Faculty Publications
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Item Performance of deep excavation for an underground metro station constructed by top-down method—A case study(Springer, 2019) Muhammad Ramees Ali, T.M.; C, C.The subway system plays a vital role in reducing the traffic congestion problems in urban cities. Recently, a number of underground railway transportation networks are commissioned and opened for operation or are being constructed in many cities in India. Metro station excavations is a tough task and pose threat to public safety, due to high intricacy and uncertainty in excavation activities. The stability of deep excavation and adjacent buildings has gained highlighted concerns during metro station construction. The underground metro station under study is located in Anna Salai Road, Chennai, India. The station is located at the centre of the main street. The project comprised of three basement levels. It has a length of 240 m and a width of 19–22 m at the track level. The excavation depth is about 18.0 m. The entire site is located within a floodplain. The performance of the deep excavation and adjacent area were monitored using extensive instruments. Monitored data are analyzed and presented as a case study in this paper. © Springer Nature Singapore Pte Ltd. 2019.Item Methods to monitor resources and logistic planning at project sites(Springer, 2019) Challa, P.R.; Das, B.B.Construction projects are unique and complex in nature. Various resources such as men, material, plant and machinery, capital, information, space, time, and above all local infrastructure are involved in the construction activity. Hence, controlling and monitoring the flow of resources plays a vital role in the timely completion of the project contributing to reduced delays leading to time and cost optimization. Productivity analysis of transit mixer, workmen have been done through data collected from a commercial project and suggestions to improve the same have been proposed. Logistics of two different sites one with onsite storage and other with offsite storage have been studied and recommendations to improve the logistics are provided. Some of the methods to monitor the resources on site have been proposed, which can be used without any hindrances at the construction project sites. Further, based on planned resources, a simplified logistics planning template and labour productivity monitoring data sheet which can be updated based on project’s progress is developed that can be used with ease at project site considering all the necessary factors. © Springer Nature Singapore Pte Ltd. 2019.Item A framework to monitor cloud infrastructure in service oriented approach(2013) Veigas, J.P.; Chandra Sekaran, K.Cloud computing processes and stores the organization's sensitive data in the third party infrastructure. Monitoring these activities within the cloud environment is a major task for the security analysts and the cloud consumer. The cloud service providers may voluntarily suppress the security threats detected in their Infrastructure from the consumers. The goal is to decouple Intrusion Detection System (IDS) related logic from individual application business logic and adhere to the Service Oriented Architecture Standards. This paper provides a framework for Intrusion Detection and reporting service to the cloud consumers based on the type of applications and their necessary security needs. Cloud consumers can choose the desired signatures from this framework to protect their applications. The proposed technique is deployed in existing open source cloud environment with minimum changes. A proof-of-concept prototype has been implemented based on Eucalyptus open source packages to show the feasibility of this approach. Our results show that this framework provides effective way to monitor the cloud infrastructure in service oriented approach. © 2013 IEEE.Item Artificial intelligence application in drought assessment, monitoring and forecasting: a review(Springer Science and Business Media Deutschland GmbH, 2022) Kikon, A.; Deka, P.C.Drought is a natural hazard creating havoc on economic, social and environmental aspects. As a result of its slow and creeping nature, it is problematic to establish the onset as well as the termination of drought. Irrespective of its spatial and temporal variability, drought occurs in almost all regions. A wide range of drought studies has been conducted by many researchers over a long period of time. The damage caused by drought has a huge impact on the social, economic and agricultural sectors. Researchers have defined drought in different ways depending upon the parameters and its characteristics, and universally there is no proper definition for drought because of its complexity in nature. This review is focused mainly on various Artificial Intelligence techniques used in drought assessment, monitoring, management and forecasting. The findings from the study shows that drought prediction has become significance in the field of hydrology, Water Resources Management, sustainable agriculture, etc. by using the various AI techniques. In recent studies, AI has been used widely in analysing drought in different regions. The applications of AI techniques in the domain of drought assessing, monitoring, forecasting, etc., shows a rapid growth and that the impact of these will be increasing in future. For understanding the different concepts of drought study, it is needed to establish different system of drought management in order to monitor the different factors affecting drought and then take proper measures to mitigate the damage. Literature studies have been done to analyze the onset and other measures of drought management. Future research may be oriented towards Modeling and probabilistic analysis of climatic data for refining the drought vulnerability mapping, analysis of onset and termination, warning system and drought declaration process depending on the conditions of the region. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Item Automatic shadow removal algorithm for VOP, DWT based watermarking algorithm for VOP and generation of super resolved VOP(2011) Pais, A.R.; D'Souza, J.; Reddy, R.M.; Hari Krishna, P.Removal of shadow from Video Object Planes (VOPs) will assist in surveillance applications for comprehensive detection of activities. We have proposed a method for removal of shadows from the VOP. Also noise removal is done using existing methods from the VOP. To authenticate the surveillance VOP, digital watermarking is used. We have proposed digital watermarking using localized Biorthogonal wavelets for VOP. Super-resolved VOP is generated using multi-frame method. Edge model based super resolution method is used to get the better results. Also the effect of digital watermarking is studied for the super-resolved VOP. A number of test cases have been proposed and found out a best method for video surveillance application. Our proposed super resolution (SR) method gives better results than bilinear and bi-cubic methods.Item Super-resolution video generation algorithm for surveillance applications(Maney Publishing Suite 1C, Joseph's Well, Hanover Walk Leeds LS3 1AB, 2014) Pais, A.R.; D'Souza, J.; Reddy, R.M.Video surveillance is one of the major applications where high-resolution (HR) images are crucial. Since the video camera has limited spatial and temporal resolution, there is a need for super resolution video generation algorithms. In this paper, we have presented a novel technique for activity detection in the surveillance video. To achieve this goal, we have proposed and investigated efficient algorithms for Video Object Plane (VOP) generation, shadow removal from VOP and super-resolved VOP generation, for activity detection from surveillance video. The proposed VOP generation algorithm is computationally efficient and works for both dynamic and static backgrounds. The novel shadow removal algorithm for the VOP is based on texture and its performance has been studied based on average shadow detection and discrimination rates. The proposed super-resolution video generation algorithm has been designed using edge models. The performance of this algorithm has been evaluated using a numerical analysis technique and is found to be better than bi-cubic and bi-linear interpolation techniques. © 2014 RPS.Item Condition monitoring of roller bearing by K-star classifier and K-nearest neighborhood classifier using sound signal(Tech Science Press sale@techscience.com, 2017) Sharma, R.K.; Sugumaran, V.; Kumar, H.; Amarnath, M.Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared. © Copyright 2017 Tech Science Press.Item Extraction of MapReduce-based features from spectrograms for audio-based surveillance(Elsevier Inc. usjcs@elsevier.com, 2019) Mulimani, M.; Koolagudi, S.G.In this paper, we proposed a novel parallel method for extraction of significant information from spectrograms using MapReduce programming model for the audio-based surveillance system, which effectively recognizes critical acoustic events in the surrounding environment. Extraction of reliable information as features from spectrograms of big noisy audio event dataset demands high computational time. Parallelizing the feature extraction using MapReduce programming model on Hadoop improves the efficiency of the overall system. The acoustic events with real-time background noise from Mivia lab audio event data set are used for surveillance applications. The proposed approach is time efficient and achieves high performance of recognizing critical acoustic events with the average recognition rate of 96.5% in different noisy conditions. © 2019 Elsevier Inc.Item UAV based cost-effective real-time abnormal event detection using edge computing(Springer, 2019) Shahzad Alam, M.S.; Natesha, B.V.; Ashwin, T.S.; Guddeti, R.M.R.Recent advancements in computer vision led to the development of a real-time surveillance system which ensures the safety and security of the people in public places. An aerial surveillance system will be advantageous in this scenario using a platform like Unmanned Aerial Vehicle (UAV) will be very reliable and can be considered as a cost-effective option for this task. To make the system fully autonomous, we require real-time abnormal event detection. But, this is computationally complex and time-consuming due to the heavy load on the UAV, which affords limited processing and payload capacity. In this paper, we propose a cost-effective approach for aerial surveillance in which we move the large computation tasks to the cloud while keeping limited computation on-board UAV device using edge computing technique. Further, our proposed system will maintain the minimum communication between UAV and cloud. Thus it not only reduces the network traffic but also reduces the end-to-end delay. The proposed method is based on the state-of-the-art YOLO (You Only Look Once) technique for real-time object detection deployed on edge computing device using Intel neural compute stick Movidius VPU (Vision Processing Unit), and we applied abnormal event detection using motion influence map on the cloud. Experimental results demonstrate that the proposed system reduces the end-to-end delay. Further, Tiny YOLO is six times faster while processing the frames per second (fps) when compared to other state-of-the-art methods. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Enhanced Optical Wireless Communication System for Bio-signal Monitoring Applications(Springer, 2020) Krishnan, P.; Gopikrishna, S.In this paper, optical wireless communication (OWC) technology based mobile remote care unit is proposed. Since, the existing RF based medical systems are suffer with misdiagnosis due to electromagnetic interference (EMI) and the influence of the radiation field on medical equipment’s. These issues are addressed with the help of OWC links between the medical sensors and receiver fixed in the ceiling of the hospital room. The power efficiency of the proposed system is analyzed. The results show the importance of OWC over the RF communication network in healthcare monitoring systems. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.