ORCID Profile
0000-0002-3635-4252
Current Organisations
Curtin University
,
University of Melbourne
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Research Topics
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.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Signal Processing | Communications Technologies | Computer Communications Networks | Signal Processing | Systems Theory And Control | Biomedical Engineering | Electrical and Electronic Engineering | Artificial Intelligence and Image Processing | Automotive Engineering | Optimisation | Automotive Engineering | Structural Engineering | Biomedical Engineering not elsewhere classified | Simulation And Modelling | Pattern Recognition and Data Mining | Distributed and Grid Systems | Image Processing | Pattern Recognition | Catalytic Process Engineering | Non-automotive Combustion and Fuel Engineering (incl. Alternative/Renewable Fuels) | Stochastic Analysis And Modelling | Other Electronic Engineering | Polymers and Plastics | Engineering/Technology Instrumentation | Neurobiology | Composite and Hybrid Materials | Cardiology (incl. Cardiovascular Diseases) | Transport Engineering | Applied Mathematics | Broadband Network Technology | Construction Materials | Environmental Engineering | Civil Engineering | Chemical Engineering | Nanomaterials | Forestry Biomass and Bioproducts | Photonic and electro-optical devices sensors and systems (excl. communications) | Manufacturing Engineering Not Elsewhere Classified | Distributed Computing | Knowledge Representation and Machine Learning | Manufacturing Engineering | Control Engineering | Horticultural Crop Protection (Pests, Diseases and Weeds) | Materials Engineering | Robotics And Mechatronics | Biomedical Engineering Not Elsewhere Classified | Optical fibre communication systems and technologies | Data Security | Decision Support And Group Support Systems | Biomedical Instrumentation | Medical Devices | Crop and Pasture Production | Road And Rail Transportation | Wireless Communications | Environmental Engineering Modelling | Foetal Development and Medicine | Ubiquitous Computing | Computer Vision | Mobile Technologies | Networking and Communications | Transport Engineering | Crop and Pasture Protection (Pests, Diseases and Weeds) | Crop and Pasture Biomass and Bioproducts | Electronics sensors and digital hardware | Computer Communications Networks |
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Information Processing Services (incl. Data Entry and Capture) | Expanding Knowledge in Technology | Expanding Knowledge in Engineering | Road safety | Information and Communication Services not elsewhere classified | Computer equipment | Hydrogen Storage | Telecommunications | Combined operations | Automotive equipment | Mobile Data Networks and Services | Diagnostic methods | Diagnostic Methods | Weather | Application tools and system utilities | Atmospheric Composition (incl. Greenhouse Gas Inventory) | Data, image and text equipment | Environmentally Sustainable Manufacturing not elsewhere classified | Other road transport | Clay Products | Urban and Industrial Air Quality | Modules—special and attached processors | Communication services not elsewhere classified | Application Tools and System Utilities | Medical Instruments | Health Status (e.g. Indicators of Well-Being) | Hydrogen Distribution | Integrated (ecosystem) assessment and management | Control of Plant Pests, Diseases and Exotic Species in Farmland, Arable Cropland and Permanent Cropland Environments | Forestry not elsewhere classified | Construction Materials Performance and Processes not elsewhere classified | Medical instrumentation | Expanding Knowledge in the Medical and Health Sciences | Scientific instrumentation | Hydrogen Production from Renewable Energy | Computer software and services not elsewhere classified | Plastic Products (incl. Construction Materials) | Transport not elsewhere classified | Environmentally Sustainable Transport not elsewhere classified | Machinery and equipment not elsewhere classified
Related Links
Publications
An efficient genetic algorithm for maximizing area coverage in wireless sensor networks
Publisher: Elsevier BV
Date: 07-2019
Privacy-Preserving Aggregation of Smart Metering via Transformation and Encryption
Publisher: IEEE
Date: 08-2017
Automatic tumour volume delineation in respiratory-gated PET images
Publisher: Wiley
Date: 02-2011
DOI: 10.1111/J.1754-9485.2010.02231.X
Abstract: Positron emission tomography (PET) is a state-of-the-art functional imaging technique used in the accurate detection of cancer. The main problem with the tumours present in the lungs is that they are non-stationary during each respiratory cycle. Tumours in the lungs can get displaced up to 2.5 cm during respiration. Accurate detection of the tumour enables avoiding the addition of extra margin around the tumour that is usually used during radiotherapy treatment planning. This paper presents a novel method to detect and track tumour in respiratory-gated PET images. The approach followed to achieve this task is to automatically delineate the tumour from the first frame using support vector machines. The resulting volume and position information from the first frame is used in tracking its motion in the subsequent frames with the help of level set (LS) deformable model. An excellent accuracy of 97% is obtained using wavelets and support vector machines. The volume calculated as a result of the machine learning (ML) stage is used as a constraint for deformable models and the tumour is tracked in the remaining seven phases of the respiratory cycle. As a result, the complete information about tumour movement during each respiratory cycle is available in relatively short time. The combination of the LS and ML approach accurately delineated the tumour volume from all frames, thereby providing a scope of using PET images towards planning an accurate and effective radiotherapy treatment for lung cancer.
Determination of Object Directions Using Optical Flow for Crowd Monitoring
Publisher: Springer Berlin Heidelberg
Date: 2013
Classification of convulsive psychogenic non-epileptic seizures using histogram of oriented motion of accelerometry signals
Publisher: IEEE
Date: 08-2015
A Self-Compassion Group Intervention for Patients Living With Chronic Medical Illness
Publisher: Physicians Postgraduate Press, Inc
Date: 26-09-2019
DOI: 10.4088/PCC.19M02470
Fuzzy gated neuronal architecture for pattern recognition
Publisher: IEEE
Recognizing central and obstructive sleep apnea events from normal breathing events in ECG recordings
Publisher: IEEE
Date: 09-2008
Secure Data Aggregation in Wireless Sensor Networks
Publisher: Springer London
Date: 2009
Robust Heart Rate Estimation During Physical Exercise Using Photoplethysmographic Signals
Publisher: IEEE
Date: 07-2018
Key Management in Wireless Sensor Networks
Publisher: Springer London
Date: 2008
Probabilistic Detection of Crowd Events on Riemannian Manifolds
Publisher: IEEE
Date: 11-2014
A robust tracking control scheme for robotic manipulators with uncertain dynamics
Publisher: IEEE
A modifi v-SV method for simplified regression
Publisher: IEEE
Maximum Entropy-Based Auto Drift Correction Using High- and Low-Precision Sensors
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.
An extrapolated sequential minimal optimization algorithm for support vector machines
Publisher: IEEE
Particle swarm optimization–based power allocation for Alamouti amplify and forward relaying protocol
Publisher: Wiley
Date: 11-04-2019
DOI: 10.1002/DAC.3954
Estimation of crowd density by clustering motion cues
Publisher: Springer Science and Business Media LLC
Date: 23-09-2014
Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2017
A pilot study on the use of accelerometer sensors for monitoring post acute stroke patients
Publisher: IEEE
Date: 07-2013
Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Real Value Solvent Accessibility Prediction using Adaptive Support Vector Regression
Publisher: IEEE
Date: 04-2007
Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2009
The effects of asymmetric volume conductor modeling on non-invasive fetal ECG extraction
Publisher: IOP Publishing
Date: 11-2018
Abstract: Non-invasive fetal electrocardiography (NI-FECG) shows promise for capturing novel physiological information that may indicate signs of fetal distress. However, significant deterioration in NI-FECG signal quality occurs during the presence of a highly non-conductive layer known as vernix caseosa which forms on the fetal body surface beginning in approximately the 28th week of gestation. This work investigates asymmetric modeling of vernix caseosa and other maternal-fetal tissues in accordance with clinical observations and assesses their impacts for NI-FECG signal processing. We develop a process for simulating dynamic maternal-fetal abdominal ECG mixtures using a synthetic cardiac source model embedded in a finite element volume conductor. Using this process, changes in NI-FECG signal morphology are assessed in an extensive set of finite element models including spatially variable distributions of vernix caseosa. Our simulations show that volume conductor asymmetry can result in over 70% error in the observed T/QRS ratio and significant changes to signal morphology compared to a homogeneous volume conductor model. Volume conductor effects must be considered when analyzing T/QRS ratios obtained via NI-FECG and should be considered in future algorithm benchmarks using simulated data. This work shows that without knowledge of the influence of volume conductor effects, clinical evaluation of the T/QRS ratio derived via NI-FECG should be avoided.
Risk-Aware Beacon Scheduling for Tree-Based ZigBee/IEEE 802.15.4 Wireless Networks
Publisher: ICST
Date: 2008
Distributed Real-Time IoT for Autonomous Vehicles
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data
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.
Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
A pilot study of urban noise monitoring architecture using wireless sensor networks
Publisher: IEEE
Date: 08-2013
Detection of Anomalous Crowd Behaviour Using Hyperspherical Clustering
Publisher: IEEE
Date: 11-2014
A fog-driven dynamic resource allocation technique in ultra dense femtocell networks
Publisher: Elsevier BV
Date: 11-2019
Poincaré plot methods for heart rate variability analysis
Publisher: Springer US
Date: 2013
A stable adaptive PI controller for AQM
Publisher: IEEE
Date: 10-2007
Digital estimation and control with a new discrete time operator
Publisher: IEEE
Tone–Entropy Analysis of Heart Rate Variability in Cardiac Autonomic Neuropathy
Publisher: CRC Press
Date: 11-09-2017
Classification of convulsive psychogenic non-epileptic seizures using muscle transforms obtained from accelerometry signal
Publisher: IEEE
Date: 08-2015
A Rapid Hybrid Clustering Algorithm for Large Volumes of High Dimensional Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
A new adaptive image sequence coding scheme using Kohonen's SOFM
Publisher: IEEE
Time‐frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures
Publisher: Wiley
Date: 03-05-2013
DOI: 10.1111/EPI.12207
Abstract: A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in-patient video-electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time-frequency mapping of data from a movement-recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES. Time-frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure-like events from 35 patients recorded during in-patient video-EEG monitoring. Twenty-six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time-frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56-s blocks for the course of each event. The coefficient of variation (CoV) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23% p < 0.001). A blinded review of the time-frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as "nondiagnostic." Using a CoV cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed. Time-frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure-like events.
Fetal Heart Rate Variability
Publisher: CRC Press
Date: 11-09-2017
Achieving AI-Enabled Robust End-to-End Quality of Experience Over Backhaul Radio Access Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2022
Detection of epileptic seizure based on entropy analysis of short-term EEG
Publisher: Public Library of Science (PLoS)
Date: 15-03-2018
Model-based estimation of aortic and mitral valves opening and closing timings in developing human fetuses
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Evolving Fuzzy Rules for Anomaly Detection in Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2015
Computational Intelligence for Movement Sciences
Publisher: IGI Global
Date: 2006
Computational Intelligence in Biomedical Engineering
Publisher: CRC Press
Date: 04-12-2007
Ensemble Fuzzy Clustering Using Cumulative Aggregation on Random Projections
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
The Influence of Vectorcardiogram Orientation on the T/QRS Ratio Obtained Via Non-Invasive Fetal ECG
Publisher: IEEE
Date: 07-2019
Spatial Indexing for Data Searching in Mobile Sensing Environments
Publisher: MDPI AG
Date: 18-06-2017
DOI: 10.3390/S17061427
Approximate Cluster Heat Maps of Large High-Dimensional Data
Publisher: IEEE
Date: 08-2018
Defining asymmetry in heart rate variability signals using a Poincaré plot
Publisher: IOP Publishing
Date: 08-10-2009
DOI: 10.1088/0967-3334/30/11/007
Abstract: The asymmetry in heart rate variability is a visibly obvious phenomenon in the Poincaré plot of normal sinus rhythm. It shows the unevenness in the distribution of points above and below the line of identity, which indicates instantaneous changes in the beat to beat heart rate. The major limitation of the existing asymmetry definition is that it considers only the instantaneous changes in the beat to beat heart rate rather than the pattern (increase/decrease). In this paper, a novel definition of asymmetry is proposed considering the geometry of a 2D Poincaré plot. Based on the proposed definition, traditional asymmetry indices--Guzik's index (GI), Porta's index (PI) and Ehlers' index (EI)--have been redefined. In order to compare the effectiveness of the new definition, all indices have been calculated for RR interval series of 54 subjects with normal sinus rhythm of 5 min and 30 min duration. The new definition resulted in a higher prevalence of normal subjects showing asymmetry in heart rate variability.
Estimation of vocal fold plane in 3D CT images for diagnosis of vocal fold abnormalities
Publisher: IEEE
Date: 08-2015
Classification of multidimensional trajectories for acoustic modeling using support vector machines
Publisher: IEEE
Efficient Homomorphic Hashing Approach for Secure Reprogramming in Wireless Sensor Networks
Publisher: MECS Publisher
Date: 15-02-2012
Comparison of Heuristic Algorithms for the Degree Constrained Minimum Spanning Tree
Publisher: Springer US
Date: 1996
Complex Correlation Measure: a novel descriptor for Poincaré plot
Publisher: Springer Science and Business Media LLC
Date: 2009
Density Estimation Using a Generalized Neuron
Publisher: IEEE
Date: 07-2006
Stable Hybrid Adaptive Pole Assignment Control
Publisher: Elsevier BV
Date: 07-1993
Pulmonary tumor volume delineation in PET images using deformable models
Publisher: IEEE
Date: 08-2008
The Role of Visual Assessment of Clusters for Big Data Analysis: From Real-World Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Crowd density estimation based on optical flow and hierarchical clustering
Publisher: IEEE
Date: 08-2013
Rate Constrained Robust Adaptive Control
Publisher: IEEE
Date: 06-1992
Cross Entropy Profiling to Test Pattern Synchrony in Short-Term Signals
Publisher: IEEE
Date: 07-2019
Adaptive Tracking Control of Robot Manipulators in Cartesian Space Coordinates
Publisher: IEEE
Date: 06-1991
Pattern based anomalous user detection in cognitive radio networks
Publisher: IEEE
Date: 04-2015
Stability, Consistency and Performance of Distribution Entropy in Analysing Short Length Heart Rate Variability (HRV) Signal
Publisher: Frontiers Media SA
Date: 20-09-2017
Crowd Activity Change Point Detection in Videos via Graph Stream Mining
Publisher: IEEE
Date: 06-2018
Solving Protein Structures Using Molecular Replacement Via Protein Fragments
Publisher: Springer Berlin Heidelberg
Analyzing temporal variability of standard descriptors of Poincaré plots
Publisher: Elsevier BV
Date: 11-2010
DOI: 10.1016/J.JELECTROCARD.2010.09.001
Abstract: The Poincaré map is a visual technique to recognize the hidden correlation patterns of a time series signal. The standard descriptors of the Poincaré map are used to quantify the plot that measures the gross variability of the time series data. However, the problem lies in capturing temporal information of the plot quantitatively. In this article, we propose a new formulation for calculating the standard descriptors SD1 and SD2 from localized measures SD1^(w) and SD2^(w). To justify the importance of the temporal measure, SD1^(w), SD2^(w) are calculated for the 2 case studies (normal sinus rhythm [NSR] vs congestive heart failure and NSR vs arrhythmia) and are compared with the performance using the overall measures (SD1, SD2). Using overall SD1, receiver operating characteristic areas of 0.72 and 0.86 were obtained for NSR vs congestive heart failure and NSR vs arrhythmia, and using the proposed method resulted in 0.82 and 0.89. Because we have shown that the overall SD1 and SD2 are functions of the respective localized measures SD1^(w) and SD2^(w), we can conclude that use of localized measure provides equal or higher performance in pathology detection compared with the overall SD1 or SD2.
A vision-based system to detect potholes and uneven surfaces for assisting blind people
Publisher: IEEE
Date: 05-2016
Guest Editorial: Computational Intelligence for Human-in-the-Loop Cyber Physical Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
A review of design aspects for neurocomputers
Publisher: IEEE
Privacy-preserving collaborative fuzzy clustering
Publisher: Elsevier BV
Date: 07-2018
A Data-centered Fog PIatform for Smart Living
Publisher: Chapman and Hall/CRC
Date: 06-01-2017
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms
Publisher: IEEE
Date: 07-2017
Design of low-cost autonomous water quality monitoring system
Publisher: IEEE
Date: 08-2013
Challenges and prospects for negawatt trading in light of recent technological developments
Publisher: Springer Science and Business Media LLC
Date: 12-08-2020
Fast and Scalable Big Data Trajectory Clustering for Understanding Urban Mobility
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
A hybrid neural approach to combinatorial optimization
Publisher: Elsevier BV
Date: 06-1996
Improving load forecasting based on deep learning and K-shape clustering
Publisher: IEEE
Date: 05-2017
Hilbert Pair of Wavelets via the Matching Design Technique
Publisher: IEEE
An Adaptive REM for Improving AQM Performance
Publisher: IEEE
Date: 2008
DOI: 10.1109/ICC.2008.22
Network-design sensitivity analysis
Publisher: ACM
Date: 16-06-2014
Stabilizing RED using a Fuzzy Controller
Publisher: IEEE
Date: 06-2007
DOI: 10.1109/ICC.2007.52
A hierarchical transport architecture for wireless sensor networks
Publisher: IEEE
Date: 12-2008
Identifying Groups of Fake Reviewers Using a Semisupervised Approach
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Risk-Aware Distributed Beacon Scheduling for Tree-Based ZigBee Wireless Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
DOI: 10.1109/TMC.2011.88
Motor recovery monitoring in post acute stroke patients using wireless accelerometer and cross-correlation
Publisher: IEEE
Date: 07-2013
Pattern classification using a self-organizing neural network
Publisher: IEEE
Anomalous Crowd Event Analysis Using Isometric Mapping
Publisher: Springer International Publishing
Date: 25-12-2015
Algorithms for two dimensional multi set canonical correlation analysis
Publisher: Elsevier BV
Date: 08-2018
Adaptive pole placement control subject to input amplitude constraints
Publisher: IEEE
A Machine Learning Approach for Classification of Tremor - A Neurological Movement Disorder
Publisher: Springer International Publishing
Date: 24-08-2019
Distributed data fusion using support vector machines
Publisher: Int. Soc. Inf. Fusion
Energy Efficient Time Synchronization in WSN for Critical Infrastructure Monitoring
Publisher: Springer Berlin Heidelberg
Date: 2011
An Application Placement Technique for Concurrent IoT Applications in Edge and Fog Computing Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
PROTEIN SECONDARY STRUCTURE PREDICTION USING SUPPORT VECTOR MACHINES AND A NEW FEATURE REPRESENTATION
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2011
DOI: 10.1142/S1469026806002076
Abstract: Knowledge of the secondary structure and solvent accessibility of a protein plays a vital role in the prediction of fold, and eventually the tertiary structure of the protein. A challenging issue of predicting protein secondary structure from sequence alone is addressed. Support vector machines (SVM) are employed for the classification and the SVM outputs are converted to posterior probabilities for multi-class classification. The effect of using Chou–Fasman parameters and physico-chemical parameters along with evolutionary information in the form of position specific scoring matrix (PSSM) is analyzed. These proposed methods are tested on the RS126 and CB513 datasets. A new dataset is curated (PSS504) using recent release of CATH. On the CB513 dataset, sevenfold cross-validation accuracy of 77.9% was obtained using the proposed encoding method. A new method of calculating the reliability index based on the number of votes and the Support Vector Machine decision value is also proposed. A blind test on the EVA dataset gives an average Q 3 accuracy of 74.5% and ranks in top five protein structure prediction methods. Supplementary material including datasets are available on .
Real-Time Urban Microclimate Analysis Using Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2018
Visual Structural Assessment and Anomaly Detection for High-Velocity Data Streams
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Security games for voltage control in smart grid
Publisher: IEEE
Date: 06-0066
Computerised Cardiotocography Analysis for the Automated Detection of Fetal Compromise during Labour: A Review
Publisher: MDPI AG
Date: 25-08-2023
DOI: 10.3390/BIOENGINEERING10091007
Abstract: The measurement and analysis of fetal heart rate (FHR) and uterine contraction (UC) patterns, known as cardiotocography (CTG), is a key technology for detecting fetal compromise during labour. This technology is commonly used by clinicians to make decisions on the mode of delivery to minimise adverse outcomes. A range of computerised CTG analysis techniques have been proposed to overcome the limitations of manual clinician interpretation. While these automated techniques can potentially improve patient outcomes, their adoption into clinical practice remains limited. This review provides an overview of current FHR and UC monitoring technologies, public and private CTG datasets, pre-processing steps, and classification algorithms used in automated approaches for fetal compromise detection. It aims to highlight challenges inhibiting the translation of automated CTG analysis methods from research to clinical application and provide recommendations to overcome them.
Approximating Dunn’s Cluster Validity Indices for Partitions of Big Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Computational Intelligence Techniques
Publisher: CRC Press
Date: 04-12-2009
Wireless Accelerometry is Feasible in Acute Monitoring of Upper Limb Motor Recovery after Ischemic Stroke
Publisher: S. Karger AG
Date: 2014
DOI: 10.1159/000360808
Abstract: b i Background: /i /b Clinical deterioration in the acute stage of ischemic stroke powerfully predicts outcome and may serve as a marker for urgent intervention. However, accurate monitoring of acute stroke patients is h ered by the lack of validated continuous monitoring devices. We sought to assess the use of wireless accelerometry in this setting, hypothesizing that stroke patients would have a greater difference in movement between upper limbs than controls and that the magnitude of correlation between upper limb movements would be negatively associated with the National Institutes of Health Stroke Scale (NIHSS) score. b i Methods: /i /b In this pilot study, 20 patients with acute ischemic stroke and unilateral upper limb weakness and 10 controls were recruited from a comprehensive stroke centre. All subjects were fitted with two 3-axis accelerometers and underwent 24 h of continuous accelerometry recording of upper limb movements and repeat NIHSS assessments. The intra-class correlation coefficient (ICC), assessing the similarity (or otherwise) of spontaneous movements in each arm was calculated. The association between NIHSS (total and motor subset scores) and the magnitude of ICC was estimated by Spearman's rank correlation, receiver-operating characteristic curve analysis was performed and the optimal diagnostic threshold value of ICC was calculated. b i Results: /i /b The magnitude of the ICC was significantly associated with the baseline NIHSS score (p = 0.02) and non-significantly associated with the baseline NIHSS motor score (p = 0.08). At the optimal diagnostic threshold of ICC magnitude = 0.7, wireless accelerometry distinguished patients from controls with a sensitivity of 0.95, a specificity of 0.6 and a diagnostic odds ratio of 28.5. b i Conclusions: /i /b The wireless accelerometry system successfully detects a motor deficit in the setting of acute ischemic stroke, accurately differentiating patients from controls, and correlates well with the baseline NIHSS score. Its use is feasible in the acute stroke setting. Overall, it shows promise as a diagnostic tool to continuously monitor acute stroke patients but requires validation in a larger trial.
Computational Intelligence in Electroencephalogram Analysis
Publisher: CRC Press
Date: 04-12-2009
Upper limb movement profiles during spontaneous motion in acute stroke
Publisher: IOP Publishing
Date: 04-2021
Abstract: Objective. The clinical assessment of upper limb hemiparesis in acute stroke involves repeated manual examination of hand movements during instructed tasks. This process is labour-intensive and prone to human error as well as being strenuous for the patient. Wearable motion sensors can automate the process by measuring characteristics of hand activity. Existing work in this direction either uses multiple sensors or complex instructed movements, or analyzes only the quantity of upper limb motion. These methods are obtrusive and strenuous for acute stroke patients and are also sensitive to noise. In this work, we propose to use only two wrist-worn accelerometer sensors to study the quality of completely spontaneous upper limb motion and investigate correlation with clinical scores for acute stroke care. Approach. The velocity time series estimated from acquired acceleration data during spontaneous motion is decomposed into smaller movement elements. Measures of density, duration and smoothness of these component elements are extracted and their disparity is studied across the two hands. Main results. Spontaneous upper limb motion in acute stroke can be decomposed into movement elements that resemble point-to-point reaching tasks. These elements are smoother and sparser in the normal hand than in the hemiparetic hand, and the amount of smoothness correlates with hemiparetic severity. Features characterizing the disparity of these movement elements between the two hands show statistical significance in differentiating mild-to-moderate and severe hemiparesis. Using data from 67 acute stroke patients, the proposed method can classify the two levels of hemiparetic severity with 85% accuracy. Additionally, compared to activity-based features, the proposed method is robust to the presence of noise in acquired data. Significance. This work demonstrates that the quality of upper limb motion can characterize and identify hemiparesis in stroke survivors. This is clinically significant towards the continuous automated assessment of hemiparesis in acute stroke using minimally intrusive wearable sensors.
Sleep-Wake Classification using Statistical Features Extracted from Photoplethysmographic Signals
Publisher: IEEE
Date: 07-2019
Summary and Future Trends
Publisher: CRC Press
Date: 04-12-2009
An Improved Approach to Crowd Event Detection by Reducing Data Dimensions
Publisher: Springer International Publishing
Date: 25-12-2015
Selection of Empirical Mode Decomposition Techniques for Extracting Breathing Rate From PPG
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
A Unified Flow Control Approach for QoS Balance in Differentiated Services
Publisher: IEEE
Date: 05-2010
Continuous time indirect adaptive control with disturbance rejection
Publisher: Elsevier BV
Date: 03-1992
Secondary measures of regularity from an entropy profile in detecting Arrhythmia
Publisher: IEEE
Date: 07-2017
A Scalable Framework for Trajectory Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
An improved scheme for privacy-preserving collaborative anomaly detection
Publisher: IEEE
Date: 03-2016
Tone entropy analysis of foetal heart rate variability
Publisher: MDPI AG
Date: 02-03-2015
DOI: 10.3390/E17031042
Smoke detection in video using wavelets and support vector machines
Publisher: Elsevier BV
Date: 11-2009
Multiscale entropy profiling to estimate complexity of heart rate dynamics
Publisher: American Physical Society (APS)
Date: 15-07-2019
Adaptive control of partially known single-link flexible manipulators
Publisher: IEEE
Date: 1991
Human head detection using Histograms of Oriented optical flow in low quality videos with occlusion
Publisher: IEEE
Date: 12-2013
Performance of a Convolutional Neural Network Derived from PPG Signal in Classifying Sleep Stages
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Analytical model of coding-based reprogramming protocols in lossy wireless sensor networks
Publisher: IEEE
Date: 06-2013
Ultrasound monitoring of inter-knee distances during gait
Publisher: IEEE
Date: 09-2009
Network architecture and QoS issues in the internet of things for a smart city
Publisher: IEEE
Date: 10-2012
A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
An experimental evaluation of neural network approach to circuit partitioning
Publisher: IEEE
Control and Communication Techniques for the Smart Grid: An Energy Efficiency Perspective
Publisher: Elsevier BV
Date: 2014
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
Publisher: Association for Computing Machinery (ACM)
Date: 15-12-2023
DOI: 10.1145/3544836
Abstract: Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a erse range of IoT application scenarios. To fully utilize the potential of this computing paradigm, scalable, adaptive, and accurate scheduling mechanisms and algorithms are required to efficiently capture the dynamics and requirements of users, IoT applications, environmental properties, and optimization targets. This article presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. Based on our new classification schemes, current works in the literature are analyzed, research gaps of each category are identified, and respective future directions are described.
Geospatial Estimation-Based Auto Drift Correction in Wireless Sensor Networks
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.
Fast linear stationary methods for automatically biased support vector machines
Publisher: IEEE
Estimating Generalized Dunn's Cluster Validity Indices for Big Data
Publisher: IEEE
Date: 10-2018
Multi-Scale Kernel Methods for Classification
Publisher: IEEE
Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals
Publisher: Springer Science and Business Media LLC
Date: 22-12-2012
DOI: 10.1007/S10877-011-9323-Z
Abstract: Obstructive sleep apnea (OSA) causes a pause in airflow with reduced breathing effort. In contrast, central sleep apnea (CSA) event is not accompanied with breathing effort. The aim of this study is to differentiate CSA and OSA events using wavelet packet analysis and support vector machines of ECG signals over 5 s period. Eight level wavelet packet analysis was performed on each 5 s clip using Daubechies (DB3) mother wavelet and for comparison discrete wavelet analysis was performed using Symlet (SYM3) wavelets. The choice of wavelet basis function was based on a grid search using Daubechies, Symlet and biorthogonal wavelets with decomposition levels varying between 2 and 5. Support vector machine is used for two-class classification. Out of 29 overnight polysomnographic studies, 23 of them were used in the training phase and 6 patients were used for independent testing. The proposed algorithm is shown to perform better in classifying CSA and OSA with wavelet packet features (accuracy-91%, sensitivity-88.14% and specificity-91.11%) than with the traditional wavelet decomposition based features (accuracy-83.79%, sensitivity-89.18% and specificity-83.59%). The independent test resulted in overall classification accuracy, sensitivity and specificity of 91.08, 91.02 and 91.09% respectively using wavelet packet analysis. The classification result indicates the possibility of non-invasively classifying CSA and OSA events based on shorter segments of ECG signals.
A fuzzy neural network based on hierarchical space partitioning
Publisher: IEEE
Date: 1997
Disturbance Rejection In Adaptive Control Of Industrial Processes
Publisher: IEEE
Cluster-Based Crowd Movement Behavior Detection
Publisher: IEEE
Date: 12-2018
Entropy Profiling to Detect ST Change in Heart Rate Variability Signals
Publisher: IEEE
Date: 07-2019
Security Games for Risk Minimization in Automatic Generation Control
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Personalized Anatomic Modeling for Noninvasive Fetal ECG: Methodology and Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
KALwEN: a new practical and interoperable key management scheme for body sensor networks
Publisher: Wiley
Date: 03-12-2010
DOI: 10.1002/SEC.256
Internet of Things (IoT): A vision, architectural elements, and future directions
Publisher: Elsevier BV
Date: 09-2013
Arytenoid cartilage feature point detection using laryngeal 3D CT images in Parkinson's disease
Publisher: IEEE
Date: 07-2017
Detection of fetal arrhythmias in non-invasive fetal ECG recordings using data-driven entropy profiling
Publisher: IOP Publishing
Date: 28-02-2022
Abstract: Objective. Fetal arrhythmias are a life-threatening disorder occurring in up to 2% of pregnancies. If identified, many fetal arrhythmias can be effectively treated using anti-arrhythmic therapies. In this paper, we present a novel method of detecting fetal arrhythmias in short length non-invasive fetal electrocardiography (NI-FECG) recordings. Approach. Our method consists of extracting a fetal heart rate time series from each NI-FECG recording and computing an entropy profile using a data-driven range of the entropy tolerance parameter r . To validate our approach, we apply our entropy profiling method to a large clinical data set of 318 NI-FECG recordings. Main Results. We demonstrate that our method ( TotalS En ) provides strong performance for classifying arrhythmic fetuses (AUC of 0.83) and outperforms entropy measures such as S En (AUC of 0.68) and FuzzyEn (AUC of 0.72). We also find that NI-FECG recordings incorrectly classified using the investigated entropy measures have significantly lower signal quality, and that excluding recordings of low signal quality (13.5% of recordings) increases the classification performance of TotalS En (AUC of 0.90). Significance. The superior performance of our approach enables automated detection of fetal arrhythmias and warrants further investigation in a prospective clinical trial.
An adaptive tracking controller using neural networks for nonlinear systems
Publisher: IEEE
Modified thresholding technique of MMSPCA for extracting respiratory activity from short length PPG signal
Publisher: IEEE
Date: 07-2017
High-Resolution Monitoring of Atmospheric Pollutants Using a System of Low-Cost Sensors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
An Information Framework for Creating a Smart City Through Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Tracking improvement for stable robot control using neural networks
Publisher: IEEE
Date: 1995
Information constrained and finite-time distributed optimisation algorithms
Publisher: IEEE
Date: 12-2017
Onset Detection of Epileptic Seizures From Accelerometry Signal
Publisher: IEEE
Date: 07-2018
Missing Data Imputation With Bayesian Maximum Entropy for Internet of Things Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2021
Privacy-Preserving Collaborative Deep Learning with Application to Human Activity Recognition
Publisher: ACM
Date: 06-11-2017
A robust algorithm for foreground extraction in crowded scenes
Publisher: IEEE
Date: 10-2012
Automated Scoring of Hemiparesis in Acute Stroke From Measures of Upper Limb Co-Ordination Using Wearable Accelerometry
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2020
Assessment of fetal development using cardiac valve intervals
Publisher: Frontiers Media SA
Date: 17-05-2017
Internet of Things for Structural Health Monitoring
Publisher: CRC Press
Date: 15-09-2017
A robust tracking controller for robotic manipulators
Publisher: IEEE
Jeeva: Enterprise Grid Enabled Web Portal for Protein Secondary Structure Prediction
Publisher: IEEE
Date: 12-2008
A visual-numeric approach to clustering and anomaly detection for trajectory data
Publisher: Springer Science and Business Media LLC
Date: 10-12-2015
A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Quantitative Poincaré Plot
Publisher: Springer US
Date: 2013
Non-protruding hazard detection for the aged vision-impaired
Publisher: IEEE
Date: 04-2016
Bayesian maximum entropy and interacting multiple model based automatic sensor drift detection and correction in an IoT environment
Publisher: IEEE
Date: 02-2018
Automated Estimation of Fetal Cardiac Timing Events From Doppler Ultrasound Signal Using Hybrid Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Crowd Event Detection on Optical Flow Manifolds
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Reflections on a process for developing public trust in energy technologies: Follow-up results of the Australian large group process
Publisher: Elsevier BV
Date: 2011
Learning Efficiently- The Deep CNNs-Tree Network
Publisher: IEEE
Date: 11-2015
Multisubject fMRI data analysis via two dimensional multi-set canonical correlation analysis
Publisher: IEEE
Date: 04-2017
A study of biorthogonal wavelets in digital watermarking
Publisher: IEEE
Some issues with the intelligent control of industrial processes
Publisher: IEEE
Motor recovery monitoring using acceleration measurements in post acute stroke patients
Publisher: Springer Science and Business Media LLC
Date: 16-04-2013
Abstract: Stroke is one of the major causes of morbidity and mortality. Its recovery and treatment depends on close clinical monitoring by a clinician especially during the first few hours after the onset of stroke. Patients who do not exhibit early motor recovery post thrombolysis may benefit from more aggressive treatment. A novel approach for monitoring stroke during the first few hours after the onset of stroke using a wireless accelerometer based motor activity monitoring system is developed. It monitors the motor activity by measuring the acceleration of the arms in three axes. In the presented proof of concept study, the measured acceleration data is transferred wirelessly using iMote2 platform to the base station that is equipped with an online algorithm capable of calculating an index equivalent to the National Institute of Health Stroke Score (NIHSS) motor index. The system is developed by collecting data from 15 patients. We have successfully demonstrated an end-to-end stroke monitoring system reporting an accuracy of calculating stroke index of more than 80%, highest Cohen’s overall agreement of 0.91 (with excellent κ coefficient of 0.76). A wireless accelerometer based ‘hot stroke’ monitoring system is developed to monitor the motor recovery in acute-stroke patients. It has been shown to monitor stroke patients continuously, which has not been possible so far with high reliability.
Understanding Irregularity Characteristics of Short-Term HRV Signals Using Sample Entropy Profile
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
A stable adaptive implementation of the internal model principle
Publisher: IEEE
Date: 1990
PPG Derived Heart Rate Estimation During Intensive Physical Exercise
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Kernel Methods in Protein Structure Prediction
Publisher: Wiley
Date: 21-04-2008
Secure k-Connectivity Properties of Wireless Sensor Networks
Publisher: IEEE
Date: 10-2007
A new implementation technique for fast spectral based document retrieval systems
Publisher: IEEE Comput. Soc
Dynamic Channel Assignment Schemes Using Neural Networks
Publisher: CRC Press
Date: 15-09-2010
Head detection using motion features and multi level pyramid architecture
Publisher: Elsevier BV
Date: 08-2015
Universal neural network controllers
Publisher: IEEE
Date: 1997
Insider DoS Attacks on Epidemic Propagation Strategies of Network Reprogramming in Wireless Sensor Networks
Publisher: IEEE
Date: 2009
DOI: 10.1109/IAS.2009.215
PPFA: Privacy Preserving Fog-Enabled Aggregation in Smart Grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2018
Analysis of fetal heart rate asymmetry before and after 35 weeks of gestation
Publisher: Elsevier BV
Date: 08-2015
Convergence of self-organizing nets with high dimensional neighborhood relation
Publisher: IEEE
Network Resource Allocation for Industry 4.0 with Delay and Safety Constraints
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2016
Stability of input amplitude constrained adaptive pole placement control systems
Publisher: Elsevier BV
Date: 06-1994
WAKE: Key management scheme for wide-area measurement systems in smart grid
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2013
Teaching multivariable control with CADCS packages and PCs
Publisher: Elsevier BV
Date: 1990
Performance evaluation of a RISC neuro-processor for neural networks
Publisher: IEEE Comput. Soc. Press
Biomedical Signal Processing
Publisher: CRC Press
Date: 04-12-2009
Automatic segmentation of the rima glottidis in 4D laryngeal CT scans in Parkinson's disease
Publisher: IEEE
Date: 08-2015
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