Shiv Nadar University

About: Shiv Nadar University is a education organization based out in Dadri, Uttar Pradesh, India. It is known for research contribution in the topics: Population & Graphene. The organization has 1015 authors who have published 1924 publications receiving 18420 citations.

An IoT-Based Vehicle Accident Detection and Classification System Using Sensor Fusion

Abstract: Road accidents are a leading cause of death and disability among youth. Contemporary research on accident detection systems is focused on either decreasing the reporting time or improving the accuracy of accident detection. Internet-of-Things (IoT) platforms have been utilized considerably in recent times to reduce the time required for rescue after an accident. This work presents an IoT-based automotive accident detection and classification (ADC) system, which uses the fusion of smartphone’s built-in and connected sensors not only to detect but also to report the type of accident. This novel technique improves the rescue efficacy of various emergency services, such as emergency medical services (EMSs), fire stations, towing services, etc., as knowledge about the type of accident is extremely valuable in planning and executing rescue and relief operations. The emergency assistance providers can better equip themselves according to the situation after making an inference about the injuries sustained by the victims and the damage to the vehicle. In this work, three machine learning models based on Naive Bayes (NB), Gaussian mixture model (GMM), and decision tree (DT) techniques are compared to identify the best ADC model. Five physical parameters related to vehicle movement, i.e., speed, absolute linear acceleration (ALA), change-in-altitude, pitch, and roll, have been used to train and test each candidate ADC model to identify the correct class of accident among collision, rollover, falloff, and no accident. NB-based ADC model is found to be highly accurate with 0.95 mean F1-score.

Comparative transcriptome meta-analysis of Arabidopsis thaliana under drought and cold stress

Abstract: Multiple environmental stresses adversely affect plant growth and development. Plants under multiple stress condition trigger cascade of signals and show response unique to specific stress as well as shared responses, common to individual stresses. Here, we aim to identify common and unique genetic components during stress response mechanisms liable for cross-talk between stresses. Although drought and cold stress have been widely studied, insignificant information is available about how their combination affects plants. To that end, we performed meta-analysis and co-expression network comparison of drought and cold stress response in Arabidopsis thaliana by analyzing 390 microarray samples belonging to 29 microarray studies. We observed 6120 and 7079 DEGs (differentially expressed genes) under drought and cold stress respectively, using Rank Product methodology. Statistically, 28% (2890) DEGs were found to be common in both the stresses (i.e.; drought and cold stress) with most of them having similar expression pattern. Further, gene ontology-based enrichment analysis have identified shared biological processes and molecular mechanisms such as—‘photosynthesis’, ‘respiratory burst’, ‘response to hormone’, ‘signal transduction’, ‘metabolic process’, ‘response to water deprivation’, which were affected under cold and drought stress. Forty three transcription factor families were found to be expressed under both the stress conditions. Primarily, WRKY, NAC, MYB, AP2/ERF and bZIP transcription factor family genes were highly enriched in all genes sets and were found to regulate 56% of common genes expressed in drought and cold stress. Gene co-expression network analysis by WGCNA (weighted gene co-expression network analysis) revealed 21 and 16 highly inter-correlated gene modules with specific expression profiles under drought and cold stress respectively. Detection and analysis of gene modules shared between two stresses revealed the presence of four consensus gene modules.

Furan substituted diketopyrrolopyrrole and thienylenevinylene based low band gap copolymer for high mobility organic thin film transistors

Abstract: A novel solution processable donor–acceptor (D–A) based low band gap polymer semiconductor poly{3,6-difuran-2-yl-2,5-di(2-octyldodecyl)-pyrrolo[3,4-c]pyrrole-1,4-dione-alt-thienylenevinylene} (PDPPF-TVT), was designed and synthesized by a Pd-catalyzed Stille coupling route An electron deficient furan based diketopyrrolopyrrole (DPP) block and electron rich thienylenevinylene (TVT) donor moiety were attached alternately in the polymer backbone The polymer exhibited good solubility, film forming ability and thermal stability The polymer exhibits wide absorption bands from 400 nm to 950 nm (UV-vis-NIR region) with absorption maximum centered at 782 nm in thin film The optical band gap (Eoptg) calculated from the polymer film absorption onset is around 137 eV The π-energy band level (ionization potential) calculated by photoelectron spectroscopy in air (PESA) for PDPPF-TVT is around 522 eV AFM and TEM analyses of the polymer reveal nodular terrace morphology with optimized crystallinity after 200 °C thermal annealing This polymer exhibits p-channel charge transport characteristics when used as the active semiconductor in organic thin-film transistor (OTFT) devices The highest hole mobility of 013 cm2 V−1 s−1 is achieved in bottom gate and top-contact OTFT devices with on/off ratios in the range of 106–107 This work reveals that the replacement of thiophene by furan in DPP copolymers exhibits such a high mobility, which makes DPP furan a promising block for making a wide range of promising polymer semiconductors for broad applications in organic electronics

Soft-Switched High Voltage Gain Boost-Integrated Flyback Converter Interfaced Single-Phase Grid-Tied Inverter for SPV Integration

Abstract: A two-stage grid-tied efficient solar photovoltaic (SPV) system with zero voltage switching (ZVS) and zero current switching (ZCS) at the dc–dc converter stage and adaptive synchronization at the inverter stage is proposed. In the first stage, an improved high step-up boost-integrated flyback converter with quasi resonant voltage multiplier cell is proposed. ZVS turn-on of all mosfet s and ZCS turn-off of all diodes of resonant voltage multiplier cell results in high efficient power conversion compared with the conventional boost-integrated flyback converters. Small-sized coupled inductor enhances magnetic utilization in this converter. Second stage consists of a single-phase H-bridge grid interfaced inverter with variable band hysteresis current control technique. Amplitude adaptive notch filter is used to extract fundamental unit voltage vector of grid voltage required for estimation of synchronized reference current for hysteresis controller. A 250 W laboratory prototype is developed and experimentally evaluated. Experimental results demonstrate efficient renewable energy conversion at dc–dc converter stage and quality power injection at dc–ac inverter stage of the proposed grid-tied SPV system under changing solar insulation levels.