arXiv subjects

Manasi Kulkarni

Publications and source records attributed to Manasi Kulkarni.

3 recordsLinked to original sources

arXiv2026

Exploration of UV-bright sources in Globular cluster NGC 3201 using UVIT observations

We present a comprehensive study of the ultraviolet (UV)-bright stellar population in the globular cluster NGC 3201 using the Ultraviolet Imaging Telescope (UVIT) onboard AstroSat. By combining far-UV photometry of the UVIT F148W and F169M filters with Gaia and ground-based telescope optical data, we construct UV$-$optical color-magnitude diagrams (CMDs) to identify UV-bright stellar evolutionary phases. Physical parameters of all detected sources are derived by fitting stellar atmosphere models to the observed multi-wavelength spectral energy distributions (SEDs). The effective temperatures of blue horizontal branch stars (BHBs) span the range 7250$-$11250 K. We identify two extreme horizontal-branch stars (EHBs) with effective temperatures of approximately 25,000 K and 17,000 K; one lies within the cluster core radius ($r_\mathrm{c}$=1.3$'$), while the other is located outside. We also detect two candidate ``gap'' objects in the transitional phase between HB stars and white dwarfs, which may represent hot blue hook (BHk) stars. For the blue straggler stars (BSSs), we determine effective temperatures, radii, and luminosities through SED fittings, and estimate their masses and isochrone-equivalent ages using PARSEC isochrones. Finally, we studied the normalized cumulative radial distribution of the observed BHB stars, BSSs, MS stars, RGB stars, and variable stars along with the specific frequency of BSSs across the cluster. These results provide new insights into the late-stage stellar evolution and dynamical processes in NGC 3201.

astro-ph.GA↗

arXiv2024

Cyberbully and Online Harassment: Issues Associated with Digital Wellbeing

As digital technology becomes increasingly embedded in daily life, its impact on social interactions has become a critical area of study, particularly concerning cyberbullying. This meta-analysis investigates the dual role of technology in cyberbullying both as a catalyst that can exacerbate the issue and as a potential solution. Cyberbullying, characterized by the use of digital platforms to harass, threaten, or humiliate individuals, poses significant challenges to mental and social wellbeing. This research synthesizes empirical findings from diverse studies to evaluate how innovative technological interventions, such as content monitoring algorithms, anonymous reporting systems, and educational initiatives integrated within digital platforms, contribute to reducing the prevalence of cyberbullying. The study focuses on the effectiveness of these interventions in various settings, highlighting the need for adaptive strategies that respond to the dynamic digital landscape. By offering a comprehensive overview of the current state of cyberbullying and the efficacy of technology based solutions, this analysis provides valuable insights for stakeholders, including educators, policymakers, and technology developers, aiming to enhance digital wellbeing and create safer online environments. The findings underscore the importance of leveraging technology not only as a medium of communication but also as a strategic tool to combat the negative impacts of cyberbullying, thus promoting a more inclusive and respectful digital world.

cs.CY↗

arXiv2018

Lip Reading Using Convolutional Auto Encoders as Feature Extractor

Visual recognition of speech using the lip movement is called Lip-reading. Recent developments in this nascent field uses different neural networks as feature extractors which serve as input to a model which can map the temporal relationship and classify. Though end to end sentence level Lip-reading is the current trend, we proposed a new model which employs word level classification and breaks the set benchmarks for standard datasets. In our model we use convolutional autoencoders as feature extractors which are then fed to a Long short-term memory model. We tested our proposed model on BBC's LRW dataset, MIRACL-VC1 and GRID dataset. Achieving a classification accuracy of 98% on MIRACL-VC1 as compared to 93.4% of the set benchmark (Rekik et al., 2014). On BBC's LRW the proposed model performed better than the baseline model of convolutional neural networks and Long short-term memory model (Garg et al., 2016). Showing the features learned by the models we clearly indicate how the proposed model works better than the baseline model. The same model can also be extended for end to end sentence level classification.

cs.CV↗