Computer Science and Engineering 2017-10-20T06:53:06Z Eye movement as a predictor of cognitive ability Eye movement as a predictor of cognitive ability Patel, Valay Paresh With aging population, neurodegenerative disorder is becoming a public health issue. Given the adverse effects of such disorder, tools for early screening become very critical. Early screening may help to detect such disorder at a nascent stage that in turn can be clinically addressed thereby delaying its progression. With technological progress, brain scanning can help to identify such disorders. However, access to such tools are restricted due to high cost and necessity of technical skills to operate that are limited to specialized healthcare centers. Tracking of one�s eye movement in response to a visual task can be a cost-effective alternative in developing such a screening platform. Motivated by this, in this research study, we have designed computer-based gaze-sensitive system (MindEye) that exposed participants to various visual tasks. Our system captured one�s eye movement data to quantify gaze in terms of reaction time (i.e., the degree of promptness) while shifting one�s gaze to the desired target point and number of trials with fixation in correct sequence (i.e., the ability to remember and recall) in response to visual targets presented systematically at different regions of a computer monitor. We have carried out two experimental studies. First study involving 10 healthy young individuals categorized into two groups based on their age indicated that one�s gaze related indices measured by MindEye is sensitive to the variation in age. The second study involving 10 elderly individuals were again categorized into two groups based on their evaluation by standard paper-and-pencil tests. The results indicate that the variation of the gaze-related indices measured by MindEye become more prominent in the elderly group than that of the younger groups in Study 1. Additionally, the preliminary results of Study 2 are promising in terms of the feasibility of one�s gaze-related indices measured by MindEye as potential biomarkers of one�s cognitive decline. We hope that the cost-effective and user-friendly MindEye screening platform holds promise for use as a complementary tool in the hands of the clinicians. Thus, the usage of MindEye can be a step towards bringing about a paradigm shift even in rural healthcare. 2017-01-01T00:00:00Z Distributed estimation in sparse heterogeneous networks Distributed estimation in sparse heterogeneous networks Kumar, Kalyankar Shravan Advances in wireless communication and adaptive signal processing has resulted in increased interest in multidimensional sensor systems. The most popular form of multidimensional sensor system is the wireless sensor network (WSN), which is essentially a group of distributed sensors fully or partially connected wirelessly. The distributed nature of the sensor network has opened up the area of distributed estimation, which is a special case of signal or parameter estimation. In order to achieve e ective parameter estimation in scenarios where the parameter to be estimated is sparse in nature, a new sparse distributed estimation scheme has been designed in this thesis. The convergence behaviour of the proposed estima- tion approach has been further enhanced using a set of algorithms. A dynamic heterogeneous network estimation scheme has been also designed to reduce the computational overhead. An attempt has been made to design and implement a distributed speech estimation scheme in real time and promising results have been obtained. 2017-01-01T00:00:00Z Visual object perception problems in computer vision Visual object perception problems in computer vision Vora, Aditya Narendrabhai Video object segmentation is the task of estimating foreground object segments from the background throughout video. We propose a frame-by-frame approach for video object segmentation that uses cluster information in order to select foreground segments. Unlike previous approaches for video object segmentation that makes use of optical flow in order to localize dynamic object segments throughout the video, we rather focus on selecting a set of foreground segments from a pool of region proposals through clustering, which helps to avoid making use of optical flow and thus help our algorithm to scale-up to longer video sequences. Object localization is the task of estimating precise localized windows around all object instances in the image. We proposed an algorithm for object localization given that single object instance appears in the image. Unlike previous supervised and weakly supervised techniques that require heavy training in order to learn classifiers, our approach is completely unsupervised. Our approach depends on iterative spectral clustering in order select proposals that contain an object from a huge set of proposals generated from an object proposal generation algorithm. From these set of filtered object proposals, we then estimate the final localized window by considering the inter and intra class variations among the object proposals, thus making the entire algorithm completely unsupervised. We consider designing a fully automated action recognition system under uncontrolled environments. Most existing algorithms rely on constructing handcrafted features from the input and then learn classifiers based on the designed features. However, these hand-crafted features are inefficient in modelling more complex scenes. CNN are a class of deep learning models that can learn features automatically from the input during the training process. We design a 3D convolutional neural network for human action recognition. This model is able to extract features in spatio-temporal domain, thereby able to capture the motion information encoded in multiple contiguous frames required for all video processing applications. 2017-01-01T00:00:00Z An inhomogeneous Jacobi equation for minimal surfaces and a perturbative change of Holographic Entanglement Entropy An inhomogeneous Jacobi equation for minimal surfaces and a perturbative change of Holographic Entanglement Entropy Ghosh, Avirup; Mishra, Rohit The change in Holographic entanglement entropy (HEE) for small fluctuations about pure AdS are obtained by a perturbative expansion of the area functional in terms of the change in the bulk metric and the embedded extremal surface. However, it is known that change in the embedding appears at second order or higher. It was shown that these changes in the embedding can be calculated in the 2+1 dimensional case by solving a 'generalized geodesic deviation equation'. We generalize this result to arbitrary dimensions by deriving an inhomogeneous form of the Jacobi equation for minimal surfaces. The solutions of this equation map a minimal surface in a given space-time to a minimal surface in a space-time which is a perturbation over the initial space-time. Using this we perturbatively calculate the changes in HEE up to second order for the boosted black brane like perturbations over AdS4 . 2017-10-01T00:00:00Z