7 research outputs found
Thermal and electrical transport properties of polyvinyl alcohol and bismuth ferrite nanocomposites film
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Passive Target Motion Analysis With Own-Ship Location Uncertainty in the Presence of Non-Gaussian Sensor Noise
No full textPublisher Copyright: © 2025 The Author(s). IET Radar, Sonar & Navigation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Passive target motion analysis (TMA) is traditionally performed using angle-only measurements, which requires the own-ship to execute a manoeuvre to make the tracking system observable. These manoeuvres are burdensome for the naval community. In contrast, this work explores underwater TMA by incorporating time delay and Doppler frequency measurements along with angle data, eliminating the need for own-ship manoeuvre and improving estimation accuracy. Measurement noises are assumed to follow a non-Gaussian distribution, and maximum correntropy (MC)-based Bayesian filtering framework is adopted to solve the problem. Furthermore, the own-ship's position is inherently uncertain due to navigation errors, and this work addresses the uncertainty by modifying the measurement noise covariance matrix within the estimation framework. Simulation results demonstrate that the proposed methodology achieves improved tracking performance in terms of root mean square error (RMSE) and (Formula presented.) track loss compared to existing state-of-the-art MC Kalman filtering approaches.Peer reviewe
dAJC: A 2.02mW 50Mbps Direct Analog to MJPEG Converter for Video Sensor Node using Low-Noise Switched Capacitor MAC-Quantizer with Auto-Calibration and Sparsity-Aware ADC
No full textWith the advancement in the field of the Internet of Things(IoT) and Internet of Bodies(IoB), video camera applications using Video Sensor Nodes(VSNs) have gained importance in the field of autonomous driving, health monitoring, robot control, and security camera applications. However, these applications typically involve high data rates due to the transmission of high-resolution video signals, resulting from high data volume generated from the analog-to-digital converters (ADCs). This significant data deluge poses processing and storage overheads, exacerbating the problem. To address this challenge, we propose a low-power solution aimed at reducing the power consumption in Video Sensor Nodes (VSNs) by shifting the computation from the digital domain to the inherently energy-efficient analog domain. Unlike standard architectures where computation and processing are typically performed in digital signal processing (DSP) blocks after the ADCs, our approach eliminates the need for such blocks. Instead, we leverage a switched capacitor-based computation unit in the analog domain, resulting in a reduction in power consumption. We achieve a reduction in power consumption compared to digital implementations. Furthermore, we employ a sparsity-aware ADC, which is enabled only for significant compressed samples that contribute to a small fraction () of the total captured analog samples, we achieve a lower ADC conversion energy without any considerable degradation, contributing to the overall energy savings in the system.15 pages, 25 Figures, First publication: Custom Integrated Circuits Conference 2023, 6 author
Knowledge and perception regarding research among undergraduate medical students: a cross sectional study in Nilratan Sircar Medical college, Kolkata
No full textAbstract:
Background: Amendments in MBBS curriculum is increasing focus of research orientation of Indian Medical Graduates, still there is lack of research projects conducted by undergraduate students and a prominent deficit of evidence regarding their perception towards in research especially in west Bengal.
Aim/Objective: To describe the knowledge and perception regarding research and their socio-demographic correlates among Phase 1 and Phase 2 undergraduate students of NRS Medical College and Hospital, during the year 2023
Results: Majority of the students were aged between 20 and 21 years (49.3%), male (67%), Hindu (82%), from nuclear families (80.9%) and belonged to the upper class according to modified B.G. Prasad Scale (69.3%). Students have varying degree of knowledge regarding various steps of conducting research, research organisations across India, however, knowledge among phase I students was less than among phase II students. Median score regarding importance of research in academics and clinical practice was 8 (IQR:6-9) and for importance of involvement in research & motivation towards research was 7 (IQR:5-9). Median motivation score, reflective of the level of motivation, was only significantly different across sex, where females feel less motivated than males. As perceived by the study population, lack of adequate training come out as the biggest barrier in doing research.
Conclusion: Undergraduate medical students are motivated to conduct research but they are deficient in knowledge regarding the nuances of conducting the research mostly due to lack of time in their busy curriculum and lack of adequate training
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Neurosurgery Research & Education Foundation Medical Student Summer Research Fellowship applicant trends and impact on future career trajectory
No full textThe Neurosurgery Research & Education Foundation (NREF) Medical Student Summer Research Fellowship (MSSRF) is a prominent research fellowship offered to medical students. The authors investigated how gender and academic characteristics of the MSSRF applicant pool have evolved since the fellowship's inception. Likewise, they evaluated the impact of the MSSRF on career progression, scholarly productivity, and subsequent grant funding within neurosurgery.A list of MSSRF awardees (2008-2023) and nonawardee applicants (2015-2023) was provided by the NREF. Demographic and career progression variables were obtained through publicly available platforms, and scholarly productivity metrics were collected using Clarivate Web of Science. The Fisher's exact test was used to compare categorical variables, the Mann-Whitney U-test was used to compare continuous variables, and the Mann-Kendall test was used to assess trends. Binary logistic regression was utilized to explore factors associated with matching into neurosurgery.A total of 297 awardees from 2008 to 2023, 183 awardees from 2015 to 2023, and 355 nonawardees from 2015 to 2023 were included. A greater percentage of awardees attended a top 20 medical school than nonawardees (p = 0.002). There was a statistically significant upward trend in the percentage of female awardees since 2010 (p = 0.01). Between 2015 and 2023, there was no difference in the percentage of awardees who matched into neurosurgery compared to nonawardees (60.5% vs 50.2%, p = 0.07), but awardees matched into better Doximity-ranked neurosurgery residency programs (p = 0.04). While there was no difference in the number of total publications or first author publications before residency between awardees and nonawardees who matched into neurosurgery since 2015, awardees had a higher h-index (5.0 vs 4.0, p = 0.03). Specifically among awardees who pursued neurosurgery since 2008, there was a statistically significant upward trend in the median number of total publications before residency (p < 0.001), first author publications (p = 0.001), and h-index (p = 0.007). Among neurosurgery attending physicians who received MSSRF awards, 64.7% practiced in an academic setting. Across academic neurosurgery attending physicians who received MSSRF awards, the ratio of NREF MSSRF award dollars to subsequent National Institutes of Health (NIH) grant funding dollars was 9.05.The NREF MSSRF is associated with high-quality research and strong academic productivity among aspiring medical students, with a high proportion of awardees pursuing neurosurgery and matching into top-ranked residency programs. Likewise, this early-career fellowship has a substantial return on investment in terms of subsequent NIH grant funding
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Exploring the landscape of machine learning applications in neurosurgery: A bibliometric analysis and narrative review of trends and future directions
No full textThe field of neurosurgery has consistently represented an area of innovation and integration of technology since its inception. As such, machine learning (ML) has found its way into applications within neurosurgery relatively rapidly. Through this bibliometric review and cluster analysis, we seek to identify trends and emerging applications of ML within neurosurgery.
A bibliometric analysis was carried out in the Web of Science database from January 2000 to March 2023. The full dataset of the 200 most cited publications including title, author information, journal, citation count, keywords, and abstracts for each publication was evaluated in CiteSpace. CiteSpace was used to elucidate publication characteristics, trends, and topic clusters via collaborate network analysis using the Kamada-Kawai algorithm.
The 25 most cited titles were included in our analysis. Harvard University and its affiliates represented the top institution, contributing nearly 25% of publications in the literature. World Neurosurgery was the journal with the highest net citation count of 747 (29%). Collaborative network analysis generated 12 unique clusters, the largest of which was machine learning, followed by feature importance, and deep brain stimulation.
This review highlights the most impactful articles pertaining to ML in the field of neurosurgery. ML has been applied into several sub-specialties within neurosurgery to optimize patient care, with special attention to outcome predictors, patient selection, and surgical decision making
