Faculty Publications
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Item Integrated coastal zone management plan for Udupi coast using remote sensing, geographical information system and global position system(SPIE spie@spie.org, 2008) Dwarakish, G.S.; Vinay, S.A.; Dinakar, S.M.; Pai, B.J.; Mahaganesha, K.; Natesan, U.Coastal areas are under great pressure due to increase in human population and industrialization/commercialization and hence these areas are vulnerable to environmental degradation, resource reduction and user conflicts. In the present study an Integrated Coastal Zone Management Plan (ICZMP) has been developed for Udupi Coast in Karnataka, along West Coast of India. The various data products used in the present study includes IRS-1C LISS-III + PAN and IRS-P6 LISS III remotely sensed data, Naval Hydrographic Charts and Survey of India (SOI) toposheets, in addition to ground truth data. Thematic maps such as land use/ land cover map, bathymetry map, shoreline configuration map, transportation and drainage network maps, GPS survey map, CRZ map, contour map, DEM, inundation map, critical erosion area map were prepared. A Coastal Vulnerability Index has also been calculated for the study area to know the resistance of study area to sea level rise and is demarcated into four categories; Very high, High, Moderate and Low vulnerability, and a vulnerability map has been prepared. The results of the present study are encouraging. Some of the specific conclusions of the study are; about 50% study area is prone to erosion, river mouths along study area show shifting tendency towards south, and the beaches along the Udupi Coast are maintaining dynamic equilibrium. Coastal Zone Information System (CZIS) has been developed through V.B.6.0 using results of various data analysis. © 2008 Society of Photo-Optical Instrumentation Engineers.Item An efficient search to improve neighbour selection mechanism in P2P network(2009) Totekar, C.R.; Santhi Thilagam, P.S.One of the key challenging aspects of peer-to-peer systems has been efficient search for objects. For this, we need to minimize the number of nodes that have to be searched, by using minimum number of messages during the search process. This can be done by selectively sending requests to nodes having higher probability of a hit for queried object. In this paper, we present an enhanced selective walk searching algorithm along with low cost replication schemes. Our algorithm is based on the fact that most users in peer-to-peer network share various types of data in different proportions. This knowledge of amount of different kinds of data shared by each node is used to selectively forward the query to a node having higher hit-ratio for the data of requested type, based on history of recently succeeded queries. Replication scheme replicates frequently accessed data objects on the nodes which get high number of similar queries or closer to the peers from where most of the queries are being issued. Two simple replication schemes have been discussed and their performances are compared. Experimental results prove that our searching algorithm performs better than the selective walk searching algorithm. © 2009 Springer Berlin Heidelberg.Item Quality and business offer driven selection of web services for compositions(2009) D’Mello, D.A.; Ananthanarayana, V.S.The service composition makes use of the existing services to produce a new value added service to execute the complex business process. The service discovery finds the suitable services (candidates) for the various tasks of the composition based on the functionality. The service selection in composition assigns the best candidate for each tasks of the pre-structured composition plan based on the non-functional properties. In this paper, we propose the broker based architecture for the QoS and business offer aware Web service compositions. The broker architecture facilitates the registration of a new composite service into three different registries. The broker publishes service information into the service registry and QoS into the QoS registry. The business offers of the composite Web service are published into a separate repository called business offer (BO) registry. The broker employs the mechanism for the optimal assignment of the Web services to the individual tasks of the composition. The assignment is based on the composite service providers's (CSP) variety of requirements defined on the QoS and business offers. The broker also computes the QoS of resulting composition and provides the useful information for the CSP to publish thier business offers. © 2009 Springer Berlin Heidelberg.Item Context Aware Trust Management Scheme for Pervasive Healthcare(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Karthik, N.; Ananthanarayana, V.S.Medical sensor nodes are used in pervasive healthcare applications like remote patient monitoring, elderly care to collect patients vital signs for identifying medical emergency. These resource restricted sensor nodes are prone to various malicious attacks, data faults and data losses. Presence of faulty data, data loss in collected patient data may lead to incorrect analysis of patient condition, which decreases the reliability of pervasive healthcare system. The aim of this work is to alert the caregiver and raise the alarm only when the patient enters into medical emergency situation. The proposed scheme also reduces the false alarms and alerts caused by data fault and misbehaving sensor nodes. To achieve this, we introduce a context aware trust management scheme for data fault detection, data reconstruction and event detection in pervasive healthcare systems. It employs heuristic functions, data correlation and contextual information based algorithms to identify the data faults and events. It also reconstructs the data faults and data loss for identifying patient condition. Performance of this approach is evaluated with the help of real data samples collected by medical sensor network prototype of remote patient monitoring application. The experimental results show that the proposed trust scheme outperforms state-of-the-art techniques and achieves good detection accuracy in data fault detection and event detection. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Evaluating the Performance of CHIRPS Satellite Rainfall Data for Streamflow Forecasting(Springer Netherlands rbk@louisiana.edu, 2019) Sulugodu, B.; Deka, P.C.Streamflow forecasting can offer valuable information for optimal management of water resources, flood mitigation, and drought warning. This research aims in evaluating the effectiveness of CHIRPS satellite rainfall data in comparison with IMD gridded Rainfall Data and development of various flow forecasting models. Daily rainfall data for three decades (1983–2012) over the Nethravathi Basin, Karnataka, India is used for analysis. The analysis is carried out for the monsoon season (June–September), out of which 70% data considered for training the model and remaining for testing. Different input combinations are developed, and soft-computing methods like ANFIS, GRNN, PSO-ANN, and ELM are applied for flow forecasting on a temporal scale. The model performance is evaluated using various statistical indices like NNSE, RRMSE, and MAE. The results indicate that CHIRPS rainfall showed better performance in comparison with IMD data. ELM expressed an enhanced effect when compared to all other methods. The usefulness and effectiveness of CHIRPS data compared to IMD data has been explored. © 2019, Springer Nature B.V.Item Performance prediction of data streams on high-performance architecture(Springer Berlin Heidelberg, 2019) Gautam, B.; Annappa, A.Worldwide sensor streams are expanding continuously with unbounded velocity in volume, and for this acceleration, there is an adaptation of large stream data processing system from the homogeneous to rack-scale architecture which makes serious concern in the domain of workload optimization, scheduling, and resource management algorithms. Our proposed framework is based on providing architecture independent performance prediction model to enable resource adaptive distributed stream data processing platform. It is comprised of seven pre-defined domain for dynamic data stream metrics including a self-driven model which tries to fit these metrics using ridge regularization regression algorithm. Another significant contribution lies in fully-automated performance prediction model inherited from the state-of-the-art distributed data management system for distributed stream processing systems using Gaussian processes regression that cluster metrics with the help of dimensionality reduction algorithm. We implemented its base on Apache Heron and evaluated with proposed Benchmark Suite comprising of five domain-specific topologies. To assess the proposed methodologies, we forcefully ingest tuple skewness among the benchmarking topologies to set up the ground truth for predictions and found that accuracy of predicting the performance of data streams increased up to 80.62% from 66.36% along with the reduction of error from 37.14 to 16.06%. © 2019, The Author(s).Item Delay in Rural Road Construction: Evidence from Pradhan Mantri Gram Sadak Yojana in Shimoga District, Karnataka, India(Springer, 2021) Suresha, S.N.; Arun, V.Rural roads are the basic infrastructure required for the improvements of rural areas. Rural roads had been planned and constructed under various rural development programs by the Government of India. However, serious efforts through these programs could not make road connectivity for more than 50% of rural areas in India. Hence, the Government of India had launched a major programme known as Pradhan Mantri Gram Sadak Yojana (PMGSY) on December 25, 2000. The PMGSY programme emphasizes on time and quality of construction. This paper reports an investigation on the delay in the construction of PMGSY roads in Shimoga district, Karnataka, India. Using the real-time data of PMGSY roads available on the Online Management Monitoring and Accounting System, an analysis was made to find the delay occurrence in the construction projects of the study area and the effect of various parameters with respect to delay. The study reveals that 95% of the PMGSY road projects have not been completed within the stipulated time and the road length, construction type, and seasons do not affect delay in construction. The present study helps the policy makers to implement reliable polices on rural road programs in the future. © 2021, The Institution of Engineers (India).Item Bit error rate analysis of polarization shift keying based free space optical link over different weather conditions for inter unmanned aerial vehicles communications(Springer, 2021) Nallagonda, V.; Krishnan, P.The increasing availability of unmanned aerial vehicles (UAVs) is an exciting part of future emerging technology with advanced scientific and industrial interests. Free space optical (FSO) communications’ ability to offer very high data rates and the mobility of unmanned aerial vehicle (UAV) flying platforms make the delivery of Fifth-Generation (5G) wireless networking services appealing to FSO-UAV-based solutions. UAVs play a greater role in end-to-end delivery in next- generation wireless networking systems, serving as a base station, capacity enhancement, high data access, and other disaster management systems. To establish a link between unmanned aerial vehicles and ground stations, FSO can be applied. But, the different weather conditions liken rain, fog effects on the performance of the FSO link, contributing to the loss of the signal. In this paper, we proposed polarization shift keying (POLSK) modulated FSO link based UAV–UAV communication system for 6G beyond applications. We examine the effect of different weather conditions such as rain, fog on the bit error rate (BER) performance of the proposed system. Novel closed-form expressions for UAV–UAV based FSO propagation channel are derived, and BER performance is investigated under different weather conditions. Fog and rain are the main limiting factors mitigated in this paper by suitable mitigation techniques by increasing receiver field of view. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item GPU-aware resource management in heterogeneous cloud data centers(Springer, 2021) Kulkarni, A.K.; Annappa, B.The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into cloud data centers. With the evolution of Graphics Processing Units, composing of an extensive array of parallel computing single-instruction-multiple-data processors are being considered as a platform for high-performance computing because of their high throughput. Many cloud providers have begun offering GPU-enabled services for the users where GPUs are essential (for high computational power) to meet the desired Quality-of-service. Virtual machine placement and load balancing the GPUs in the virtualized environments like the cloud is still an evolving area of research and it is of prime importance to achieve higher resource efficiency and also to save energy. The current VM placement techniques do not consider the impact of VM workload type and GPU memory status on the VM placement decisions. This paper discusses the current issues with the First Fit policy of virtual machine placement used in VMWare Horizon and proposes a GPU-aware VM placement technique for GPU-enabled virtualized environments like cloud data centers. The experiments conducted using the synthetic workloads indicate reduction in the energy consumption, reduction in search space of physical hosts, and the makespan of the system. It also presents a summary of the current challenges for GPU resource management in virtualized environments and specific issues in developing cloud applications targeting GPUs under the virtualization layer. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.Item Deep neural models for automated multi-task diagnostic scan management - Quality enhancement, view classification and report generation(IOP Publishing Ltd, 2022) Karthik, K.; Kamath S․, S.The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval, automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications. © 2021 IOP Publishing Ltd.
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