Vladimir Stankovic

Vladimir Stankovic
University of Strathclyde · Department of Electronic and Electrical Engineering (EEE)

PhD

About

256
Publications
28,042
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4,609
Citations
Citations since 2017
71 Research Items
2275 Citations
20172018201920202021202220230100200300400
20172018201920202021202220230100200300400
20172018201920202021202220230100200300400
20172018201920202021202220230100200300400
Introduction
Explainable AI, interpretable machine learning models, causal inference, graph signal processing
Additional affiliations
October 2007 - present
University of Strathclyde
Position
  • Professor (Full)
February 2006 - September 2007
Lancaster University
Position
  • Lecturer
July 2003 - February 2006
Texas A&M University
Position
  • Research Assistant

Publications

Publications (256)
Article
Full-text available
Slope instabilities caused by heavy rainfall, man-made activity or earthquakes can be characterised by seismic events. To minimise mortality and infrastructure damage, a good understanding of seismic signal properties characterising slope failures is therefore crucial to classify seismic events recorded from continuous recordings effectively. Howev...
Article
Buildings represent 39% of global greenhouse gas emissions, thus reducing carbon emissions in buildings is of importance to greenhouse gas emissions reductions. This requires understanding how electricity is utilized in the buildings, then optimizing electricity management to seek conservation of energy. Non-intrusive load monitoring (NILM) is a te...
Article
Full-text available
This paper proposes energy-efficient solutions for the smart light-emitting diode (LED) lighting system, which provides minimal energy consumption while simultaneously satisfying illuminance requirements of the users in a typical office space. In addition to artificial light from dimmable LED lamps, natural daylight coming from external sources, su...
Article
Full-text available
Electrification of transportation is gaining traction as a viable alternative to vehicles that use fossil-fuelled internal combustion engines, which are responsible for a major part of carbon dioxide emissions. This global turn towards electrification of transportation is leading to an exponential energy and power demand, especially during late-aft...
Article
In graph signal processing, the shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random network graph is shift-enabled. In particular, popular network graph models Erdős–Rényi (ER), Watts–Strogatz (WS), Barabási–Albert (BA) for both weighted and unweighted are considered. Mor...
Article
Full-text available
Abstract: This pilot study aimed to investigate the implementation of supervised classifiers and a neural network for the recognition of activities carried out by Individuals with Lower Limb Amputation (ILLAs), as well as individuals without gait impairment, in free living conditions. Eight individuals with no gait impairments and four ILLAs wore a...
Preprint
Full-text available
The shift-enabled property of an underlying graph is essential in designing distributed filters. This article discusses when a random graph is shift-enabled. In particular, popular graph models ER, WS, BA random graph are used, weighted and unweighted, as well as signed graphs. Our results show that the considered unweighted connected random graphs...
Chapter
The image classification problem is to categorize elements of an image dataset into two or more pre‐defined classes based on inherent, detectable image features. Classification is typically posed in the context of supervised or semi‐supervised learning (SSL). This chapter discusses how SSL image classification can be mathematically formulated as op...
Article
Full-text available
With the growing application of undirected graphs for signal/image processing on graphs and distributed machine learning, we demonstrate that the shift-enabled condition is as necessary for undirected graphs as it is for directed graphs. It has recently been shown that, contrary to the widespread belief that a shift-enabled condition (necessary for...
Conference Paper
Full-text available
Accurate and fast localisation of microseismic events is a requirement for a number of applications, e.g. mining, enhanced geothermal systems. New methods for event localisation have been proposed over the last decades. The waveform-based methods are of the most recent developed ones and their main advantage is the ability to locate weak seismic ev...
Article
Full-text available
Microseismic monitoring has been increasingly used in the past two decades to illuminate (sub)surface processes such as landslides, due to its ability to record small seismic waves generated by soil movement and/or brittle behaviour of rock. Understanding the evolution of landslide processes is of paramount importance in predicting or even avoiding...
Article
Full-text available
Socioeconomic reasons post-COVID-19 demand unsupervised home-based rehabilitation and, specifically, artificial ambient intelligence with individualisation to support engagement and motivation. Artificial intelligence must also comply with accountability, responsibility, and transparency (ART) requirements for wider acceptability. This paper presen...
Article
Convolutional neural network (CNN)-based feature learning has become the state-of-the-art for many applications since, given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning is more challenging if training labels are noisy as CNN tends to overfit to the noisy...
Article
Full-text available
Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-leng...
Article
It was demonstrated that, as a nonlinear implementation of Slepian-Wolf Coding, Distributed Arithmetic Coding (DAC) outperforms traditional Low-Density Parity-Check (LPDC) codes for short code length and biased sources. This fact triggers research efforts into theoretical analysis of DAC. In our previous work, we proposed two analytical tools, Code...
Technical Report
Full-text available
A comparison method (Increased Current Factor per displacement) is developed to help further researches and design consideration for Organic Electric Motors and Hybrid Organic-Metallic Electric Motors. One specific polymer referred to as "PhanPol2", has been tested in different configurations for the Switched Reluctance Motor. This magnetic polymer...
Preprint
Full-text available
It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-sym...
Article
With the active large-scale roll-out of smart metering worldwide, details about the type of smart meter data that will be available for analysis are emerging. Consequently, focus has steadily been shifting from analysis of high-rate power readings (usually in kHz to MHz) to low-rate power readings (sampled at 1–60 s) and very low-rate meter reading...
Article
Full-text available
Federated learning is a decentralized topology of deep learning, that trains a shared model through data distributed among each client (like mobile phones, wearable devices), in order to ensure data privacy by avoiding raw data exposed in data center (server). After each client computes a new model parameter by stochastic gradient descent (SGD) bas...
Preprint
Federated learning is a decentralized topology of deep learning, that trains a shared model through data distributed among each client (like mobile phones, wearable devices), in order to ensure data privacy by avoiding raw data exposed in data center (server). After each client computes a new model parameter by stochastic gradient decrease (SGD) ba...
Article
Full-text available
This article presents an indoor assistive system that addresses the challenges faced by visually impaired individuals. The proposed system helps the visually impaired individuals to move indoor and make them independent of any external assistance. The proposed system consists of a camera with a processing unit and an accompanying Time-of-Flight sen...
Article
Full-text available
With increased demand for tele-rehabilitation, many autonomous home-based rehabilitation systems have appeared recently. Many of these systems, however, suffer from lack of patient acceptance and engagement or fail to provide satisfactory accuracy; both are needed for appropriate diagnostics. This paper first provides a detailed discussion of curre...
Preprint
Full-text available
Convolutional neural network (CNN)-based feature learning has become state of the art, since given sufficient training data, CNN can significantly outperform traditional methods for various classification tasks. However, feature learning becomes more difficult if some training labels are noisy. With traditional regularization techniques, CNN often...
Article
Full-text available
Miniaturized silicon photonics spectrometers capable of detecting specific absorption features have great potential for mass market applications in medicine, environmental monitoring, and hazard detection. However, state-of-the-art silicon spectrometers are limited by fabrication imperfections and environmental conditions, especially temperature va...
Preprint
Full-text available
It has recently been shown that, contrary to the wide belief that a shift-enabled condition (necessary for any shift-invariant filter to be representable by a graph shift matrix) can be ignored because any non-shift-enabled matrix can be converted to a shift-enabled matrix, such a conversion in general may not hold for a directed graph with non-sym...
Article
Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, imag...
Article
Background Stroke is increasingly one of the main causes of impairment and disability. Contextual and empirical evidence demonstrate that, mainly due to service delivery constraints, but also due to a move toward personalized health care in the comfort of patients’ homes, more stroke survivors undergo rehabilitation at home with minimal or no super...
Preprint
Lighting systems based on light-emitting diodes (LEDs) possess many benefits over their incandescent counterparts including longer lifespans, lower energy costs, better quality of light and no toxic elements, all without sacrificing consumer satisfaction. Their lifespan is not affected by switching frequency allowing for better illumination control...
Conference Paper
The task of a semi-supervised binary classifier is to predict missing binary labels in a dataset given partial label observations and features of the data. From a graph signal processing (GSP) perspective, such task can be posed as a signal interpolation problem, where the labels on data are interpreted as a binary signal on a graph connecting node...
Article
Full-text available
While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibrat...
Article
The large-scale deployment of smart metering worldwide has ignited renewed interest in electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM algorithms disaggregate one appliance at a time, remove the estimated appliance contribution from the total load, and then move on to disaggregate the next appliance. On one hand,...
Article
Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings. The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the...
Article
While optical motion analysis systems can provide high-fidelity gait parameters, they are usually impractical for local clinics and home use, due to high cost, requirement for large space, and lack of portability. In this study, we focus on a cost-effective and portable, single-camera gait analysis solution, based on video acquisition with calibrat...
Conference Paper
Semi-supervised binary classifier learning is a fundamental machine learning task where only partial binary labels are observed, and labels of the remaining data need to be interpolated. Leveraging on the advances of graph signal processing (GSP), recently binary classifier learning is posed as a signal restoration problem regularized using a graph...
Preprint
Full-text available
In a 2013 paper by Sandryhaila and Moura, the authors introduced a condition (herein we will call it shift-enabled condition) that any shift invariant filter can be represented by the shift matrix if the condition is satisfied. In the same, the authors also argued that shift-enabled condition can be ignored as any non-shift-enabled matrix can be co...
Article
Full-text available
Large-scale smart energy metering deployment worldwide and integration of smart meters within the smart grid will enable two-way communication between the consumer and energy network, thus ensuring improved response to demand. Energy disaggregation or Non-intrusive load monitoring (NILM), namely disaggregation of the total metered electricity consu...
Article
We study detection of random signals corrupted by noise that over time switch their values (states) between a finite set of possible values, where the switchings occur at unknown points in time. We model such signals as hidden semi-Markov signals (HSMS), which generalize classical Markov chains by introducing explicit (possibly non-geometric) distr...
Conference Paper
Most current non-intrusive load monitoring (NILM) algorithms disaggregate one appliance at a time, remove the appliance contribution towards the total load, and then move on to the next appliance. On one hand, this is effective since it avoids multi-class classification, and analytical models for each appliance can be developed independently of oth...
Article
The food industry is one of the world's largest contributors to carbon emissions, due to energy consumption throughout the food life cycle. This paper is focused on the residential consumption phase of the food life cycle assessment (LCA), i.e., energy consumption during home cooking. Specifically, while much effort has been placed on improving app...
Article
Full-text available
We study detection of random signals corrupted by noise that over time switch their values (states) from a finite set of possible values, where the switchings occur at unknown points in time. We model such signals by means of a random duration model that to each possible state assigns a probability mass function which controls the statistics of dur...
Article
Full-text available
In digital signal processing, shift-invariant filters can be represented as a polynomial expansion of a shift operation,that is, the Z-transform representation. When extended to graph signal processing (GSP), this would mean that a shift-invariant graph filter can be represented as a polynomial of the adjacency (shift) matrix of the graph. However,...
Article
Full-text available
Machine-type communications and large-scale information processing architectures are among key (r)evolutionary enhancements of emerging fifth-generation (5G) mobile cellular networks. Massive data acquisition and processing will make 5G network an ideal platform for large-scale system monitoring and control with applications in future smart transpo...
Chapter
The instrumental view of smart homes and their users is premised on active management of energy demand contributing to energy system objectives. In this chapter we explore a novel way of using data from smart home technologies (SHTs) to link energy consumption in homes to daily activities. We use activities as a descriptive term for the common ways...
Article
Low-cost depth sensors, such as Microsoft Kinect, have potential for non-contact health monitoring that is robust to ambient lighting conditions. However, captured depth images typically suer from high acquisition noise, and hence processing them to estimate biometrics is difficult. In this paper, we propose to capture depth video of a human subjec...
Article
Full-text available
Smart meter roll-outs provide easy access to granular meter measurements, enabling advanced energy services, ranging from demand response measures, tailored energy feedback and smart home/building automation. To design such services, train and validate models, access to data that resembles what is expected of smart meters, collected in a real-world...
Article
Full-text available
Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, sub-metering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption o...
Article
Obstructive sleep apnea, characterized by repetitive obstruction in the upper airway during sleep, is a common sleep disorder that could significantly compromise sleep quality and quality of life in general. The obstructive respiratory events can be detected by attended in-laboratory or unattended ambulatory sleep studies. Such studies require many...
Article
Distributed arithmetic coding (DAC) is a variant of AC that can realize Slepian-Wolf coding in a nonlinear way. In our previous work, we defined codebook cardinality spectrum (CCS) and Hamming distance spectrum (HDS) for DAC. In this paper, we make use of CCS and HDS to analyze tailed DAC, which is a form of DAC that, as traditional AC, maps the la...
Conference Paper
A number of advantages of unicondylar arthroplasty (UKA) over total knee arthroplasty in patients presenting osteoarthritis in only a single compartment have been identified in the literature. However, accurate implant positioning and alignment targets, which have been shown to significantly affect outcomes, are routinely missed by conventional tec...
Article
With the large-scale roll-out of smart metering worldwide, there is a growing need to account for the individual contribution of appliances to the load demand. In this paper, we design a Graph signal processing (GSP)-based approach for non-intrusive appliance load monitoring (NILM), i.e., disaggregation of total energy consumption down to individua...
Article
With increasing importance given to telerehabilitation, there is a growing need for accurate, low-cost, and portable motion capture systems that do not require specialist assessment venues. This paper proposes a novel framework for motion capture using only a single depth camera, which is portable and cost effective compared to most industry-standa...
Article
Full-text available
The availability of smart metering and smart appliances enables detecting and characterising appliance use in a household, quantifying energy savings through efficient appliance use and predicting appliance-specific demand from load measurements is possible. With growing electric kettle ownership and usage, lack of any efficiency labelling guidelin...
Article
Distributed arithmetic coding (DAC) is an effective technique for implementing Slepian-Wolf coding (SWC). It has been shown that a DAC code partitions source space into unequal-size codebooks, so that the overall performance of DAC codes depends on the cardinality and structure of these codebooks. The problem of DAC codebook cardinality has been so...
Article
Full-text available
Stroke is a worldwide healthcare problem, which often causes long-term motor impairment, handicap, and disability. Optical motion analysis systems are commonly used for impairment assessment due to high accuracy. However, the requirement of equipment-heavy and large laboratory space together with operational expertise makes these systems impractica...