Faculty Publications

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    Laboratory investigations on the effect of fragmentation and heterogeneity of coastal vegetation in wave height attenuation
    (Springer, 2019) Shirlal, K.G.; John, B.M.; Rao, S.
    It has long been known that “bio-shields” do function as a sustainable solution for preserving our coasts. The presence of gaps in the “bio-shield”, that is, the forest cover, referred to as patchiness, is a common phenomenon in natural habitats. Various anthropogenic and natural causes can result in such gaps in coastal forests. This paper presents the results of a physical model investigation carried out with a fragmented heterogeneous vegetation model in a wave flume 50 m long, 0.71 m wide and 1.1 m deep. The heterogeneous meadow is modelled as a combined body of artificial submerged seagrass, rigid vegetation and emergent vegetation. To study the effect of fragmentation in vegetation, transverse gaps of varying widths are introduced in the heterogeneous model. The material used for modelling is polyethylene and nylon. The test runs were carried out with monochromatic waves of heights ranging from 0.08 to 0.16 m in water depths of 0.40 and 0.45 m, and wave periods 1.8 and 2 s. The wave height measurements at different locations within the vegetated meadow exhibit an exponential decay of wave heights. The presence of gaps in vegetation does not have a significant effect on wave height reduction. However, the experimental study revealed that heterogeneous vegetation showed a great promise leading to considerable wave attenuation, thus offering a good level of protection to life and property on the leeside. © Springer Nature Singapore Pte Ltd. 2019.
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    Multiple aggregator multiple chain routing protocol for heterogeneous wireless sensor networks
    (2013) Harichandan, P.; Jaiswal, A.; Kumar, S.
    Wireless sensor nodes are deployed to gather useful information from the field but their constraint on battery power leads us to think about energy efficient routing protocols so that they can operate over longer periods of time. We study the advantages of having multiple chains in a network with each chain's topmost node (called the aggregator) collecting the data from the nodes beneath it and transmitting it to the sink. In the proposed scheme, a chain in each region works as PEGASIS. We also study how considering heterogeneity in the network can improve the lifetime of a network by a significant period. We assume that a fraction of the nodes in the network possess additional energy. We show by simulations that the introduction of heterogeneity into the network results in a greater lifetime, compared to those of the classical data aggregation schemes, with the duration increasing with the amount of additional energy considered. © 2013 IEEE.
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    Resource aware scheduling in Hadoop for heterogeneous workloads based on load estimation
    (2013) Kapil, B.S.; Kamath S․, S.S.
    Currently, most cloud based applications require large scale data processing capability. Data to be processed is growing at a rate much faster than available computing power. Hadoop is used to enable distributed processing on large clusters of commodity hardware. In large clusters, the workloads may be heterogeneous in nature, that is, I/O bound, CPU bound or network intensive jobs that demand different types of resources requirement so as to run simultaneously on large cluster. Hadoops job scheduling is based on FIFO where, parallelization based on types of job has not been taken into account for scheduling. In this paper, we propose a new scheduling algorithm for Hadoop based distributed system, based on the classification of workloads to assign a specific category to a particular cluster according to current load of the cluster. The proposed scheduler increases the performance of both CPU and I/O resources in a cluster under heterogeneous workloads, by approximately 12% when compared to Hadoops FIFO scheduler. © 2013 IEEE.
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    Analyzing the Heterogeneity in Public Transit Demand: Impact of Spatial and Temporal Attributes
    (Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Venkateswari, N.P.
    The usage of personal vehicles is causing several issues like traffic congestion, greenhouse gas emissions, and massive energy consumption. These issues can be alleviated by implementing a public bus transport system. But the irregular frequency of buses and longer waiting times are diminishing the attractiveness of public bus transportation in India. To implement an affordable and efficient public transport system, it is necessary to understand the heterogeneity of public transit demand under different conditions. Limited scholarly exploration on the impact of spatial and temporal characteristics on the heterogeneity of public transit demand under Indian conditions. This paper aims to measure public transit demand heterogeneity using the coefficient of variation of transit demand, which is defined as a ratio of standard deviation to mean. Spatial, temporal, and weather characteristics are considered to analyze their influence on the heterogeneity of public transit demand. The statistical variability analysis is performed using Electronic Ticketing Machine (ETM) data, weather data, and bus network data. The results indicate that the combined impact of weather conditions and the built environment has a stronger influence on the variability in Public Transit Demand (PTD) than each factor individually. Based on the analyses, it is recommended to change the service type to improve the transport system's efficiency. This study suggests the importance of incorporating spatial, temporal, and weather characteristics. This study can help stakeholders to optimize public transport networks and schedule service frequency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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    Heterogeneous data format integration and conversion (HDFIC) using machine learning and IBM-DFDL for IoT
    (Springer Nature, 2024) Sandeep, S.; Chandavarkar, B.R.; Khatri, S.
    The future of the Internet of Things (IoT) demands the integration of synergetic applications to cater to societal needs. Examples of IoT-based confederated applications include Ambient Assisted Living with Active Healthy Ageing, CasAware with Smart Energy, Smart Gas Distribution Networks with GIS systems, and more. However, the data heterogeneity hinders integration, as these systems follow different standards, data formats, semantic models, and representations. Further, this leads to data interoperability issues in IoT. The major concern of academia and industry in the smooth integration of heterogeneous applications is interpreting different data formats and representing them in a common schema for further analysis. Existing solutions, such as message payload translation, middleware/cloud format, and Inter-IoT, are complex, time-consuming, and ineffective. Hence, this paper proposes the heterogeneous data format integration and conversion (HDFIC), a machine learning-based system to identify data formats using a Random Forest classifier and integrate them using the Data Format Description Language (DFDL). The content-based data format identification in the proposed HDFIC is trained with the standard features defined in RFC 7111, 8259, and 8996. Subsequently, the data is integrated into a single XML Schema Definition and converted into the required data format using the IBM App Connect Enterprise tool and DFDL. Finally, the performance of HDFIC is evaluated with the synergetic patient body vitals and room ambiance dataset for accuracy, data integration time, and conversion efficiency. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    Numerical studies on modeling heterogeneity in elastic properties of 8HS woven C/C composites
    (Taylor and Francis Ltd., 2025) Vishnu, O.S.; Pavan, G.S.; R, S.; Thomas, A.
    The variation in fiber volume fraction and pores developed at the microscale during the manufacturing process is a source of heterogeneity in the elastic properties of woven Carbon/Carbon (C/C) composites. This study investigates the effect of heterogeneity on the elastic properties of Eight Harness Satin (8HS) woven C/C composites using a two-scale (micro–meso) finite element (FE) homogenization method. At the microscale, variations in fiber volume fraction and porosity are incorporated by generating 25 representative volume elements(RVEs) from the reconstructed CT scan images. The RVEs preserve the shape, size, orientation, and spatial distribution of pores that are present in the microstructure. The carbon fibers are virtually generated inside the 25 micro-RVEs using the random sequential adsorption (RSA) algorithm in accordance with the reconstructed microstructure of actual pores. At the mesoscale, the model incorporates warp and weft yarns embedded in a pyrolytic carbon matrix. Yarn heterogeneity is modeled by subdividing the meso RVE into smaller domains, each assigned elastic properties derived from the microscale RVEs. The degree of heterogeneity was varied using different combinations of the microscale RVEs to assign material properties. This approach effectively incorporates the randomness of the microstructure into the computation of the effective elastic properties of woven composites. The on and off-axis elastic properties of 8HS woven C/C composites are computed, and the results determined from the numerical study are compared with experimental tests conducted on 0° and 45° specimens. This study highlights the importance of fiber volume fraction and pores on material heterogeneity in accurately computing the elastic properties of 8HS woven C/C composites. © 2025 Taylor & Francis Group, LLC.
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    Deep learning-based public transit passenger flow prediction model: integration of weather and temporal attributes
    (Springer Science and Business Media Deutschland GmbH, 2025) Shanthappa, N.K.; Mulangi, R.H.; Harsha, H.M.
    A reliable prediction model is critical for the public transit system to keep it periodically updated. However, it is a challenging task to develop a model of high precision when there is heterogeneity in the travel demand which is very common in developing countries. The spatial and temporal attributes along with external factors like weather should be incorporated into the prediction models to account for heterogeneity. Numerous studies in the past developed passenger flow prediction models considering spatial and temporal dependencies, whereas the integration of weather components with temporal dependencies while developing a prediction model for public bus transit has not been widely considered. Hence, the present research work employs long short-term memory (LSTM) to develop a route-level bus passenger flow prediction model, called RPTW-LSTM, by integrating temporal dependencies such as recent time intervals (R), daily periodicity (P) and weekly trend (T), and weather variables (W). The model is tested using a real-life dataset of the Udupi city bus service, located on the west coast of Karnataka, India. Additionally, Shapley Additive Explanation (SHAP) analysis is adopted to identify the relative importance of the features used. Results imply that the inclusion of the aforementioned factors enhanced the performance of RPTW-LSTM when compared to basic LSTM and other conventional models. Additionally, weekly trend and weather exhibit higher significance on the model than recent time intervals. This implies that evaluating the features affecting the heterogeneity in passenger flow and incorporating them into the model assists transport planners in achieving high precision. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.