Faculty Publications
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Publications by NITK Faculty
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Item A review of dynamic web service composition techniques(2011) D’Mello, D.A.; Ananthanarayana, V.S.; Salian, S.The requester's service request sometimes includes multiple related functionalities to be satisfied by the Web service. In many cases theWeb service has a limited functionality which is not sufficient to meet the requester's complex functional needs. The discovery mechanism for such complex service request involving multiple tasks (operations) may fail due to unavailability of suitable Web services advertised in the registry. In such a scenario, a need arises to compose the available atomic or composite Web services to satisfy the requester's complex request. Dynamic Web service composition generates and executes the composition plan based on the requester's runtime functional and nonfunctional requirements. This paper provides the review of Web service composition architectures and techniques used to generate new (value added) services. © Springer-Verlag Berlin Heidelberg 2011.Item Investigation of CMOS Based Integration Approach Using DAI Technique for Next Generation Wireless Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Roy, G.M.; Kanuajia, B.K.; Dwari, S.; Kumar, S.; Song, H.This research work investigates a CMOS based low noise amplifier (LNA) using differential active inductor with eight-shaped patch antenna for next generation wireless communication. The proposed work conceded into three different phases. The first phase proposes LNA architecture which includes multistage cascode amplifier with a gate inductor gain peaking technique. The ground approach for this architecture employs active inductor technique that includes two stages of differential amplifier. The proposed novel technique leads to give incremental in inductance by using of common mode feedback resistor and lowers the undesirable parasitic resistance effect. Additionally, this technique offers gain enhanced noise cancellation and achieves a frequency band of around 5.7 GHz. The proposed architecture includes single stage differential AI and enhances the bandwidth up to 6.8 GHz with peak gain of 21 dB at 7.8 GHz. The noise figure and stability factor are achieved which is reasonably good at 1 dB. The proposed architecture is design and optimized on advanced design RF simulator using 0.045 µm CMOS process technology. While in second phase, a narrow band eight-shaped patch antenna is designed which provides operating band range from 5.8 to 6.5 GHz with 6.2 GHz resonating frequency. Highest peak gain of 15 dB and maximum radiation power of 42.5 dBm is succeed by proposed antenna. The final phase provides integration strategy of LNA with antenna and achieves desired gain of nearly 21 dB with minimum NF of 1.2–1.5 dB in the same band. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.Item Shipping code towards data in an inter-region serverless environment to leverage latency(Springer, 2023) Sethi, B.; Addya, S.K.; Bhutada, J.; Ghosh, S.K.Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Classification and grade prediction of kidney cancer histological images using deep learning(Springer, 2024) Chanchal, A.K.; N, S.; Lal, S.; Kumar, S.; Saxena, P.U.P.Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist’s experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Item MSSEAG-UNet: A Novel Deep Learning Architecture for Cloud Segmentation in Fisheye Sky Images and Solar Energy Forecast(Institute of Electrical and Electronics Engineers Inc., 2025) Kumar, A.; Kashyap, Y.; Sharma, K.; Vittal, K.; Shubhanga, K.N.This study analyzes sky images captured using a ground-based fisheye camera, aiming to address the challenge of accurately segmenting clouds, which is difficult due to their fuzzy and indistinct boundaries and uneven lighting conditions. Accurate segmentation of clouds in ground-based sky images is crucial for accurate solar energy forecasting. Motivated by these challenges, this article has proposed a novel deep learning architecture called multispatial squeeze-and-excite attention gated U-Net (MSSEAG-UNet) for cloud segmentation in ground-based fisheye sky images. The proposed architecture integrates a multispatial convolutional (MS-CNN) block and squeeze-and-excitation (SE) blocks in the encoder path to improve multiscale feature extraction (MFF) and recalibrate feature maps, while an attention block is incorporated in the decoder path to emphasize key cloud features. The segmentation performance of the MSSEAG-UNet is compared with five benchmark models, and results show that the proposed model outperforms than all benchmarks models. Furthermore, the segmented cloud images produced by the MSSEAG-UNet are used to calculate the cloud percentage, which is then integrated with the original sky images using a multicolumn convolutional model for global horizontal irradiance (GHI) forecast. GHI forecast is conducted for 15-, 30-, and 60-min ahead timesteps, with the best results achieved for the 60-min forecast, yielding mean absolute error (MAE), mean square error (mse), and RMSE values of 6.245%, 0.683%, and 8.265%, respectively. These results highlight the effectiveness of the proposed approach in improving both cloud segmentation accuracy and short-term solar irradiance forecasting. © 1980-2012 IEEE.Item Experimental investigation on a novel base-isolator for ground supported liquid storage tanks(Springer Science and Business Media B.V., 2025) Jogi, P.; Jayalekshmi, B.R.The dynamic analysis of ground-supported rectangular liquid storage tanks (LSTs) with base isolation, using steel core and filler bearings, is conducted experimentally and numerically through the finite element method. Five types of bearings are designed for the selected tank geometry, namely laminated rubber bearing, sand-filled rubber bearing, coir-filled rubber bearing, steel core-sand filled rubber bearing, and steel core-coir filled rubber bearing. LST models with these bearings are fabricated and tested on a shake table under harmonic motion. The models are also analyzed numerically in ABAQUS using a coupled acoustic-structural approach to examine fluid–structure interaction behaviour and compare with experimental results. Seismic response parameters, including convective displacement, hydrodynamic pressures on the tank wall and base, and bearing displacements, are investigated under harmonic motions. The designed bearings significantly reduce hydrodynamic pressures compared to standard laminated rubber bearings. Specifically, the base isolator with steel core-coir filler reduces the hydrodynamic pressure by more than 86% and the bearing displacement by 96% as compared to non-isolated LSTs. This novel isolation effectively reduces the risk of LST failure under dynamic loads due to its high energy dissipation capacity, effective stiffness, damping, and yield strength, even at low bearing displacements. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.