Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Dynamics of strongly coupled fluid-filled micro-cavities and PMUTs in integrated microfluidic devices(American Society of Mechanical Engineers (ASME) infocentral@asme.org, 2016) Dangi, A.; Singh, R.; Deshmukh, D.; Pratap, R.In this work, we present a novel device developed by integration of an array of Piezoelectric Micromachined Ultrasonic Transducers (PMUTs) with a microfluidic chip that can be used for characterizing the acoustical properties of the liquid present in the back-cavity of the PMUT. PMUT membrane operates in flexural mode of vibration and it is directly coupled with the cylindrical back-cavity formed during the release of the PMUT membrane. This leads to very strong structural-acoustic coupling between the PMUT and the liquid present in the its back-cavity. Presence of fluid around the thin PMUT membrane causes a significant reduction in the resonant frequencies of the PMUT due to mass loading imposed by the surrounding fluid. It also leads to the excitation of the acoustic modes of the cylindrical back-cavity when the PMUT vibrates near the fundamental acoustic frequencies of the cavity. These acoustic reverberations appear in the vibration response of the PMUT in form of additional resonant peaks. Further we explore the feasibility of capturing the acoustic signature of microbubbles introduced in the backcavity liquid. Microbubbles are generated on the microfluidic chip using flow focusing technique and introduced in the cylindrical back-cavity of the PMUT through a network of channels and wells made on PDMS and adhered to the PMUT from the backside. This approach can provide an alternative method for on-chip characterization of microbubbles. © © 2016 by ASME.Item Estimating multiple physical parameters from speech data(IEEE Computer Society help@computer.org, 2016) Kalluri, S.B.; Vijayakumar, A.; Vijayasenan, D.; Singh, R.In this work, we explore prediction of different physical parameters from speech data. We aim to predict shoulder size and waist size of people from speech data in addition to the conventional height and weight parameters. A data-set with this information is created from 207 volunteers. A bag of words representation based on log magnitude spectrum is used as features. A support vector regression predicts the physical parameters from the bag of the words representation. The system is able to achieve a root mean square error of 6.6 cm for height estimation, 2.6cm for shoulder size, 7.1cm for waist size and 8.9 kg for weight estimation. The results of height estimation is on par with state of the art results. © 2016 IEEE.Item Micro-Architectural support for High Availability of NoC-based MP-SoC(Institute of Electrical and Electronics Engineers Inc., 2019) Singh, R.; Ranga, S.V.; Patil, S.; Krishna, M.; Mehta, M.; Anoop, M.N.; Nandy, S.K.; Haldar, C.; Narayan, R.; Neumann, F.; Baufreton, P.In this paper, we focus on increasing the availability of Multi-Processor System on Chip (MP-SoC) for executing user applications, even when some components of the system are faulty. A Network-on-Chip (NoC) provides high bandwidth communication substrate for the multitude of components/modules in such MP-SoCs. Health of such MP-SoC, and hence its availability, is largely dependent on the health of the NoC. We consider an NoC comprising a bidirectional toroidal mesh interconnection of routers. We use a distributed built-in-self-test to identify faulty communication links. We use information so obtained to determine healthy subsystems that can be made available for executing user applications. This feature is key for enhancing availability of MP-SoCs. We realize this feature as a micro-architectural enhancement in MP-SoC that incurs an insignificant hardware overhead of less than 2%. Latency incurred for analyzing availability of MP-SoC is also insignificant. We functionally validate our proposal by emulating the system on a FPGA device and demonstrate increase in availability of the MP-SoC. © 2019 IEEE.Item BEV-MoSeg: Segmenting Moving Objects in Bird's Eye View(Institute of Electrical and Electronics Engineers Inc., 2023) Sigatapu, A.K.; Satagopan, V.; Sistu, G.; Singh, R.; Narasimhadhan, A.V.Accurate detection of moving objects plays a vital role in motion planning and vehicle maneuvering for autonomous vehicles. Though there is a significant improvement in perception tasks like object detection and semantic segmentation by adopting Bird's Eye View (BEV) based techniques like LiftSplatShoot, SimpleBEV etc., the moving object segmentation has gained limited attention. This research addresses this gap and propose a novel end-to-end architecture that implicitly utilizes temporal cues like optical flow in BEV space by correlation or cross-attention for moving vehicle segmentation. This work also introduces custom labels to annotate moving objects in the nuScenes dataset, enhancing its utility for the BEV motion segmentation task. We achieved an Moving Vehicle IoU Score of 26% on nuScenes dataset on full six camera rig and 22% on single front camera. The code for generating these labels and the qualitative results of our model can be found in, Project page with code: https://ajayrafa25.github.io/BEV-MoSeg/ © 2023 IEEE.Item Theoretical Investigation of BC3Monolayer for the Electrode of Na-ion Batteries(Institute of Electrical and Electronics Engineers Inc., 2024) Vudumula, K.; Jasil, T.K.; Yadav, A.K.; Singh, R.; Vinturaj, V.P.; Pandey, S.K.Recently, the Boron Carbide (BC3) material has gained more attention as the electrode of Li/Na ion batteries due to its ability to store lithium or sodium metal without ion clustering and phase separation. In this work, using the Quantum ESPRESSO tool, density functional theory (DFT) calculations were carried out to perform the structural and electrical properties of the BC3 monolayer material. The lattice parameters were optimized to achieve the minimum energy structure for further calculation of band structure, the density of states and dielectric constants in the pristine and Na-adsorbed on the 2 ∗ 2 ∗ 1 BC3 monolayer. The obtained minimum energy value is -330.302 Ry for the pristine BC3 monolayer, where as for Na- adsorbed BC3 the obtained minimum energy is -417.485. Additionally, the pristine and Na-adsorbed BC3 reveal semiconducting nature (indirect band gap 0.43 eV) and metallic nature respectively. Our study demonstrates that the BC3 monolayer has prominent potential for its application as the electrode of Na-ion batteries. © 2024 IEEE.Item Leveraging Hybrid Modeling for Enhanced Runtime Prediction in Big Data Jobs(Institute of Electrical and Electronics Engineers Inc., 2024) Singh, R.; Zadokar, V.N.; Kumar, S.; Doddamani, S.S.; Bhowmik, B.In an era of rapid data expansion, big data has significantly transformed various industries, redefining the processes of data processing, analysis, and utilization. The widespread adoption of digital technologies has driven this surge in big data, leading to an unprecedented accumulation of information from sources such as social media, sensors, and transactions. As big data evolves, it presents significant challenges and unique opportunities, necessitating innovative solutions to leverage its potential fully. One critical challenge in big data environments is accurately predicting job runtimes, essential for optimizing resource utilization and enhancing overall system performance. Current approaches, including analytical models and machine learning algorithms, often need help to manage the complexities of unstructured data and maintain interpretability effectively. This paper proposes a novel hybrid modeling approach that integrates the strengths of both techniques to improve job runtime predictions. The hybrid architecture combines an analytical model, which captures the intricate characteristics of jobs and execution environments, with a machine learning model trained to detect patterns and relationships in historical data. As demonstrated on real-world big datasets, the hybrid model achieves greater accuracy by merging these capabilities. Utilizing the flexible capabilities of PySpark and incorporating advanced feature engineering techniques, the model dynamically adapts to various dataset sizes and complexities, ensuring robust performance across different scenarios. © 2024 IEEE.