ORCID Profile
0000-0001-5512-114X
Current Organisation
RMIT University
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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.
Data Encryption | Pattern Recognition and Data Mining | Computer Software | Computer System Security | Data Format |
Electronic Information Storage and Retrieval Services | Expanding Knowledge in the Information and Computing Sciences | National Security | Information Processing Services (incl. Data Entry and Capture)
Publisher: Elsevier BV
Date: 04-2021
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 2013
DOI: 10.1016/J.CMPB.2012.08.015
Abstract: Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower s ling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low s ling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different s ling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower s ling frequency. Moreover, increasing the s le size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data.
Publisher: Springer Science and Business Media LLC
Date: 24-09-2009
DOI: 10.1007/S10916-008-9208-Y
Abstract: With the advent of high-speed internet band-width consuming video conferencing applications will rapidly become attractive to e-patients seeking real-time video consultations from e-doctors. In a conventional system patients connect to a known server in a medical center of his choice. If the server (i.e. a server via which a medical consultant communicates with a patient) is busy, the patient must wait before the server becomes free. Such a system is not efficient as many patients in medical centers with busy servers may either have to wait long, or are simply turned away. Patients may also leave when they become impatient. Not only the patients suffer due to server unavailability, medical service providers also incur revenue losses due to lost patients. To counter these problems, we propose a distributed cooperative Video Consultation on Demand (VCoD) system where servers are located in many different medical centers in different neighbourhoods close to patient concentrations. In such a cooperative system if patients find their nearby servers under heavy load they are automatically directed to servers that are least loaded by using efficient server selection method (also called anycasting). Simple numerical analysis shows that this not only maximizes revenues for medical service providers by reducing number of lost patients, but also improves average response time for e-patients.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Wiley
Date: 18-07-2016
DOI: 10.1002/WCM.2488
Publisher: IEEE
Date: 05-2016
Publisher: Elsevier BV
Date: 05-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Elsevier BV
Date: 02-2017
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Elsevier BV
Date: 12-2015
Publisher: No publisher found
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: Elsevier BV
Date: 2014
DOI: 10.1016/J.CMPB.2013.06.019
Abstract: Hospital waiting times are considerably long, with no signs of reducing any-time soon. A number of factors including population growth, the ageing population and a lack of new infrastructure are expected to further exacerbate waiting times in the near future. In this work, we show how healthcare services can be modelled as queueing nodes, together with healthcare service workflows, such that these workflows can be optimised during execution in order to reduce patient waiting times. Services such as X-ray, computer tomography, and magnetic resonance imaging often form queues, thus, by taking into account the waiting times of each service, the workflow can be re-orchestrated and optimised. Experimental results indicate average waiting time reductions are achievable by optimising workflows using dynamic re-orchestration.
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Elsevier BV
Date: 2019
Publisher: ACM
Date: 02-07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer International Publishing
Date: 2022
Publisher: IEEE
Date: 08-2011
DOI: 10.1109/NCA.2011.65
Publisher: Elsevier BV
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: No publisher found
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2011
Publisher: IEEE
Date: 2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2017
Publisher: Elsevier BV
Date: 03-2014
Publisher: Elsevier BV
Date: 02-2019
Publisher: Elsevier BV
Date: 09-2015
Publisher: Association for Computing Machinery (ACM)
Date: 06-12-2021
DOI: 10.1145/3453171
Abstract: Edge computing is an emerging technology for the acquisition of Internet-of-Things (IoT) data and provisioning different services in connected living. Artificial Intelligence (AI) powered edge devices (edge-AI) facilitate intelligent IoT data acquisition and services through data analytics. However, data in edge networks are prone to several security threats such as external and internal attacks and transmission errors. Attackers can inject false data during data acquisition or modify stored data in the edge data storage to h er data analytics. Therefore, an edge-AI device must verify the authenticity of IoT data before using them in data analytics. This article presents an IoT data authenticity model in edge-AI for a connected living using data hiding techniques. Our proposed data authenticity model securely hides the data source’s identification number within IoT data before sending it to edge devices. Edge-AI devices extract hidden information for verifying data authenticity. Existing data hiding approaches for biosignal cannot reconstruct original IoT data after extracting the hidden message from it (i.e., lossy) and are not usable for IoT data authenticity. We propose the first lossless IoT data hiding technique in this article based on error-correcting codes (ECCs). We conduct several experiments to demonstrate the performance of our proposed method. Experimental results establish the lossless property of the proposed approach while maintaining other data hiding properties.
Publisher: Elsevier BV
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2013
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/SCC.2011.15
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2017
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/FIT.2011.54
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: No publisher found
Date: 2018
Publisher: Elsevier BV
Date: 06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Elsevier BV
Date: 05-2014
Publisher: Inderscience Publishers
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer International Publishing
Date: 2021
Publisher: Elsevier BV
Date: 08-2019
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: IEEE
Date: 2006
DOI: 10.1109/ICNS.2006.22
Publisher: No publisher found
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
DOI: 10.1109/MCC.2016.76
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: IEEE
Date: 06-2017
Publisher: Springer Science and Business Media LLC
Date: 21-06-2009
DOI: 10.1007/S10916-008-9172-6
Abstract: With cardiovascular disease as the number one killer of modern era, Electrocardiogram (ECG) is collected, stored and transmitted in greater frequency than ever before. However, in reality, ECG is rarely transmitted and stored in a secured manner. Recent research shows that eavesdropper can reveal the identity and cardiovascular condition from an intercepted ECG. Therefore, ECG data must be anonymized before transmission over the network and also stored as such in medical repositories. To achieve this, first of all, this paper presents a new ECG feature detection mechanism, which was compared against existing cross correlation (CC) based template matching algorithms. Two types of CC methods were used for comparison. Compared to the CC based approaches, which had 40% and 53% misclassification rates, the proposed detection algorithm did not perform any single misclassification. Secondly, a new ECG obfuscation method was designed and implemented on 15 subjects using added noises corresponding to each of the ECG features. This obfuscated ECG can be freely distributed over the internet without the necessity of encryption, since the original features needed to identify personal information of the patient remain concealed. Only authorized personnel possessing a secret key will be able to reconstruct the original ECG from the obfuscated ECG. Distribution of the would appear as regular ECG without encryption. Therefore, traditional decryption techniques including powerful brute force attack are useless against this obfuscation.
Publisher: IEEE
Date: 08-2011
Publisher: Wiley
Date: 05-09-2019
DOI: 10.1002/CPE.4706
Publisher: Elsevier BV
Date: 08-2018
Publisher: Elsevier BV
Date: 10-2020
Publisher: IEEE
Date: 09-2014
Publisher: ACM
Date: 10-07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 07-2008
Publisher: Oxford University Press (OUP)
Date: 22-03-2022
Abstract: The edge authentication of graphs has been studied in the literature because graphs are one of the most widely used data organization structures. The majority of such schemes cannot be used to authenticate general directed graphs (GDGs) other schemes cannot be used for addressing either the issue of dynamic update or the issue of information leakage (such as the existence of nodes/edges and structural relationship of the graph). Also, all the existing schemes do not consider the forward security: if the signer’s secret key has been compromised, all previously generated signatures remain valid. This property provides high-level security protection for authentication schemes. To address these issues, in this work, we propose a forward-secure edge authentication scheme for GDGs. Observe that existing such schemes can only give a proof such that ‘there is an edge between nodes $u$ and $v$’. Our scheme, however, can directly give a proof such that ‘there is no edge between nodes $u$ and $v$’, which makes the function of edge authentication schemes more erse. Moreover, our proposed scheme is proven to be secure against an adaptive chosen-message adversary in the random oracle model. To show its desirable performance, we analyze the computational costs of our scheme and compare it with other related schemes in terms of features.
Publisher: IEEE
Date: 2006
Publisher: ACM
Date: 04-02-2020
Publisher: Royal Society of Chemistry (RSC)
Date: 2021
DOI: 10.1039/D1LC00179E
Abstract: A rapid, low-cost, and disposable microfluidic thread-based isotachophoresis method was developed for the purification and preconcentration of nucleic acids from biological s les, prior to their extraction and successful analysis using qPCR.
Publisher: ACM
Date: 05-11-2018
Publisher: IEEE
Date: 08-2011
Publisher: IEEE
Date: 08-2012
Publisher: Elsevier BV
Date: 03-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Elsevier BV
Date: 07-2020
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
Publisher: Elsevier BV
Date: 2014
Publisher: Elsevier BV
Date: 02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: No publisher found
Date: 2007
Publisher: IEEE
Date: 11-2006
Publisher: No publisher found
Date: 2004
Publisher: No publisher found
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 09-2016
Publisher: IEEE
Date: 04-2013
Publisher: Elsevier BV
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2023
Publisher: Elsevier BV
Date: 07-2018
DOI: 10.1016/J.CMPB.2018.03.019
Abstract: Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual behavioural analysis of mental disorders. Despite the very recent surge in interest in this area, relatively little has been done thus far to assist in iduals living with Obsessive Compulsive Disorder. The work proposed herein represents a proof of concept system designed to demonstrate the efficacy of such an approach, from the computational perspective. The specific focus of this work lies in demonstrating a mechanism for clustering different kinds of Obsessive Compulsive Disorder behaviours and then comparing new behaviours to existing behaviours to determine the approximate level of anxiety represented by a compulsive behaviour. The proposed system uses Temporal Motion Heat Maps, SURF descriptors, a visual bag of words model and SVM-based classification to categorise representations of various behaviours commonly seen in OCD. Moreover, we apply a set of statistical measures to the images in a given category in order to derive an approximate anxiety level for a given compulsive behaviour. This proof of concept is an essential step in the direction of integrating computational approaches into the treatment of psychiatric conditions such as Obsessive Compulsive Disorder, for more effective recovery. Results gleaned from experimental simulations indicate that the proposed system is capable of correctly classifying different types of simulated Obsessive Compulsive Disorder behaviour classes 75% of the time, with the misclassifications almost exclusively occurring when two behavioural clusters appear highly similar. Based on this information the proposed system is then able to assign an approximate behavioural anxiety level to the compulsive behaviours that meets the approval of a mental health professional. The proposed system demonstrates a good ability to categorise various types of simulated OCD behaviour, in addition to establishing an approximate anxiety level for a given compulsive behaviour. This research demonstrates strong potential for the use of such systems in assisting mental health professionals looking to better understand their patients' condition and treatment progress across time.
Publisher: Springer International Publishing
Date: 07-09-2022
Publisher: No publisher found
Date: 2020
Publisher: Elsevier BV
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: No publisher found
Date: 2015
Publisher: Elsevier BV
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2020
Publisher: IEEE
Date: 2000
Publisher: Elsevier BV
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Elsevier BV
Date: 06-2013
Publisher: Elsevier BV
Date: 06-2021
Publisher: Elsevier BV
Date: 02-2017
DOI: 10.1016/J.CMPB.2016.10.018
Abstract: In home-based context-aware monitoring patient's real-time data of multiple vital signs (e.g. heart rate, blood pressure) are continuously generated from wearable sensors. The changes in such vital parameters are highly correlated. They are also patient-centric and can be either recurrent or can fluctuate. The objective of this study is to develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values, and prediction of anomalies using the interrelation among multiple vital signs. In this paper, multi-label classification algorithms are applied in classifier design to forecast these values and related abnormalities. We proposed a completely new approach of patient-specific vital sign prediction system using their correlations. The developed technique can guide healthcare professionals to make accurate clinical decisions. Moreover, our model can support many patients with various clinical conditions concurrently by utilizing the power of cloud computing technology. The developed method also reduces the rate of false predictions in remote monitoring centres. In the experimental settings, the statistical features and correlations of six vital signs are formulated as multi-label classification problem. Eight multi-label classification algorithms along with three fundamental machine learning algorithms are used and tested on a public dataset of 85 patients. Different multi-label classification evaluation measures such as Hamming score, F1-micro average, and accuracy are used for interpreting the prediction performance of patient-specific situation classifications. We achieved 90-95% Hamming score values across 24 classifier combinations for 85 different patients used in our experiment. The results are compared with single-label classifiers and without considering the correlations among the vitals. The comparisons show that multi-label method is the best technique for this problem domain. The evaluation results reveal that multi-label classification techniques using the correlations among multiple vitals are effective ways for early estimation of future values of those vitals. In context-aware remote monitoring this process can greatly help the doctors in quick diagnostic decision making.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2009
Publisher: IEEE
Date: 2005
DOI: 10.1109/LCN.2005.7
Publisher: Elsevier BV
Date: 09-2015
Publisher: IEEE
Date: 07-2013
Publisher: Elsevier BV
Date: 07-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
DOI: 10.1109/MCC.2016.30
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: No publisher found
Date: 2021
Publisher: IEEE
Date: 09-2021
Publisher: IEEE
Date: 09-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 05-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Association for Computing Machinery (ACM)
Date: 17-04-2022
DOI: 10.1145/3446372
Abstract: Social media have been growing rapidly and become essential elements of many people’s lives. Meanwhile, social media have also come to be a popular source for identity deception. Many social media identity deception cases have arisen over the past few years. Recent studies have been conducted to prevent and detect identity deception. This survey analyzes various identity deception attacks, which can be categorized into fake profile, identity theft, and identity cloning. This survey provides a detailed review of social media identity deception detection techniques. It also identifies primary research challenges and issues in the existing detection techniques. This article is expected to benefit both researchers and social media providers.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2022
Publisher: No publisher found
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/NCA.2010.18
Publisher: Springer International Publishing
Date: 2020
Publisher: No publisher found
Date: 2011
Publisher: Elsevier BV
Date: 07-2014
Publisher: IEEE Comput. Soc
Date: 1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2015
DOI: 10.1109/MCC.2015.45
Publisher: Elsevier BV
Date: 03-2020
DOI: 10.1016/J.CMPB.2019.105126
Abstract: Home-based personal healthcare systems are becoming popular and affordable due to the development of Internet of Things (IoT) devices. However, with an increasing number of users, such healthcare systems are challenged to store and process enormous volumes of data. For instance, multi-biosignal data are collected continuously from patients using IoT device like body sensors and are sent to the server by portable devices for further analysis (e.g., knowledge discovery or the clinical event prediction). These enormous amount of data from large number of patients are causing the transmission overhead and high latency in the network which are responsible for inefficiency issues in clinical event prediction. To address these problems, in this paper, data assessment method is introduced to improve the efficiency in data collection and data prediction. The assessment algorithm is inspired by National Early Warning Score (NEWS) used in Emergency Department. In our method, only the abnormal time-sequence data for analysis are sent to the server. Thus, the waiting time of data before prediction can be optimized because data with higher priority are processed in front of those with lower priority, which helps our system to provide diagnostic decisions in a proper time according to patients' urgency. Our experiments show that the proposed model ideally can save 20% volume of data in the collection and can reduce 75% waiting time of data with the highest priority before predicting. In addition, the waiting time of data for further analysis is optimized compared to the normal processing flow. The paper introduces an enhanced healthcare system with assessing data priority in order to optimize the data collection and the prediction in terms of data size and waiting time.
Publisher: IEEE
Date: 11-2007
Publisher: IEEE
Date: 11-2007
Publisher: Elsevier BV
Date: 08-2022
Publisher: Elsevier BV
Date: 05-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-02-2023
Publisher: Springer International Publishing
Date: 23-02-2022
Publisher: IEEE
Date: 2002
Publisher: Elsevier BV
Date: 09-2019
Publisher: Wiley
Date: 17-03-2011
DOI: 10.1002/SEC.262
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/NCA.2010.46
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 09-2015
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 09-2014
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Wiley
Date: 09-2008
DOI: 10.1002/SEC.44
Publisher: Elsevier BV
Date: 12-2017
Publisher: Elsevier BV
Date: 05-2016
Publisher: IEEE
Date: 03-2020
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 07-2017
Publisher: Elsevier BV
Date: 07-2017
Publisher: Institution of Engineering and Technology (IET)
Date: 06-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Publisher: No publisher found
Date: 2021
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/NCA.2010.37
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: Elsevier BV
Date: 11-2019
Publisher: Elsevier BV
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Springer International Publishing
Date: 2022
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Springer New York
Date: 2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Wiley
Date: 16-06-2023
DOI: 10.1002/CPE.7812
Abstract: ECG signal is widely used in most cardiology e‐health systems. Patients may be monitored continuously for at least 12 h a day. Therefore, the ECG signal size transmitted to a hospital server during continuous monitoring is significant. Furthermore, transmission of the large size ECG signal is a power consuming process. ECG compression is one of the proposed solutions to overcome this problem. In this paper, a new fractal‐based ECG lossy compression technique is proposed. It is clear that fractal can use ECG signal self similarity characteristics efficiently to achieve high compression ratios. The proposed technique is based on developing the fractal model in conjunction with Iterated Function System. Fractal is well known as a time consuming technique, and therefore, new mathematical development is proposed to potentially reduce fractal computations. Experiments have proven the significant performance of fast fractal in comparison with the traditional version. Furthermore, the resultant compression ratios are close to the traditional fractal results and higher than other existing techniques.
Publisher: Elsevier BV
Date: 05-2011
Publisher: Elsevier BV
Date: 05-2016
Publisher: IEEE
Date: 10-2013
Publisher: Springer Science and Business Media LLC
Date: 06-01-2011
DOI: 10.1007/S10916-009-9412-4
Abstract: Adoption of compression technology is often required for wireless cardiovascular monitoring, due to the enormous size of Electrocardiography (ECG) signal and limited bandwidth of Internet. However, compressed ECG must be decompressed before performing human identification using present research on ECG based biometric techniques. This additional step of decompression creates a significant processing delay for identification task. This becomes an obvious burden on a system, if this needs to be done for a trillion of compressed ECG per hour by the hospital. Even though the hospital might be able to come up with an expensive infrastructure to tame the exuberant processing, for small intermediate nodes in a multihop network identification preceded by decompression is confronting. In this paper, we report a technique by which a person can be identified directly from his / her compressed ECG. This technique completely obviates the step of decompression and therefore upholds biometric identification less intimidating for the smaller nodes in a multihop network. The biometric template created by this new technique is lower in size compared to the existing ECG based biometrics as well as other forms of biometrics like face, finger, retina etc. (up to 8302 times lower than face template and 9 times lower than existing ECG based biometric template). Lower size of the template substantially reduces the one-to-many matching time for biometric recognition, resulting in a faster biometric authentication mechanism.
Publisher: Institution of Engineering and Technology (IET)
Date: 12-2017
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 10-2014
Publisher: Elsevier BV
Date: 05-2019
Publisher: Springer International Publishing
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
DOI: 10.1109/MCC.2014.65
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer Berlin Heidelberg
Date: 2001
Publisher: Elsevier BV
Date: 10-2015
Publisher: No publisher found
Date: 2001
Publisher: Springer Science and Business Media LLC
Date: 06-07-2012
Publisher: Elsevier BV
Date: 06-2014
Publisher: IEEE
Date: 11-2006
Publisher: IEEE Comput. Soc
Date: 2000
Publisher: IEEE
Date: 08-2011
Publisher: Elsevier BV
Date: 07-2020
Publisher: Wiley
Date: 07-2010
DOI: 10.1002/SEC.76
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: Elsevier BV
Date: 04-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: Wiley
Date: 28-08-2011
DOI: 10.1002/SEC.221
Publisher: No publisher found
Date: 2017
Publisher: IEEE
Date: 07-2013
Publisher: IEEE
Date: 07-2013
Publisher: No publisher found
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: No publisher found
Date: 2022
Publisher: Wiley
Date: 03-08-2011
DOI: 10.1002/SEC.226
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2023
Publisher: IEEE
Date: 12-2010
Publisher: Springer London
Date: 1998
DOI: 10.1007/BFB0110078
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2001
DOI: 10.1109/35.920862
Publisher: IEEE
Date: 03-2016
Publisher: Elsevier BV
Date: 06-2013
Publisher: IEEE
Date: 08-2011
DOI: 10.1109/NCA.2011.40
Publisher: Elsevier BV
Date: 06-2019
Start Date: 2018
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 2021
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 2023
Funder: Australian Research Council
View Funded ActivityStart Date: 2019
End Date: 2022
Funder: Qatar National Research Fund
View Funded ActivityStart Date: 2022
End Date: 2024
Funder: Australian Research Council
View Funded ActivityStart Date: 07-2018
End Date: 12-2023
Amount: $383,826.00
Funder: Australian Research Council
View Funded ActivityStart Date: 01-2018
End Date: 05-2021
Amount: $330,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2021
End Date: 12-2024
Amount: $450,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 12-2022
End Date: 12-2025
Amount: $390,000.00
Funder: Australian Research Council
View Funded Activity