Electrical and Electronic Engineering - Research Publications
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ItemA robust algorithm for foreground extraction in crowded scenes(IEEE, 2012-12-01)The widespread availability of surveillance cameras and digital technology has improved video based security measures in public places. Surveillance systems have been assisting officials both in civil and military applications. It is helping to identify unlawful activities by means of uninterrupted transmission of surveillance videos. By this, the system is adding extraneous onus on to the already existing workload of security officers. Instead, if the surveillance system is intelligent and efficient enough to identify the events of interest and alert the officers, it alleviates the burden of continuous monitoring. In other words, our existing surveillance systems are lacking to identify the objects that are dissimilar in shape, size, and color especially in identifying human beings (nonrigid motions). Global illumination changes, frequent occurrences of shadows, insufficient lighting conditions, unique properties of slow and fast moving objects, unforeseen appearance of objects and its behavior, availability of system memory, etc., may be ascribed to the limitations of existing systems. In this paper, we present a filtering technique to extract foreground information, which uses RGB component and chrominance channels to neutralize the effects of nonuniform illumination, remove shadows, and detect both slow-moving and distant objects.
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ItemA Pilot Study of Urban Noise Monitoring Architecture Using Wireless Sensor Networks(IEEE, 2013-01-01)Internet of Things (IoT) is denned as interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. As the world urban population is set to cross unprecedented levels, adequate provision of services and infrastructure poses huge challenges. The emerging IoT that offers ubiquitous sensing and actuation can be utilized effectively for managing urban environments. In this paper, a new architecture for noise monitoring in urban environments is proposed. The architecture is scalable and applicable to other sensors required for city management. In addition to the architecture, a new noise monitoring hardware platform is reported and visualization of the data is presented. An emerging citizen centric participatory sensing is discussed in the context of noise monitoring.
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ItemA Pilot Study on the use of Accelerometer Sensors for Monitoring Post Acute Stroke Patients(IEEE, 2013)The high incidence of stroke has raised a major concern among health professionals in recent years. Concerted efforts from medical and engineering communities are being exercised to tackle the problem at its early stage. In this direction, a pilot study to analyze and detect the affected arm of the stroke patient based on hand movements is presented. The premise is that the correlation of magnitude of the activities of the two arms vary significantly for stroke patients from controls. Further, the cross-correlation of right and left arms for three axes are differentiable for patients and controls. A total of 22 subjects (15 patients and 7 controls) were included in this study. An overall accuracy of 95.45% was obtained with sensitivity of 1 and specificity of 0.86 using correlation based method.
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ItemCrowd Density Estimation Based on Optical Flow and Hierarchical Clustering(IEEE, 2013-01-01)Crowd density estimation has gained much attention from researchers recently due to availability of low cost cameras and communication bandwidth. In video surveillance applications, counting people and creating a temporal profile is of high interest. Surveillance systems face difficulties in detecting motion from the scene due to varying environmental conditions and occlusion. Instead of detecting and tracking individual person, density estimation is an approximate method to count people. The approximation is often more accurate than individual tracking in occluded scenarios. In this work, a new technique to estimate crowd density is proposed. A block-based dense optical flow with spatial and temporal filtering is used to obtain velocities in order to infer the locations of objects in crowded scenarios. Furthermore, a hierarchical clustering is employed to cluster the objects based on Euclidean distance metric. The Cophenetic correlation coefficient for the clusters highlighted the fact that our preprocessing and localizing of object movements form hierarchical clusters that are structured well with reasonable accuracy without temporal post-processing.
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ItemDetermination of Object Directions Using Optical Flow for Crowd Monitoring(SPRINGER-VERLAG BERLIN, 2013-01-01)Determination of object direction in a multi-camera tracking system is critical. The absence of object direction from other cameras pose challenges if the object is along the optical axis. The problem of determining object direction worsens further if the cameras in the existing infrastructure are improperly placed and are uncontrollable. To determine the direction of an object in such situations, three methods based on optical flow (OF) are presented. The first method uses centroids of optical flow vector magnitudes and Kalman filter for tracking and is suitable for less crowded scenarios. The second method uses geometric moments to evaluate the flow vector distribution and to ascertain the direction in case of crowded scenarios by partitioning the scene and then applying moments to individual partitions independently. The third method is appropriate for small-sized objects near vanishing points where global object motion is less. During surveillance, whether multi-object, single-object or crowded scenarios, the aforementioned methods are applicable accordingly. The results show that the object directions can be accurately inferred from three methods for different scenarios.
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ItemComplex Correlation Measure: a novel descriptor for Poincare plot(BMC, 2009-08-13)BACKGROUND: Poincaré plot is one of the important techniques used for visually representing the heart rate variability. It is valuable due to its ability to display nonlinear aspects of the data sequence. However, the problem lies in capturing temporal information of the plot quantitatively. The standard descriptors used in quantifying the Poincaré plot (SD1, SD2) measure the gross variability of the time series data. Determination of advanced methods for capturing temporal properties pose a significant challenge. In this paper, we propose a novel descriptor "Complex Correlation Measure (CCM)" to quantify the temporal aspect of the Poincaré plot. In contrast to SD1 and SD2, the CCM incorporates point-to-point variation of the signal. METHODS: First, we have derived expressions for CCM. Then the sensitivity of descriptors has been shown by measuring all descriptors before and after surrogation of the signal. For each case study, lag-1 Poincaré plots were constructed for three groups of subjects (Arrhythmia, Congestive Heart Failure (CHF) and those with Normal Sinus Rhythm (NSR)), and the new measure CCM was computed along with SD1 and SD2. ANOVA analysis distribution was used to define the level of significance of mean and variance of SD1, SD2 and CCM for different groups of subjects. RESULTS: CCM is defined based on the autocorrelation at different lags of the time series, hence giving an in depth measurement of the correlation structure of the Poincaré plot. A surrogate analysis was performed, and the sensitivity of the proposed descriptor was found to be higher as compared to the standard descriptors. Two case studies were conducted for recognizing arrhythmia and congestive heart failure (CHF) subjects from those with NSR, using the Physionet database and demonstrated the usefulness of the proposed descriptors in biomedical applications. CCM was found to be a more significant (p = 6.28E-18) parameter than SD1 and SD2 in discriminating arrhythmia from NSR subjects. In case of assessing CHF subjects also against NSR, CCM was again found to be the most significant (p = 9.07E-14). CONCLUSION: Hence, CCM can be used as an additional Poincaré plot descriptor to detect pathology.
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ItemJeeva: Enterprise Grid Enabled Web Portal for Protein Secondary Structure Prediction(IEEE, 2008)This paper presents a Grid portal for protein secondary structure prediction developed by using services of Aneka, a .NET-based enterprise Grid technology. The portal is used by research scientists to discover new prediction structures in a parallel manner. An SVM (Support Vector Machine)-based prediction algorithm is used with 64 sample protein sequences as a case study to demonstrate the potential of enterprise Grids.
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ItemInternet of Things (IoT): A vision, architectural elements, and future directions(ELSEVIER, 2013-09)Ubiquitous sensing enabled by Wireless Sensor Network (WSN) technologies cuts across many areas of modern day living. This offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The proliferation of these devices in a communicating-actuating network creates the Internet of Things (IoT), wherein, sensors and actuators blend seamlessly with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). Fuelled by the recent adaptation of a variety of enabling device technologies such as RFID tags and readers, near field communication (NFC) devices and embedded sensor and actuator nodes, the IoT has stepped out of its infancy and is the the next revolutionary technology in transforming the Internet into a fully integrated Future Internet. As we move from www (static pages web) to web2 (social networking web) to web3 (ubiquitous computing web), the need for data-on-demand using sophisticated intuitive queries increases significantly. This paper presents a cloud centric vision for worldwide implementation of Internet of Things. The key enabling technologies and application domains that are likely to drive IoT research in the near future are discussed. A cloud implementation using Aneka, which is based on interaction of private and public clouds is presented. We conclude our IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
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ItemProtein topology classification using two-stage support vector machines.(Universal Academy Press, 2006)The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (alpha, beta, alpha+beta) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from http://www.ee.unimelb.edu.au/ISSNIP/downloads/.