Herrero-Bermello Alaine, Li Jiangfeng, Khazaei Mohammad, Grinberg Yuri, Villafranca-Velasco Aitor, Vachon Martin, Cheben Pavel, Stankovic Lina, Stankovic Vladimir, Xu Dan-Xia, Schmid Jens H, Alonso-Ramos Carlos A; SPIE Photonics West OPTO (2020)

more publications

Teaching

Current classes:

1st year Engineering Design for Software Development inc. Python programming

4th/5th year Information Transmission and Security, inc. Digital communications principles, channel coding

Research interests

Smart meter energy analytics, including load disaggregation, prediction and profiling
Non-intrusive Load Monitoring, residential and commercial
Environmental impact assessment of consumption cycle of Food Systems and Appliances
Detecting, classifying, understanding and characterising subsurface processes arising from man-made/induced activities, such as hydraulic fracturing
Model-fitting/system characterisation
Sensor deployment and data acquisition

Professional activities

Appliance Usage Detection from Smart Meter Data Using Supervised and Unsupervised Non-Intrusive Load Monitoring: Contributor; 26/5/2020
External examiner for Megha Gaur, PhD Candidate, IIIT New Delhi, India: Examiner; 16/10/2019
PGCon: Edinburgh Postgraduate Conference: Participant; 15/10/2019
Ethical AI Network (External organisation): Member; 6/2019
European Science Foundation (External organisation): Member; 19/12/2018
Transferable low rate NILM using deep neural network architectures: Contributor; 2/10/2018

more professional activities

Projects

Smart pumping for Subsurface Engineering (Prosperity Partnership): Shipton, Zoe (Principal Investigator) Corney, Jonathan (Co-investigator) Dempster, William (Co-investigator) Perry, Marcus (Co-investigator) Pytharouli, Stella (Co-investigator) Stankovic, Lina (Co-investigator) Stankovic, Vladimir (Co-investigator) Yang, Shangtong (Co-investigator) Fan, Ding (Researcher) Parastatidis, Emmanouil (Researcher) Rizzuto, Francesco (Researcher) Xi, Xun (Researcher); 01-Jan-2018 - 31-Jan-2023
Eco-Bot: Personalised ICT-tools for the Active Engagement of Consumers Towards Sustainable Energy: Stankovic, Lina (Principal Investigator) Stankovic, Vladimir (Co-investigator) Murray, David (Researcher); 01-Jan-2017 - 31-Jan-2020
EPSRC Global Challenges Research Fund Institutional Sponsorship Award 2017 (GCRF) / R171051-102: Stankovic, Vladimir (Principal Investigator) Stankovic, Lina (Co-investigator); 01-Jan-2017 - 31-Jan-2018
SENSors and Intelligence in BuiLt Environment (SENSIBLE) MSCA RISE: Stankovic, Lina (Principal Investigator) Glesk, Ivan (Co-investigator) Gleskova, Helena (Co-investigator) Stankovic, Vladimir (Co-investigator); 01-Jan-2017 - 31-Jan-2020
Smart Building Energy Management System (BEMS): Stankovic, Lina (Principal Investigator); Market study and prototyping of data analytical tools that could be beneficial to utilities for improving their customer experience; 01-Jan-2013 - 30-Jan-2015
Monitoring ozone treatment technology: Stankovic, Lina (Principal Investigator) Andonovic, Ivan (Co-investigator) Stankovic, Vladimir (Co-investigator); 13-Jan-2013 - 31-Jan-2014

more projects

Address

Electronic and Electrical Engineering
Royal College Building

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