ORCID Profile
0000-0002-6559-6736
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Pattern Recognition and Data Mining | Artificial Intelligence and Image Processing
Fixed Line Data Networks and Services | Information Processing Services (incl. Data Entry and Capture) | Mobile Data Networks and Services |
Publisher: Springer International Publishing
Date: 2018
Publisher: IEEE
Date: 11-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.311
Publisher: Springer Singapore
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Elsevier BV
Date: 04-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 08-2018
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: IEEE
Date: 20-11-2022
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 2014
Publisher: Wiley
Date: 19-06-2012
DOI: 10.1002/WCM.2248
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 11-2022
Publisher: Springer Science and Business Media LLC
Date: 10-12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-02-2022
DOI: 10.36227/TECHRXIV.19111730
Abstract: Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.
Publisher: Association for Computing Machinery (ACM)
Date: 12-2009
Abstract: Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.
Publisher: Elsevier BV
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Elsevier BV
Date: 2021
Publisher: Elsevier BV
Date: 10-2016
Publisher: IEEE
Date: 11-2011
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 02-2018
Publisher: IEEE
Date: 04-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-02-2022
DOI: 10.36227/TECHRXIV.19111730.V1
Abstract: Anomaly detection is a common and critical data mining task, it seeks to identify observations that differ significantly from others. Anomalies may indicate rare but significant events that require action. Market manipulation is an activity that undermines stock markets worldwide. This paper shares five large real-world, labelled data sets of anomalous stock market data where market manipulation is alleged to have occurred. Cutting edge deep learning techniques are then shown to successfully detect the anomalous periods. An LSTM based method with dynamic thresholding is particularly promising in this domain as it was able to identify contextual local anomalies in the data quickly, taking seconds to score two years of trading data for each stock, which can often be a challenge for deep learning approaches.
Publisher: Springer Singapore
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: Wiley
Date: 06-04-2020
DOI: 10.1002/CPE.5726
Publisher: IEEE
Date: 10-2013
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 07-2017
Publisher: ACM
Date: 29-01-2019
Publisher: IEEE
Date: 06-2018
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/ICDM.2011.80
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.637
Publisher: IEEE
Date: 07-2010
Publisher: Association for Computing Machinery (ACM)
Date: 24-08-2017
DOI: 10.1145/3085579
Abstract: With the advancement in the Internet of Things (IoT) technologies, variety of sensors including inexpensive, low-precision sensors with sufficient computing and communication capabilities are increasingly deployed for monitoring large geographical areas. One of the problems with the use of inexpensive sensors is that they often suffer from random or systematic errors such as drift. The sensor drift is the result of slow changes that occur in the measurement driven by aging, loss of calibration, and changes in the phenomena being monitored over a time period. These drifting sensors need to be calibrated automatically for continuous and reliable monitoring. Existing methods for drift detection and correction do not consider the measurement errors or uncertainties present in those inexpensive low-precision sensors, hence, resulting in unreliable drift estimates. In this article, we propose a novel framework to automatically detect and correct the drifts by employing Bayesian Maximum Entropy (BME) and Kalman filtering (KF) techniques. The BME method is a spatiotemporal estimation method that incorporates the measurement errors of low-precision sensors as interval quantities along with the high-precision sensor measurements in their computations. Our scheme can be implemented in a centralized as well as in a distributed manner to detect and correct the drift generated in the sensors. For the centralized scheme, we compare several Kriging-based estimation techniques in combination with KF, and show the superiority of our proposed BME-based method in detecting and correcting the drift. We also propose a multivariate BME framework for drift detection, in which multiple features can be used to improve the drift estimates. To demonstrate the applicability of our distributed approach on a real-world application scenario, we implemented our algorithm on each wireless sensor node in order to perform in-network drift detection. The evaluation on real IoT datasets gathered from an indoor and an outdoor deployments reveal the superiority of our method in correctly identifying and correcting the drifts that develop in the sensors, in real time, compared to the existing approaches in the literature.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2011
Publisher: Project MUSE
Date: 2020
Publisher: ACM
Date: 17-10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Society for Industrial and Applied Mathematics
Date: 30-06-2016
Publisher: IEEE
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2016
Publisher: IEEE
Date: 04-2014
Publisher: IEEE
Date: 05-2013
Publisher: IEEE
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Elsevier BV
Date: 08-2019
Publisher: Elsevier BV
Date: 2011
Publisher: IEEE
Date: 05-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: IEEE
Date: 10-2007
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 09-2011
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 12-2009
Publisher: Frontiers Media SA
Date: 02-03-2021
DOI: 10.3389/FPHYS.2021.612245
Abstract: The aim of this paper is to investigate the cardiorespiratory synchronization in athletes subjected to extreme physical stress combined with a cognitive stress tasks. ECG and respiration were measured in 14 athletes before and after the Ironman competition. Stroop test was applied between the measurements before and after the Ironman competition to induce cognitive stress. Synchrogram and empirical mode decomposition analysis were used for the first time to investigate the effects of physical stress, induced by the Ironman competition, on the phase synchronization of the cardiac and respiratory systems of Ironman athletes before and after the competition. A cognitive stress task (Stroop test) was performed both pre- and post-Ironman event in order to prevent the athletes from cognitively controlling their breathing rates. Our analysis showed that cardiorespiratory synchronization increased post-Ironman race compared to pre-Ironman. The results suggest that the amount of stress the athletes are recovering from post-competition is greater than the effects of the Stroop test. This indicates that the recovery phase after the competition is more important for restoring and maintaining homeostasis, which could be another reason for stronger synchronization.
Publisher: Association for Computing Machinery (ACM)
Date: 03-12-2016
DOI: 10.1145/2997656
Abstract: The growth in pervasive network infrastructure called the Internet of Things (IoT) enables a wide range of physical objects and environments to be monitored in fine spatial and temporal detail. The detailed, dynamic data that are collected in large quantities from sensor devices provide the basis for a variety of applications. Automatic interpretation of these evolving large data is required for timely detection of interesting events. This article develops and exemplifies two new relatives of the visual assessment of tendency (VAT) and improved visual assessment of tendency (iVAT) models, which uses cluster heat maps to visualize structure in static datasets. One new model is initialized with a static VAT/iVAT image, and then incrementally (hence inc-VAT/inc-iVAT) updates the current minimal spanning tree (MST) used by VAT with an efficient edge insertion scheme. Similarly, dec-VAT/dec-iVAT efficiently removes a node from the current VAT MST. A sequence of inc-iVAT/dec-iVAT images can be used for (visual) anomaly detection in evolving data streams and for sliding window based cluster assessment for time series data. The method is illustrated with four real datasets (three of them being smart city IoT data). The evaluation demonstrates the algorithms’ ability to successfully isolate anomalies and visualize changing cluster structure in the streaming data.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 03-2016
Publisher: Association for Computing Machinery (ACM)
Date: 23-04-2015
DOI: 10.1145/2736697
Abstract: Wireless sensor networks are often deployed in large numbers, over a large geographical region, in order to monitor the phenomena of interest. Sensors used in the sensor networks often suffer from random or systematic errors such as drift and bias. Even if they are calibrated at the time of deployment, they tend to drift as time progresses. Consequently, the progressive manual calibration of such a large-scale sensor network becomes impossible in practice. In this article, we address this challenge by proposing a collaborative framework to automatically detect and correct the drift in order to keep the data collected from these networks reliable. We propose a novel scheme that uses geospatial estimation-based interpolation techniques on measurements from neighboring sensors to collaboratively predict the value of phenomenon being observed. The predicted values are then used iteratively to correct the sensor drift by means of a Kalman filter. Our scheme can be implemented in a centralized as well as distributed manner to detect and correct the drift generated in the sensors. For centralized implementation of our scheme, we compare several kriging- and nonkriging-based geospatial estimation techniques in combination with the Kalman filter, and show the superiority of the kriging-based methods in detecting and correcting the drift. To demonstrate the applicability of our distributed approach on a real world application scenario, we implement our algorithm on a network consisting of Wireless Sensor Network (WSN) hardware. We further evaluate single as well as multiple drifting sensor scenarios to show the effectiveness of our algorithm for detecting and correcting drift. Further, we address the issue of high power usage for data transmission among neighboring nodes leading to low network lifetime for the distributed approach by proposing two power saving schemes. Moreover, we compare our algorithm with a blind calibration scheme in the literature and demonstrate its superiority in detecting both linear and nonlinear drifts.
Publisher: IEEE
Date: 06-2014
Publisher: IEEE
Date: 11-2014
Publisher: IEEE
Date: 20-11-2022
Publisher: IEEE
Date: 12-2018
Publisher: IEEE
Date: 04-2014
Publisher: Elsevier BV
Date: 09-2014
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 09-2022
Publisher: IEEE
Date: 04-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Institution of Engineering and Technology (IET)
Date: 06-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: Springer International Publishing
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2008
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Publisher: IEEE
Date: 12-2010
No related organisations have been discovered for Sutharshan Rajasegarar.
Start Date: 08-2020
End Date: 04-2024
Amount: $477,000.00
Funder: Australian Research Council
View Funded Activity