Electrical and Electronic Engineering - Research Publications
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ItemAutomatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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ItemCrowd Event Detection on Optical Flow Manifolds(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2016-07)Analyzing crowd events in a video is key to understanding the behavioral characteristics of people (humans). Detecting crowd events in videos is challenging because of articulated human movements and occlusions. The aim of this paper is to detect the events in a probabilistic framework for automatically interpreting the visual crowd behavior. In this paper, crowd event detection and classification in optical flow manifolds (OFMs) are addressed. A new algorithm to detect walking and running events has been proposed, which uses optical flow vector lengths in OFMs. Furthermore, a new algorithm to detect merging and splitting events has been proposed, which uses Riemannian connections in the optical flow bundle (OFB). The longest vector from the OFB provides a key feature for distinguishing walking and running events. Using a Riemannian connection, the optical flow vectors are parallel transported to localize the crowd groups. The geodesic lengths among the groups provide a criterion for merging and splitting events. Dispersion and evacuation events are jointly modeled from the walking/running and merging/splitting events. Our results show that the proposed approach delivers a comparable model to detect crowd events. Using the performance evaluation of tracking and surveillance 2009 dataset, the proposed method is shown to produce the best results in merging, splitting, and dispersion events, and comparable results in walking, running, and evacuation events when compared with other methods.
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ItemReal-Time Urban Microclimate Analysis Using Internet of Things(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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ItemEstimation of crowd density by clustering motion cues(SPRINGER, 2015-11)
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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.