Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Sub-pixel mineral mapping using EO-1 hyperion hyperspectral data(International Society for Photogrammetry and Remote Sensing, 2014) Kumar, C.; Shetty, A.; Raval, S.; Champatiray, P.K.; Sharma, R.This study describes the utility of Earth Observation (EO)-1 Hyperion data for sub-pixel mineral investigation using Mixture Tuned Target Constrained Interference Minimized Filter (MTTCIMF) algorithm in hostile mountainous terrain of Rajsamand district of Rajasthan, which hosts economic mineralization such as lead, zinc, and copper etc. The study encompasses pre-processing, data reduction, Pixel Purity Index (PPI) and endmember extraction from reflectance image of surface minerals such as illite, montmorillonite, phlogopite, dolomite and chlorite. These endmembers were then assessed with USGS mineral spectral library and lab spectra of rock samples collected from field for spectral inspection. Subsequently, MTTCIMF algorithm was implemented on processed image to obtain mineral distribution map of each detected mineral. A virtual verification method has been adopted to evaluate the classified image, which uses directly image information to evaluate the result and confirm the overall accuracy and kappa coefficient of 68% and 0.6 respectively. The sub-pixel level mineral information with reasonable accuracy could be a valuable guide to geological and exploration community for expensive ground and/or lab experiments to discover economic deposits. Thus, the study demonstrates the feasibility of Hyperion data for sub-pixel mineral mapping using MTTCIMF algorithm with cost and time effective approach.Item Audio songs classification based on music patterns(Springer Verlag service@springer.de, 2016) Sharma, R.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.In this work, effort has been made to classify audio songs based on their music pattern which helps us to retrieve the music clips based on listener’s taste. This task is helpful in indexing and accessing the music clip based on listener’s state. Seven main categories are considered for this work such as devotional, energetic, folk, happy, pleasant, sad and, sleepy. Forty music clips of each category for training phase and fifteen clips of each category for testing phase are considered; vibrato-related features such as jitter and shimmer along with the mel-frequency cepstral coefficients (MFCCs); statistical values of pitch such as min, max, mean, and standard deviation are computed and added to the MFCCs, jitter, and shimmer which results in a 19-dimensional feature vector. feedforward backpropagation neural network (BPNN) is used as a classifier due to its efficiency in mapping the nonlinear relations. The accuracy of 82% is achieved on an average for 105 testing clips. © Springer India 2016.Item Employing Differentiable Neural Computers for Image Captioning and Neural Machine Translation(Elsevier B.V., 2020) Sharma, R.; Kumar, A.; Meena, D.; Pushp, S.In the history of artificial neural networks, LSTMs have proved to be a high-performance architecture at sequential data learning. Although LSTMs are remarkable in learning sequential data but are limited in their ability to learn long-term dependencies and representation of certain data structures because of the lack of external memory. In this paper, we tackled two main tasks, one is language translation and other is image captioning. We approached the problem of language translation by leveraging the capabilities of the recently developed DNC architectures. Here we modified the DNC architecture by including dual neural controllers instead of one and an external memory module. Inside our controller, we employed a neural network with memory-augmentation which differs from the original differentiable neural computer, we implemented a dual controller's system in which one controller is for encoding the query sequence whereas another controller is for decoding the translated sequences. During the encoding cycle, new inputs are read and the memory is updated accordingly. In the decoding cycle, the memory is protected from any writing from the decoding controller. Thus, the decoder phase generates a translated sequence at a time step. Therefore, the proposed dual controller neural network with memory-augmentation is then trained and tested on the Europarl dataset. For the image captioning task, our architecture is inspired by an end-to-end image captioning model where CNN's output is passed to RNN as input only once and the RNN generates words depending on the input. We trained our DNC captioning model on 2015 MSCOCO dataset. In the end, we compared and shows the superiority of our architecture as compared to conventionally used LSTM and NTM architectures. © 2020 The Authors. Published by Elsevier B.V.Item Vocal Tract Articulatory Contour Detection in Real-Time Magnetic Resonance Images Using Spatio-Temporal Context(Institute of Electrical and Electronics Engineers Inc., 2020) Hebbar, S.A.; Sharma, R.; Somandepalli, K.; Toutios, A.; Narayanan, S.Due to its ability to visualize and measure the dynamics of vocal tract shaping during speech production, real-time magnetic resonance imaging (rtMRI) has emerged as one of the prominent research tools. The ability to track different articulators such as the tongue, lips, velum, and the pharynx is a crucial step toward automating further scientific and clinical analysis. Recently, various researchers have addressed the problem of detecting articulatory boundaries, but those are primarily limited to static-image based methods. In this work, we propose to use information from temporal dynamics together with the spatial structure to detect the articulatory boundaries in rtMRI videos. We train a convolutional LSTM network to detect and label the articulatory contours. We compare the produced contours against reference labels generated by iteratively fitting a manually created subject-specific template. We observe that the proposed method outperforms solely image-based methods, especially for the difficult-to-track articulators involved in airway constriction formation during speech. © 2020 IEEE.Item Automatizing the Khasra Maps Generation Process Using Open Source Software: QGIS and Python Coding Language(Springer Science and Business Media Deutschland GmbH, 2022) Sharma, R.; Beg, M.K.; Bhojaraja, B.E.; Umesh, P.Humans are trying to acquire a piece of land from the time they have come into existence. In modern era, the management of land and its ownership is taken up by the Land and Revenue Department of the State. In order to do that, they need maps with specific objectives, so that even a laymen can understand and use it. The process explained in this paper automate the process of map making after getting the digitized shapefile of the khasra (property identification number), as a single village is divided into numerous grids and it is a tedious work and can have lots of errors while doing it manually. So in order to do the process in swift manner and without having any errors, the process was developed using the Quantum Geographic Information System (QGIS) and Python. The proposed method involves making the use of models built in QGIS along with the Python console. It helps to run the whole process on its own with taking the required input parameters and storing the outputs in a specific folder designed for them. The requirement of the project was to do the same operations on a village file and to get the final khasra map from the village polygon file. Depending upon the village area and its dimensions, the numbers of grids for a particular village is decided and the same GIS tools need to be run on each grid files which make this process a tedious work and more prone to errors. By making use of the method suggested in the paper, all the work can be done error proof with the use of Python. The use of Python code helps to do work in just couple of seconds which would have taken days to complete. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Visualisation and Assessment of Seasonal Variations in Bus Passenger Mobility Pattern(Springer Science and Business Media Deutschland GmbH, 2024) Nithin, K.S.; Mulangi, R.H.; Sharma, R.; Baishya, H.; Panth, P.; Mohtashim, M.D.Passenger mobility pattern is an essential characteristic in designing, managing and operating the public transit system. It depicts how passenger behaviour responds to changes in spatial and temporal attributes. In the past, studies have done the spatiotemporal analysis of hourly and daily variations but the effect of seasonal variation on the passenger mobility pattern has been neglected which causes inadequate planning and has led to an inefficient transit system. In the present study, non-negative tensor decomposition (NTD) is used to carry out spatiotemporal analysis of bus passengers by considering seasonal variation in passenger mobility. Six months of electronic ticketing machine (ETM) data of the intra-city bus service of Davangere is used. From the analysis, it is observed that people coming from outside, majorly from suburban and village areas used public transit more during the wet season compared to dry months. It portrays that people are sensitive to weather conditions and tend to shift from private vehicles to public transit and vice-versa. Hence, this methodology helps transit planners in the managerial aspect through rescheduling services and frequency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.