Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Pedestrian tracking algorithm in NLOS environments(2012) Gupta, C.; Biswas, D.This paper presents a cellular network based positioning algorithm in an urban environment characterized by multipath and severe non line of sight (NLOS) errors. The proposed algorithm mitigates localization error up to 75% as shown by the simulation results. The algorithm involves an initial averaging step followed by a prediction step for optimization, confining the estimated location close to the actual location. The proposed algorithm doesn't require additional hardware like sensors, accelerometers, gyroscopes etc. for localization as used in traditional cellular network based positioning methods. This approach can also be utilized in indoor positioning system (IPS) and global positioning systems (GPS) when at most three satellites are available. Low computational complexity of the algorithm is an added advantage. Utilization of orthogonal sources of information for improving accuracy is also explored. © 2012 IEEE.Item Machine Learning Based Propagation Loss Module for Enabling Digital Twins of Wireless Networks in ns-3(Association for Computing Machinery, 2022) Almeida, E.N.; Rushad, M.; Kota, S.R.; Nambiar, A.; Harti, H.L.; Gupta, C.; Waseem, D.; Santos, G.; Fontes, H.; Campos, R.; Tahiliani, M.P.The creation of digital twins of experimental testbeds allows the validation of novel wireless networking solutions and the evaluation of their performance in realistic conditions, without the cost, complexity and limited availability of experimental testbeds. Current trace-based simulation approaches for ns-3 enable the repetition and reproduction of the same exact conditions observed in past experiments. However, they are limited by the fact that the simulation setup must exactly match the original experimental setup, including the network topology, the mobility patterns and the number of network nodes. In this paper, we propose the Machine Learning based Propagation Loss (MLPL) module for ns-3. Based on network traces collected in an experimental testbed, the MLPL module estimates the propagation loss as the sum of a deterministic path loss and a stochastic fast-fading loss. The MLPL module is validated with unit tests. Moreover, we test the MLPL module with real network traces, and compare the results obtained with existing propagation loss models in ns-3 and real experimental results. The results obtained show that the MLPL module can accurately predict the propagation loss observed in a real environment and reproduce the experimental conditions of a given testbed, enabling the creation of digital twins of wireless network environments in ns-3. © 2022 ACM.Item Spectral Indices based Land Cover Classification using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Payani, C.A.; Gupta, C.; Anand Kumar, M.In this paper, we use Landsat 8 and 9 satellite data to predict the land area that is suitable for agriculture and farming. Early identification and deriving insights from areas and their land properties will give us the scope for better utilization of the area. To achieve this, We used 2 manually created datasets using google earth engine. Even though the main motive is to predict the productive cropland using the created dataset. classification task we intended is to identify the type of region in the given area of land as Water, Barren land, cropland, forests, and urban areas. Deep feed forward neural network and 1D CNN models are used for this classification. The DFNN model consistently outperformed the 1D CNN across all datasets, showing superior classification accuracy and overall performance. On both the KAPLCU, MLCU, and Hybrid datasets, DFNN demonstrated better precision, recall, and F1 scores, confirming its effectiveness in classifying land regions based on satellite imagery. Future work could involve exploring land cover classification using government datasets and developing labeled repositories from unlabeled satellite images to further expand research potential in this domain. © 2024 IEEE.Item Osteosarcoma Bone Cancer Detection(Springer Science and Business Media Deutschland GmbH, 2025) Payani, C.A.; Gupta, C.; Vamsidhar, K.; Bhat, P.; Patil, N.Osteosarcoma is a type of bone cancer commonly found in the elongated bones found in the upper and lower limbs. The precise cause is unknown, but experts believe it’s linked to changes in the DNA of the bones, resulting in the growth of abnormal and harmful bone tissue. If caught early, osteosarcoma is treatable, with about 75% people cured when the cancer hasn’t spread to other body parts. Analyzing biopsy samples can be time-consuming, but there are advanced computer programs, known as supervised deep learning methods, that can help speed up the process and enhance the efficiency of the diagnosis. Previous studies have already evaluated the performance of deep learning models such as VGG16, VGG19, DenseNet201, and ResNet101, among which ResNet101 performed better with 90.36% accuracy. When it comes to understanding complex image features, previous models lack behind. We propose EfficientNetV2, Xception, and InceptionV3 models, among which Xception outperformed other models with 94.5% accuracy on the image dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.