N
Nadia Kanwal
Researcher at Athlone Institute of Technology
Publications - 49
Citations - 794
Nadia Kanwal is an academic researcher from Athlone Institute of Technology. The author has contributed to research in topics: Augmented reality & Computer science. The author has an hindex of 11, co-authored 46 publications receiving 462 citations. Previous affiliations of Nadia Kanwal include Lahore College for Women University & University of Essex.
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A Survey of Modern Deep Learning based Object Detection Models
Syed Sahil Abbas Zaidi,Mohammad Samar Ansari,Asra Aslam,Nadia Kanwal,Mamoona Naveed Asghar,Brian Lee +5 more
TL;DR: In this article, a survey of recent developments in deep learning based object detectors is presented along with some of the prominent backbone architectures used in recognition tasks and compared the performances of these architectures on multiple metrics.
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Seizure detection from EEG signals using Multivariate Empirical Mode Decomposition.
TL;DR: A data driven approach to classify ictal and non-ictal EEG signals using the multivariate empirical mode decomposition (MEMD) algorithm and selects suitable feature sets based on the multiscale T-F representation of the EEG data via MEMD for the classification purposes.
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Augmented reality applications for cultural heritage using Kinect
TL;DR: It is shown that the combination of depth and image correspondences from the Kinect can yield a reliable estimate of the location and pose of the camera, though noise from the depth sensor introduces an unpleasant jittering of the rendered view.
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A Navigation System for the Visually Impaired: A Fusion of Vision and Depth Sensor
TL;DR: A complete navigation system based on low cost and physically unobtrusive sensors such as a camera and an infrared sensor based around corners and depth values from Kinect's infrared sensor that operates adequately by both blindfolded and blind people.
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A novel system for spatial and temporal imaging of intrinsic plant water use efficiency
TL;DR: Applications of this system will augment the research community’s need for novel screening methods to identify rapidly novel lines, cultivars, or species with improved A and WUEi in order to meet the current demands on modern agriculture and food production.