Browsing by Author "Gupta, P."

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A low loss hexagonal six-port optical circulator using silicon photonic crystal
(Springer, 2023) Sangeetha, S.; Vani, D.; Gupta, P.; Krishnan, P.
A 6-port optical circulator using silicon photonic crystals has been designed and proposed in this paper as an essential component of an optical communication system. The proposed 6-port circulator has greater isolation values between the input and isolated ports and lower insertion loss values between the input and output ports. The proposed circulator achieves maximum isolation of 38.7 dB and minimum insertion loss of 0.0029 dB. The proposed designs are extremely useful in the telecommunications industry, wavelength division multiplexing and photonic integrated circuits applications due to their low insertion and high isolations. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Acoustic fluid–structure study of 2D cavity with composite curved flexible walls using graphene platelets reinforcement by higher-order finite element approach
(Elsevier Ltd, 2021) Jeyaraj, J.; Gupta, P.; Vasudevan, V.; Polit, O.; Manickam, G.
In the present study, acousto-vibration analysis of 2D fluid-filled cavities/tanks having flat and curved flexible walls is made using a trigonometric function based shear deformable theory and the Helmholtz wave model for fluid domain. The governing equation formed here is solved through higher-order finite element approach. The walls are modeled by C1 continuous 3-noded beam element and the fluid is idealized using an eight-noded quadrilateral element. Structural and coupled frequencies are evaluated for fluid-filled cavities with rigid/flexible vertical walls along with flat/curved beam on top. The sound pressure level is also predicted in the fluid domain due to a steady-state mechanical harmonic load on the top of the cavity. This investigation is conducted for metallic cavities and then extended to graphene platelets reinforced cavity. The effect of degree of fluid–structure coupling is examined assuming different fluid domains. Considering a wide range of cavity geometry and material parameters such as thickness ratio, curved beam angle, graded porosity and graphene platelets, porosity coefficient, loading of GPL, fluid medium, a comprehensive investigation is depicted to highlight their impacts on vibro-acoustic nature of fluid-filled cavities. It is observed that the dynamic characteristics of rigid and flexible wall cavities are significantly different from each other. © 2021 Elsevier Ltd
Analytic technique for optimal workload scheduling in data-center using phase detection
(2015) Gupta, P.; Koolagudi, S.G.; Khanna, R.; Ganguli, M.; Sankaranarayanan, A.N.
Typically, complex resource-interdependence and heterogeneous workload patterns can result in sub-optimal job allocation leading to performance loss or under-utilization of compute resources. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. For a workload hosting environment, pool of available resources are optimally configured and utilized to sustain certain expectation of Quality-of-Service (QoS) in the presence of power, thermal and reliability constraints. The workload (or job) scheduling mechanism is expected to withstand dynamic variations in demand stresses while maximizing the resource utilization and minimizing the performance loss. Furthermore, workloads can be co-allocated to the clusters with least amount of resource contention. In this paper we introduce the methodology that facilitates the coordinated scheduling of the workloads to the systems with least contentious resources through phase-assisted dynamic characterization. We describe the method to perform optimal job scheduling by using phase model synthesized by learning and classifying the run-time behavior of workloads. � 2015 IEEE.
Analytic technique for optimal workload scheduling in data-center using phase detection
(Institute of Electrical and Electronics Engineers Inc., 2015) Gupta, P.; Koolagudi, S.G.; Khanna, R.; Ganguli, M.; Sankaranarayanan, A.N.
Typically, complex resource-interdependence and heterogeneous workload patterns can result in sub-optimal job allocation leading to performance loss or under-utilization of compute resources. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. For a workload hosting environment, pool of available resources are optimally configured and utilized to sustain certain expectation of Quality-of-Service (QoS) in the presence of power, thermal and reliability constraints. The workload (or job) scheduling mechanism is expected to withstand dynamic variations in demand stresses while maximizing the resource utilization and minimizing the performance loss. Furthermore, workloads can be co-allocated to the clusters with least amount of resource contention. In this paper we introduce the methodology that facilitates the coordinated scheduling of the workloads to the systems with least contentious resources through phase-assisted dynamic characterization. We describe the method to perform optimal job scheduling by using phase model synthesized by learning and classifying the run-time behavior of workloads. Â© 2015 IEEE.
Autonomic characterization of workloads using workload fingerprinting
(2015) Khanna, R.; Ganguli, M.; Narayan, A.; Abhiram, R.; Gupta, P.
In a cloud service management environment, service level agreements (SLA) define the expectation of quality (Quality-of-Service) for managing performance loss in a given service-hosting environment comprising of a pool of compute resources. Typically, complexity of resource inter-dependencies in a server system often results to sub-optimal behaviors leading to performance loss. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. Dynamic characterization methods can synthesize self-correcting workload fingerprint code-book that facilitates phase prediction to achieve continuous tuning through proactive workload-allocation and load-balancing. In this paper we introduce the methodology that facilitates the coordinated tuning of the system resources through phase-assisted dynamic characterization. We describe the method to develop a multi-variate phase model by learning and classifying the run-time behavior of workloads. We demonstrate the workload phase forecasting method using phase extraction using a combination of machine learning approach. Results show the new model is about 98% accurate in phase identification and 97.15% accurate in forecasting the compute demands. � 2014 IEEE.
Autonomic characterization of workloads using workload fingerprinting
(Institute of Electrical and Electronics Engineers Inc., 2015) Khanna, R.; Ganguli, M.; Narayan, A.; Abhiram, R.; Gupta, P.
In a cloud service management environment, service level agreements (SLA) define the expectation of quality (Quality-of-Service) for managing performance loss in a given service-hosting environment comprising of a pool of compute resources. Typically, complexity of resource inter-dependencies in a server system often results to sub-optimal behaviors leading to performance loss. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. Dynamic characterization methods can synthesize self-correcting workload fingerprint code-book that facilitates phase prediction to achieve continuous tuning through proactive workload-allocation and load-balancing. In this paper we introduce the methodology that facilitates the coordinated tuning of the system resources through phase-assisted dynamic characterization. We describe the method to develop a multi-variate phase model by learning and classifying the run-time behavior of workloads. We demonstrate the workload phase forecasting method using phase extraction using a combination of machine learning approach. Results show the new model is about 98% accurate in phase identification and 97.15% accurate in forecasting the compute demands. Â© 2014 IEEE.
Development of a Spectrophotometric Biphasic Assay for the Estimation of mPEG-maleimide in Thiol PEGylation Reaction Mixtures
(2016) Nanda, P.; JagadeeshBabu, P.E.; Gupta, P.; Prasad, A.G.
Methoxy(polyethylene glycol)-maleimide (mPEG-mal) is a PEG derivative used for thiol PEGylation of protein molecules and finds application in drug delivery studies. The maleimide group undergoes degradation in aqueous media, resulting in the difficult quantitative analysis of mPEG-mal. Routinely employed methods for separation and estimation of mPEG-mal include tedious chromatographic methods like ion exchange, high-performance liquid chromatography with refractive index detector and techniques like mass spectrometry and proton nuclear magnetic resonance. We present a direct and reproducible spectrophotometric method to quantify free and protein bound mPEG-mal in thiol PEGylation reaction mixtures. This method is based on the partitioning of a PEG bound chromophore between an aqueous ammonium isoferrothiocyanate phase to a chloroform phase in the presence of mPEG-mal. Several important parameters influencing the partitioning and stability of the chromophore, volume ratios of liquid phases, ethylenediaminetetraacetic acid concentration in the reaction mixture, mixing time, and chlorinated solvents used for partitioning have been studied. 2016, Copyright Taylor & Francis Group, LLC.
Development of a Spectrophotometric Biphasic Assay for the Estimation of mPEG-maleimide in Thiol PEGylation Reaction Mixtures
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2016) Nanda, P.; JagadeeshBabu, P.E.; Gupta, P.; Prasad, A.G.
Methoxy(polyethylene glycol)-maleimide (mPEG-mal) is a PEG derivative used for thiol PEGylation of protein molecules and finds application in drug delivery studies. The maleimide group undergoes degradation in aqueous media, resulting in the difficult quantitative analysis of mPEG-mal. Routinely employed methods for separation and estimation of mPEG-mal include tedious chromatographic methods like ion exchange, high-performance liquid chromatography with refractive index detector and techniques like mass spectrometry and proton nuclear magnetic resonance. We present a direct and reproducible spectrophotometric method to quantify free and protein bound mPEG-mal in thiol PEGylation reaction mixtures. This method is based on the partitioning of a PEG bound chromophore between an aqueous ammonium isoferrothiocyanate phase to a chloroform phase in the presence of mPEG-mal. Several important parameters influencing the partitioning and stability of the chromophore, volume ratios of liquid phases, ethylenediaminetetraacetic acid concentration in the reaction mixture, mixing time, and chlorinated solvents used for partitioning have been studied. © 2016, Copyright © Taylor & Francis Group, LLC.
Evaluation of a comprehensive non-toxic, biodegradable and sustainable cutting fluid developed from coconut oil
(SAGE Publications Ltd, 2021) Suvin, P.S.; Gupta, P.; Horng, J.-H.; Kailas, S.V.
The evolution in development of cutting fluid from petroleum based products have brought about remarkable changes to the present and growing machining industry. Most of the cutting fluids are made of mineral base oils which are toxic, non-biodegradable and unsustainable. A major issue lies in their inappropriate disposal which results in surface water and groundwater contamination and consequently, agricultural products and food contamination. Hence, the objective of this study is to develop an alternative, sustainable, non- toxic and completely bio-degradable cutting fluid to replace the mineral oil based cutting fluid. A Green cutting fluid [GCF] was prepared by combining nontoxic emulsifiers and natural additives. GCF meets many of the characteristic specifications of commercial formulations with the added advantage that it is eco-friendly. Toxicity test for cutting fluids has been carried out using fish toxicity test (OECD -203). The GCF with green additives has an LC50 value ?1064 mg/L. Commercial cutting fluid (CCF) has an LC50 value less than 100 mg/L These tests show that commercial cutting fluids are highly toxic, while the GCF can be considered as non-toxic. Biodegradability test was done using BOD-COD technique and found GCF as biodegradable and CCF as non-biodegradable. The ASTM D4627 corrosion tests infer that the GCF with grade 3 has better anticorrosive characteristics when compared to grade 4 of most CCF samples tested this could be possibly by the effect of natural additives in GCF. Drilling experiments were carried out to evaluate the machining performance of cutting fluids. Results from the drilling tests comparing the axial force/cutting force and torque showed that the performance of the GCF was comparable to that of the CCFs. Nevertheless, GCF formulation with non- toxic emulsifiers and natural additives is a good basis for further development and use of non-toxic tribological products. © IMechE 2020.
High figure-of-merit in Zn, Sb co-doped Mg2Si0.3Sn0.7 alloy through simultaneous optimization of electrical and thermal transports
(Elsevier Ltd, 2025) Sarkar, P.; Gupta, P.; Shenoy, U.S.; Singh, S.; Kundu, S.; Kumawat, N.; Kedia, D.K.; Bhat, D.K.; Bhattacharya, S.; Singh, A.
The derivatives of Mg2Si have recently attracted wide attention as promising thermoelectric materials due to earth abundant and environment friendly low-cost constituents. The main challenge in optimizing the thermoelectric figure of merit ZT, is the low electrical and high thermal conductivities of Mg2Si. The present study demonstrates high ZT of ?1.55 at 673 K in Mg2Si0.3Sn0.7 through simultaneous optimization of electrical and thermal transport through Sb and Zn co-doping. The ultra-low deformation and alloy scattering potentials in Sb and Zn co-doped samples helps in maintaining record high Hall mobility ?70–90 cm2/V.s. The doping induced pudding mold band structure with hyperconvergence in conduction band balances high Seebeck coefficient and high electrical conductivity. The point defects and dislocations created by doping helps in lowering of lattice thermal conductivity as well. The uni-leg power generator fabricated using optimized Mg1.96Zn0.04(Si0.3Sn0.7)0.98Sb0.02 exhibits a record efficiency of ?9.5 % at ?T ? 329 K. © 2025
Investigating the "wisdom of crowds" at scale
(2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Investigating the "wisdom of crowds" at scale
(Association for Computing Machinery, Inc acmhelp@acm.org, 2015) Mysore, A.S.; Yaligar, V.S.; Ibarra, I.A.; Simoiu, C.; Goel, S.; Arvind, R.; Sumanth, C.; Srikantan, A.; Bhargav, H.S.; Pahadia, M.; Dobhal, T.; Ahmed, A.; Shankar, M.; Agarwal, H.; Agarwal, R.; Anirudh-Kondaveeti, S.; Arun-Gokhale, S.; Attri, A.; Chandra, A.; Chilukuri, Y.; Dharmaji, S.; Garg, D.; Gupta, N.; Gupta, P.; Jacob, G.M.; Jain, S.; Joshi, S.; Khajuria, T.; Khillan, S.; Konam, S.; Kumar-Kolla, P.; Loomba, S.; Madan, R.; Maharaja, A.; Mathur, V.; Munshi, B.; Nawazish, M.; Neehar-Kurukunda, V.; Nirmal-Gavarraju, V.; Parashar, S.; Parikh, H.; Paritala, A.; Patil, A.; Phatak, R.; Pradhan, M.; Ravichander, A.; Sangeeth, K.; Sankaranarayanan, S.; Sehgal, V.; Sheshan, A.; Shibiraj, S.; Singh, A.; Singh, A.; Sinha, P.; Soni, P.; Thomas, B.; Tuteja, L.; Varma-Dattada, K.; Venkataraman, S.; Verma, P.; Yelurwar, I.
In a variety of problem domains, it has been observed that the aggregate opinions of groups are often more accurate than those of the constituent individuals, a phenomenon that has been termed the "wisdom of the crowd." Yet, perhaps surprisingly, there is still little consensus on how generally the phenomenon holds, how best to aggregate crowd judgements, and how social influence affects estimates. We investigate these questions by taking a meta wisdom of crowds approach. With a distributed team of over 100 student researchers across 17 institutions in the United States and India, we develop a large-scale online experiment to systematically study the wisdom of crowds effect for 1,000 different tasks in 50 subject domains. These tasks involve various types of knowledge (e.g., explicit knowledge, tacit knowledge, and prediction), question formats (e.g., multiple choice and point estimation), and inputs (e.g., text, audio, and video). To examine the effect of social influence, participants are randomly assigned to one of three different experiment conditions in which they see varying degrees of information on the responses of others. In this ongoing project, we are now preparing to recruit participants via Amazon's Mechanical Turk.
Mineral identification using unsupervised classification from hyperspectral data
(2020) Gupta, P.; Venkatesan, M.
Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset. � Springer Nature Singapore Pte Ltd 2020.
Mineral identification using unsupervised classification from hyperspectral data
(Springer, 2020) Gupta, P.; Venkatesan, M.
Hyperspectral imagery is one of the research areas in the field of remote sensing. Hyperspectral sensors record reflectance of object or material or region across the electromagnetic spectrum. Mineral identification is an urban application in the field of remote sensing of Hyperspectral data. Challenges with the hyperspectral data include high dimensionality and size of the hyperspectral data. Principle component analysis (PCA) is used to reduce the dimension of data by band selection approach. Unsupervised classification technique is one of the hot research topics. Due to the unavailability of ground truth data, unsupervised algorithm is used to classify the minerals present in the remotely sensed hyperspectral data. K-means is unsupervised clustering algorithm used to classify the mineral and then further SVM is used to check the classification accuracy. K-means is applied to end member data only. SVM used k-means result as a labelled data and classify another set of dataset. Â© Springer Nature Singapore Pte Ltd 2020.
NITK-KLESC: Kannada Language Emotional Speech Corpus for Speaker Recognition
(Institute of Electrical and Electronics Engineers Inc., 2023) Tomar, S.; Gupta, P.; Koolagudi, S.G.
This work introduces an emotional speech dataset for Speaker Recognition (SR) task. The proposed dataset is recorded in the Kannada language from the people of Karnataka state of India. The speech dataset is collected by simulating five different emotions, such as Fear, Sad, Anger, Happy, and Neutral. The dataset is named as National Institute of Technology Karnataka, India- Kannada Language Emotional Speech Corpus (NITK-KLESC). The proposed dataset will be useful for SR tasks in various emotions. The proposed emotional speech dataset will be useful for emotion recognition, analysis of emotional speech, speech recognition, gender identification, and age identification of the age group 20 to 50 years. The proposed work describes the development, processing, analysis, acquisition, and evaluation of the proposed emotional speech dataset (NITK-KLESC). The analysis of emotional speech was done by considering various basic speech parameters like Pitch, Tempo, Intensity, and Zero Crossing Rate (ZCR). The characteristics of the dataset are reported using MFCC feature extraction and considered the CNN model as a classifier, compared with the existing EmoDB dataset. The average accuracy of the Emotional Speech Speaker Recognition (ESSR) task was measured at 84.44% with the EmoDB dataset and 95.2% with the proposed NITK-KLESC dataset. Â© 2023 IEEE.
Performance of eco-friendly mortar mixes against aggressive environments
(Techno-Press info@techno-press.com, 2020) Saha, S.; C, C.; Gupta, P.
Past research efforts already established geopolymer as an environment-friendly alternative binder system for ordinary Portland cement (OPC) and recycled aggregate is also one of the promising alternative for natural aggregates. In this study, an effort was made to produce eco-friendly mortar mixes using geopolymer as binder and recycled fine aggregate (RFA) partially and study the resistance ability of these mortar mixes against the aggressive environments. To form the geopolymer binder, 70% fly ash, 30% ground granulated blast furnace slag (GGBS) and alkaline solution comprising of sodium silicate solution and 14M sodium hydroxide solution with a ratio of 1.5 were used. The ratio of alkaline liquid to binder (AL/B) was also considered as 0.4 and 0.6. In order to determine the resistance ability against aggressive environmental conditions, acid attack test, sulphate attack test and rapid chloride permeability test were conducted. Change in mass, change in compressive strength of the specimens after the immersion in acid/sulphate solution for a period of 28, 56, 90 and 120 days has been presented and discussed in this study. Results indicated that the incorporation of RFA leads to the reduction in compressive strength. Even though strength reduction was observed, eco-friendly mortar mixes containing geopolymer as binder and RFA as fine aggregate performed better when it was produced with AL/B ratio of 0.6. © 2020 Techno-Press, Ltd.