Electrical and Electronic Engineering - Research Publications

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    An efficient deep neural model for detecting crowd anomalies in videos
    Yang, M ; Tian, S ; Rao, AS ; Rajasegarar, S ; Palaniswami, M ; Zhou, Z (Springer, 2023-06-01)
    Identifying unusual crowd events is highly challenging, laborious, and prone to errors in video surveillance applications. We propose a novel end-to-end deep learning architecture called Stacked Denoising Auto-Encoder (DeepSDAE) to address these challenges, comprising SDAE, VGG16 and Plane-based one-class Support Vector Machine (SVM), abbreviated as PSVM, to detect anomalies such as stationary people in an active scene or loitering activities in a crowded scene. The DeepSDAE framework is a hybrid deep learning architecture. It consists of a four-layered SDAE and an enhanced convolutional neural network (CNN) model. Our framework employs Reinforcement Learning to optimise the learning parameters to detect crowd anomalies. We use the Markov Decision Process (MDP) with Deep Q-learning to find the optimal Q value. We also present a late fusion procedure to combine individual decisions from the intermediate and final layers of the SDAE and VGG16 networks to detect different anomalies. Our experiments on four real-world datasets reveal the superior performance of our proposed framework in detecting (frame-level and pixel-level) anomalies.
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    Real-Time Urban Microclimate Analysis Using Internet of Things
    Rathore, P ; Rao, AS ; Rajasegarar, S ; Vanz, E ; Gubbi, J ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018-04)
    Real-time environment monitoring and analysis is an important research area of Internet of Things (IoT). Understanding the behavior of the complex ecosystem requires analysis of detailed observations of an environment over a range of different conditions. One such example in urban areas includes the study of tree canopy cover over the microclimate environment using heterogeneous sensor data. There are several challenges that need to be addressed, such as obtaining reliable and detailed observations over monitoring area, detecting unusual events from data, and visualizing events in real-time in a way that is easily understandable by the end users (e.g., city councils). In this regard, we propose an integrated geovisualization framework, built for real-time wireless sensor network data on the synergy of computational intelligence and visual methods, to analyze complex patterns of urban microclimate. A Bayesian maximum entropy-based method and a hyperellipsoidal model-based algorithm have been build in our integrated framework to address above challenges. The proposed integrated framework was verified using the dataset from an indoor and two outdoor network of IoT devices deployed at two strategically selected locations in Melbourne, Australia. The data from these deployments are used for evaluation and demonstration of these components' functionality along with the designed interactive visualization components.
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    A visual-numeric approach to clustering and anomaly detection for trajectory data
    Kumar, D ; Bezdek, JC ; Rajasegarar, S ; Leckie, C ; Palaniswami, M (SPRINGER, 2017-03)
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    Elliptical Anomalies in Wireless Sensor Networks
    Rajasegarar, S ; Bezdek, JC ; Leckie, C ; Palaniswami, M (ASSOC COMPUTING MACHINERY, 2009-12)
    Anomalies in wireless sensor networks can occur due to malicious attacks, faulty sensors, changes in the observed external phenomena, or errors in communication. Defining and detecting these interesting events in energy-constrained situations is an important task in managing these types of networks. A key challenge is how to detect anomalies with few false alarms while preserving the limited energy in the network. In this article, we define different types of anomalies that occur in wireless sensor networks and provide formal models for them. We illustrate the model using statistical parameters on a dataset gathered from a real wireless sensor network deployment at the Intel Berkeley Research Laboratory. Our experiments with a novel distributed anomaly detection algorithm show that it can detect elliptical anomalies with exactly the same accuracy as that of a centralized scheme, while achieving a significant reduction in energy consumption in the network. Finally, we demonstrate that our model compares favorably to four other well-known schemes on four datasets.
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    Anomaly detection in wireless sensor networks
    Rajasegarar, S ; Leckie, C ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2008-08)
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    Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks
    Rajasegarar, S ; Leckie, C ; Bezdek, JC ; Palaniswami, M (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2010-09)