Faculty Publications
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Item Cost reduction techniques on MEP projects(Springer, 2019) Akhil, R.P.; Das, B.B.For a construction project main objective is to complete the work on time and achieve the desired profit by giving the required quality to the customer. To complete the work on time, planning and scheduling have the key roles. Reducing the cost of project is done by a continuous process, the cost-dependent factors should be identified and various methods should be implemented to get the cost reduced. In this paper construction, cost reduction methods are analysed and the implementation of such methods in MEP (Mechanical electrical pluming) projects are discussed. Construction work and MEP work will be tedious and losses due to improper planning of project, early allocation of materials, unskilled labours and shortage, price fluctuation of materials, delay in supply of materials, rework and wastage of materials, wrong budgeting, weather deflections, unorganised management and control, coordination problems of service and civil works, etc. All these obstructions will result in delay of projects and cost overrun. Time and cost optimisation can be done by various techniques which is major for a project for its successful completion. This paper reviewed best cost optimisation methods and summarise the suitable method for Indian construction industry. This study mainly discussed on different techniques to reduce the cost in MEP projects and difficulty facing to implement this techniques and its betterment of execution. © Springer Nature Singapore Pte Ltd. 2019.Item Impact of internet of things in smart cities(Institution of Engineering and Technology, 2020) Rudra, B.A smart city uses various technologies to make the lives more easy and simple fulfilling the demands of the increasing population. Internet of Things (IoT) plays a major role in making the city smart. It takes the required input and helps to make the things associated with it smart. Some of the smart operations include water and energy management which is becoming scarce. The system can be deployed in the cities to make things work and save future resources. A smart city can be empowered to increase the quality of life of the people and improve the environment to sustain for a long time. Implementing a smart city with IoT and connected technology helps enhance the quality, performance and interactivity of urban services, optimize resources and reduce costs. The chapter briefly discusses what is the role of IoT in smart cities describing the basics of what is IoT and what comprises a smart city followed by smart city segments. Benefits of IoT and their impact on the smart city along with the national and international case studies. At the end of the chapter, some of the challenges associated with the IoT with respect to smart cities. © The Institution of Engineering and Technology 2020.Item A new probabilistic rekeying method for secure multicast groups(2010) Pais, A.R.; Joshi, S.The Logical Key Hierarchy (LKH) is the most widely used protocol in multicast group rekeying. LKH maintains a balanced tree that provide uniform cost of O(log N) for compromise recovery, where N is group size. However, it does not distinguish the behavior of group members even though they may have different probabilities of join or leave. When members have diverse changing probabilities, the gap between LKH and the optimal rekeying algorithm will become bigger. The Probabilistic optimization of LKH (PLKH) scheme, optimized rekey cost by organizing LKH tree with user rekey characteristic. In this paper, we concentrate on further reducing the rekey cost by organizing LKH tree with respect to rekey probabilities of members using new join and leave operations. Simulation results show that our scheme performs 18 to 29% better than PLKH and 32 to 41% better than LKH. © 2010 Springer-Verlag.Item Processing of cenosphere/HDPE syntactic foams using an industrial scale polymer injection molding machine(Elsevier Ltd, 2016) Bharath Kumar, B.R.; Doddamani, M.R.; Zeltmann, S.E.; Gupta, N.; Ramesh, M.R.; Ramakrishna, S.Rapid production of high quality components is the key to cost reduction in industrial applications. The present work is the first attempt of manufacturing syntactic foams, hollow particle filled lightweight composites, using an industrial scale injection molding machine. High density polyethylene (HDPE) is used as the matrix material and fly ash cenospheres are used as the filler. Development of syntactic foams with cenospheres serves dual purpose of beneficial utilization of industrial waste fly ash and reduction in the cost of the component. The pressure and temperature used in the injection molding process are optimized to minimize fracture of cenospheres and obtain complete mixing of cenospheres with HDPE. The optimized parameters are used for manufacturing syntactic foams with 20, 40 and 60 wt.% cenospheres. With increasing cenosphere content, density and strength reduce and modulus increases. Surface modification of constituents results in rise in strength with increasing filler content. A theoretical model based on a differential scheme is used to estimate the properties of cenospheres by conducting parametric studies because of inherent difficulties in direct measurement of cenosphere properties. The potential for using the optimized injection molding process is demonstrated by casting several industrial components. © 2015 Elsevier Ltd.Item Model generation and process optimization of microwave-assisted aqueous extraction of anthocyanins from grape juice waste(Blackwell Publishing Inc. subscrip@blackwellpub.com, 2017) Venkatramanan, V.; Shanmugam, S.; Arulvel, A.The microwave-assisted extraction of anthocyanins from grape juice waste was investigated in this study. The optimization was implemented using response surface methodology with Box–Behnken design and genetic algorithm (GA). Anthocyanins from grape juice waste were extracted under various microwave power (100–600 W), exposure time (1–5 min) and solvent/solid ratio (10–50 ml/g). The total monomeric anthocyanin yield was considered as the response for optimization experiments. The results indicated that the quadratic model was significant for the chosen response at p <.0001. The analysis of variance and response surface plots showed a significant interaction of all the selected independent variables over anthocyanin extraction process. The maximum anthocyanin yield of 1.31881 mg/g of grape juice waste was predicted by response surface methodology, and the prediction was improved to 1.32244 mg/g of grape juice waste by GA. A confirmatory experiment performed under optimum conditions showed anthocyanin yield of 1.3215 mg/g of grape juice waste. Hence, this model was successful in predicting anthocyanin extraction from grape juice waste under microwave-assisted extraction conditions. Practical application: Anthocyanin pigments find a broad range of implementation as food colorants, antioxidants, and anticancerous agents. The waste residue obtained during grape juice production and processing is also rich in anthocyanins and can be used as an alternative source for anthocyanin extraction. This study exploits the use of grape juice waste for anthocyanin extraction, and it can be the best way of waste management and cost reduction in grape juice production units. © 2016 Wiley Periodicals, Inc.Item A partial key pre-distribution based en-route filtering scheme for wireless sensor networks(Springer Science and Business Media Deutschland GmbH, 2021) Kumar, A.; Bansal, N.; Pais, A.R.Compromised sensor nodes can be used to inject false reports (bogus reports) in wireless ssensor networks (WSNs). This can cause the sink to take wrong decisions. En-route filtering is a method to detect and filter false reports from WSNs. Most of the existing en-route filtering schemes use probabilistic approaches to filter false reports from the network, where filtering of false reports is based on a fixed probability. Thus false reports can travel multiple hops before being dropped. In this article we seek to overcome limitations of the existing schemes and reduce the overall key storage overhead in the cluster heads. In this article we propose a combinatorial design based partial en-route filtering scheme (CD-PEFS) which filters the fabricated reports deterministically. CD-PEFS reduces the energy requirements in the network by early detection and elimination of the false reports. Adoption of combinatorial design based keys get rid of shared key discovery phase from the network. This considerably reduces the communication overhead in the network. We carried out a detailed analysis of CD-PEFS against an increasing number of compromised sensor nodes in the network. We found that our scheme performs better than existing schemes in terms of filtering efficiency while maintaining low key storage overhead in the network. Further the performance of CD-PEFS is at par with existing schemes in terms of other protocol overheads. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.Item Dugdugi: An Optimal Fault Addressing Scheme for Octagon-Like On-Chip Communication Networks(Institute of Electrical and Electronics Engineers Inc., 2021) Bhowmik, B.Network-on-chip (NoC) has emerged as a scalable on-chip communication platform and, hence, has become more popular. However, as the sole communication medium, a single point of failure raised by any permanent fault can cause the failure of the entire system. Subsequently, the NoC has become a critically exposed unit that must be protected. This article primarily presents a test-time-independent and optimally distributed test scheme named 'Dugdugi' that addresses channel faults, e.g., short in an Octagon and similar NoC architectures to achieve high reliability. The proposed scheme is extended to cover other channel faults, such as stuck-at and transient faults, to give its impression of a comprehensive approach. Experimental results show that the proposed scheme incurs little hardware area and detects all modeled short faults by a few clocks with achieving fault coverage metric up to 100%. Online evaluation reveals the effect of channel-short faults on various network performance metrics. In comparison to prior methodologies, the proposed scheme improves hardware area overhead up to 71.79% and reduces test time over 94.20%. Furthermore, performance overhead, such as packet latency and energy consumption, reduces up to 40.85% and 43.87%, respectively. © 1993-2012 IEEE.Item Enhancing Deep Compression of CNNs: A Novel Regularization Loss and the Impact of Distance Metrics(Institute of Electrical and Electronics Engineers Inc., 2024) Pragnesh, P.; Mohan, B.R.Transfer learning models tackle two critical problems in deep learning. First, for small datasets, it reduces the problem of overfitting. Second, for large datasets, it reduces the computational cost as fewer iterations are required to train the model. Standard transfer learning models such as VGGNet, ResNet, and GoogLeNet require significant memory and computational power, limiting their use on devices with limited resources. The research paper contributes to overcoming this problem by compressing the transfer learning model using channel pruning. In current times, computational cost is more significant compared to memory cost. The convolution layer with fewer parameters contributes more to computational cost. Thus, we focus on pruning the convolution layer to reduce computational cost. Total loss is a combination of prediction loss and regularization loss. Regularization loss is the sum of the magnitudes of parameter values. The training process aims to reduce total loss. In order to reduce total loss, the regularization loss also needs to be reduced. Therefore, training not only minimizes prediction error but also manages the magnitude of the model's weights. Important weights are maintained at higher values to keep the prediction loss low, while unimportant weight values can be reduced to decrease regularization loss. Thus regularization adjusts the magnitudes of parameters at varying rates, depending on their importance. Quantitative pruning methods select parameters based on their magnitude, which improves the effectiveness of the pruning process. Standard L1 and L2 regularization focus on individual parameters, aiding in unstructured pruning. However, group regularization is required for structured pruning. To address this, we introduce a novel group regularization loss designed specifically for structured channel pruning. This new regularization loss optimizes the pruning process by focusing on entire groups of parameters belonging to the channel rather than just individual ones. This method ensures that structured pruning is more efficient and targeted. Custom Standard Deviation (CSD) is calculated by summing the absolute differences between each parameter value and the mean value. To evaluate the parameters of a given channel, both the L1 norm and CSD are computed. The novel regularization loss for a channel in the convolutional layer is defined as the ratio of L1 norm to CSD (L1Norm/CSD). This approach groups the regularization loss for all parameters within a channel, making the pruning process more structured and efficient. Custom regularization loss further improves pruning efficiency, enabling a 46.14% reduction in parameters and a 61.91% decrease in FLOPs. This paper also employs the K-Means algorithm for similarity-based pruning and evaluates three distance metrics: Manhattan, Euclidean, and Cosine. Results indicate that pruning by K-Means algorithms using Manhattan distance leads to a 35.15% reduction in parameters and a 49.11% decrease in FLOPs, outperforming Euclidean and Cosine distances using the same algorithm. © 2013 IEEE.Item A Reduced Component Count Self-Balance Quadruple Boost Seventeen-Level Switched Capacitor Inverter(Institute of Electrical and Electronics Engineers Inc., 2024) Ahmed, S.; Raushan, R.; Ahmad, M.W.A switched capacitor multilevel inverter (SCMLI) enables high-quality output voltage waveforms for various industrial and renewable energy applications. SCMLI uses a combination of capacitors and switches to generate multiple voltage levels from a single dc source, thereby reducing the overall cost and size of the system. This article proposes a novel configuration of a 17-level SCMLI. The proposed converter can boost four times the input voltage by exploiting the series-parallel connection of capacitors with the dc voltage source. With simple pulsewidth modulated (PWM) control, the capacitor voltages are inherently balanced under different loading conditions. Furthermore, for 11 switches, only seven independent switching signals are required. Loss analysis reveals that the proposed SCMLI has significantly reduced conduction losses, capacitor ripple voltage, voltage stress, and cost function (CF) when compared with other topologies available in the literature. Finally, the simulation results are obtained at different loads and modulation indexes. The results are experimentally validated with a scaled-down laboratory prototype. © 2024 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item Synergistic effects of natural fibres and agro-waste ash on the engineering and sustainability of stone-matrix asphalt mixes(KeAi Communications Co., 2025) Akarsh, P.K.; Marathe, S.; Sapal, H.K.; Akshaya Krishna, N.This study investigates the use of non-traditional natural fibres, specifically sisal plant fibres (SF) and coconut coir coir fibres (CCF), in Stone Matrix Asphalt (SMA) mixtures. The objective was to evaluate the optimal binder content, assess Marshall properties, and investigate drain-down, indirect tensile strength, fatigue, and rutting characteristics of the SMA mixes. Additionally, the study explores the use of sugarcane bagasse ash (SBA), an agro-waste, as a substitute for Ordinary Portland Cement (OPC), aiming to promote sustainability and waste management optimization. The research identified the optimal SMA mix with a 0.30% fibre dosage and 10% SBA, demonstrating favorable mechanical properties with Marshall stability and tensile strength ratio exceeding 90%, alongside satisfactory rutting and fatigue performance. The results showed that SF and CCF provided comparable, or even superior, performance to traditional cellulose fibres (CF), positioning them as sustainable alternatives for pavement construction. Further, a Life Cycle Cost Analysis (LCCA) was conducted on conventional and modified SMA mixes, revealing substantial long-term economic benefits. Although SMA mixes incurred slightly higher initial costs, their superior durability and reduced maintenance needs resulted in a 13.6% cost reduction for SMA-CCF and 11.1% for SMA-SF over a 20-year period. Environmental assessments confirmed that substituting synthetic fibres and OPC with SF, CCF, and SBA substantially lowered carbon emissions and enhanced sustainability, with reductions in Global Warming Potential of up to 50%. These findings highlight the potential of natural fibres and SBA in reducing costs and environmental impacts, offering a sustainable solution for future pavement construction. © 2025 Tongji University and Tongji University Press