BITS Faculty Publications
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Item Studying the role of kinect as a multi-sensory learning platform for children(ACM Digital Library, 2018) Sinha, YashAccording to the theory of Embodied Cognition, our behavior is a result of real-time interaction with surroundings, our cognitive skills, and the nervous system. From this perspective, researchers are considering a learning environment which promotes physical activities to achieve cognitive tasks. Such Natural User Interfaces (NUI) make use of gesture-based sensors like the Microsoft Kinect. Yet we lack in-depth studies of how they improve the learning process. In this paper, we present observations of two deployment studies which focus on different roles that NUI can play as a part of learning activities. We deploy the Kinect based applications:- Yoga Soft: A Digital Yoga Instructor and Mudra: A Kinect based Learning System in real life scenarios. The first study is conducted at residences of preadolescent children in Gurgaon, India. The second study is conducted at an education center specializing in the care of kindergarten children in Pilani, India.Item Lung cancer detection: a deep learning approach(Springer, 2018-10) Sinha, YashWe present an approach to detect lung cancer from CT scans using deep residual learning. We delineate a pipeline of preprocessing techniques to highlight lung regions vulnerable to cancer and extract features using UNet and ResNet models. The feature set is fed into multiple classifiers, viz. XGBoost and Random Forest, and the individual predictions are ensembled to predict the likelihood of a CT scan being cancerous. The accuracy achieved is 84% on LIDC-IRDI outperforming previous attempts.Item Non iterative LDPC decoding by syndrome generation using artificial neural network(IEEE, 2016-07) Phartiyal, Gopal SinghLow density parity-check code (LDPC) is an error correcting code used in noisy communication channel (e.g. AWGN) to reduce the probability of error in information. By using LDPC codes, this probability can be made comparatively small, so that the data transmission rate can be as close to Shannon's limit. The decoding of Low Density Parity Check (LDPC) codes by iterative process of belief propagation gives challenges for designers looking for real time performance in communication systems. This thesis work proposes the use of Artificial Neural Networks (ANN) to replace belief propagation to approach closer to Shannon's limit more closer than other traditional decoding methods. This thesis is intended to design a new methodology to decode LDPC codes in Non-iterative manner with the help of ANN and Look Up Table (LUT). This work is at initial stage and will be extended for better performance.Item A critical analysis of polarimetrie signatures on PALSAR 2 data for land cover classification(IEEE, 2016) Phartiyal, Gopal SinghIn this paper, polarization signatures are extracted for utilization of the fully polarimetrie L-band ALOS-PALSAR 2 data. These signatures are extracted for different land cover classes (i.e., urban, water, short vegetation, tall vegetation and bare soil). Critical analysis is performed on the polarization signatures generated of different classes. Further, polarization signatures of different classes are compared with the help of normalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM). Decision tree based algorithm is developed with the help of NSCM, NED and backscattered image for the classification of different land cover classes.Item Comparative study on deep neural network models for crop classification using time series polsar and optical data(ISPRS, 2018-11) Phartiyal, Gopal SinghCrop classification is an important task in many crop monitoring applications. Satellite remote sensing has provided easy, reliable, and fast approaches to crop classification task. In this study, a comparative analysis is made on the performances of various deep neural network (DNN) models for crop classification task using polarimetric synthetic aperture radar (PolSAR) and optical satellite data. For PolSAR data, Sentinel 1 dual pol SAR data is used. Sentinel 2 multispectral data is used as optical data. Five land cover classes including two crop classes of the season are taken. Time series data over the period of one crop cycle is used. Training and testing samples are measured and collected directly from the ground over the study region. Various convolutional neural network (CNN) and long short-term memory (LSTM) models are implemented, analysed, evaluated, and compared. Models are evaluated on the basis of classification accuracy and generalization performance.Item A mixed spectral and spatial convolutional neural network for land cover classification using SAR and optical data(EGU 2018, 2018) Phartiyal, Gopal SinghToday, both SAR and optical data are available with good spatial and temporal resolutions. The two data modalities complement each other in many applications. There are numerous approaches to process the two data modalities, separately or combined. Domain or modality specific approaches such as polarimetric decomposition techniques or reflectance based techniques cannot work with the two datasets combined together. Data fusion approaches incur information loss during the process and are highly application specific. Machine learning (ML) approaches can operate on the combined dataset but have their own advantages and disadvantages. There is a need to explore new ML based approaches to achieve higher performance. Convolutional neural networks (CNNs) are young, trending, and promising ML tools in remote sensing applications. CNNs have the capability to learn complex features exclusively from data. Data from the two modalities can thus be brought together and processed with increased performance. In this paper an attempt is made to analyze CNN capabilities to perform land cover classification using multi-sensor data. SAR data used in this study is L band fully polarimetric PALSAR 2 data with 6 meter spatial resolution. Three basic polarimetric bands, namely, HH, HV, and VV, and four derived bands (polarization signatures) are used. Six multispectral Landsat 8 bands, pan sharpened and resampled at 6 meter spatial resolution, are used as optical data. All 13 features are stacked together and fed as input data to the proposed CNN. The areas selected for study are Haridwar and Roorkee regions of northern India. This study introduces a CNN where convolution is performed both spatially and spectrally. We show how this is an advantage over performing only spatial convolution. Five land cover classes namely, urban, bare soil, water, dense vegetation, and agriculture are considered. The CNN is trained on more than 1200 ground truth class data points measured directly on the terrain. The classification shows results with good generalization. Comparison with other classifiers such as SVMs shows that the proposed approach provides better classification results in terms of generalization, although the cross-validation accuracy is on the same order. The evaluation of the generalization of the classified image is done using ground truth knowledge on selected subset areas in the study area.Item Improved utilization of polsar polarization signatures using convolutional-deep neural nets for land cover classification(IEEE, 2019-11) Phartiyal, Gopal SinghNormalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM) are most popularly used pattern classifiers with polarization signatures (PSs) based polarimetric synthetic aperture radar (PolSAR) data applications. These methods are not able to fully exploit the PSs as they do not exploit the spatial context or pattern of PSs which is essential. Improved utilization of PSs is still required for PolSAR applications such as agriculture crop classification and monitoring. In this study, convolutional deep neural networks (C-DNNs) are introduced and utilized as pattern classifiers for PS classification. C-DNNs have the ability to consider and control the influence of local neighborhood pixels during classification. Therefore, in this study C-DNNs are utilized to extract and exploit subtle changes between PSs of land covers to improve classification performance. Comparison with NED and NSCM classifiers signify the contribution of C-DNNs by improved performance in PolSAR data classification.Item Learning automata based contention aware data forwarding scheme for safety applications in vehicular Ad Hoc networks(IEEE, 2013-12) Dua, AmitWith an exponential growth of demands of the users to access various resources during mobility lead to the popularity of Vehicular Ad Hoc Networks (VANETs). Users may access various resources from cloud which consists of many resources for the ease of users. VANETs have been used in wide range of applications such as Intelligent Transport Systems (ITS), Safety alarms on roads/in community, online resource access using Internet connectivity etc. Among these applications, safety applications are most important and various proposals exist in literature for the same. But most of the existing proposals have used unicast sender based data forwarding which results an overall performance degradation with respect to the metrics such as packet delivery ratio, end-to-end delay and reliable data transmission. Keeping in view of the above, in this paper, we propose new Learning Automata based Contention Aware Data forwarding scheme for VANETs using cloud infrastructure. Learning Automata (LAs) are assumed to be located in the vehicles which share the information (such as vehicles density, directions of the vehicles or vehicles velocity etc) with the other LAs for taking the adaptive decisions about data forwarding. Based upon these values, automaton performs its action. Corresponding to each action performed by the automaton, its action may be rewarded or penalized by some constant values from the environment where it is working. Based upon the inputs from the environment, each automaton updates its action probability values for the next rounds. An adaptive Learning Automata based Contention Aware Data Forwarding (LACADF) algorithm is also proposed. The proposed scheme is evaluated with respect to different network parameters such as message overhead, throughput, delay etc. with varying density and mobility of the vehicles. The results obtained show that the proposed scheme is better than the other conventional schemes with respect to the above metrics.Item Efficient TDMA based virtual back off algorithm for adaptive data dissemination in VANETs(IEEE, 2014-01) Dua, AmitWith an exponential growth of Internet related technologies, there is an emergence of new class of efficient data dissemination using Vehicular Ad Hoc networks for the safety of peoples. As there are limited number of channels in IEEE 802.11 a/b/g/p standards, so efficient mechanisms are required to allocate the same for new incoming requests from the users. Keeping in view of the same, we propose a modified TDMA based virtual back off algorithm for VANETs. Vehicles are assumed to be arrived using Poisson distribution and served as exponential. By varying the distance from RSUs, we have provided the analysis of end-to end delay for varying speed of vehicles.Item Collaborative P2P context-aware information propagation in vehicular ad hoc networks(IEEE, 2015) Dua, AmitWith exponential growth of the Internet users in past few years, there is a need of context-aware information sharing among different inter-connected entities over the Internet. The prime objective of the inter-connected objects is that within a minimum use of available resources, maximum output with respect to parameters such as throughput and delay can be achieved. But, due to high velocity and irrelevant information propagation, there may be a performance degradation in some part of the network with respect to these parameters. To address these issues, we have designed novel algorithms for context-aware information propagation among the vehicles. The proposed scheme consists of algorithms for data access, data dissemination, and data suggestion. These algorithms are based on reliability of vehicle which is calculated as soon as vehicles enter the network and is updated after each successful execution of various operations of information propagation. The scheme works on by increasing the reliability which in turn solve the broadcast storm problem in which sometime irrelevant information may also be sent to the vehicles. Simulation results prove the merit of the proposed scheme over the other existing schemes with respect to parameters such as message overhead, connectivity ratio, and resources utilization.