IEEE Kharagpur Section

The Pre-University STEM event on "Emerging Technologies and Career Opportunities" is organized by the IEEE YP AG Kharagpur Section in collaboration with the IEEE Kharagpur Section and the Global Institute of Science and Technology, Haldia. Speaker(s): Mr. Aditya Kameswara Rao Nandula Agenda: 1) Introduction to the speaker 2) "Emerging Technologies and Career Opportunities" talk by speaker Mr. Aditya Kameswara Rao Nandula. 3) Q & A Session 4) Concluding remarks by Mr. Somesubra Panda, Senior Lecturer, GIST, Haldia 5) End of session Virtual: https://events.vtools.ieee.org/m/483953

In the realm of label-free imaging techniques, coherent Raman imaging (CRI) emerges as a powerful tool, offering sub-cellular spatial resolution, molecular-specific contrast, and addressing the unmet need in life sciences for label-free chemically specific imaging by detecting the intrinsic vibrational fingerprints of cells and tissues . Multiplex stimulated Raman scattering (SRS) microscopy , combining single-shot detection of broad vibrational spectra and high spectral resolution, fully exploits the innovative potential of CRI tools. State of the art implementations of multiplex SRS systems are based on custom and complex solutions, rendering them completely inaccessible to non-specialists in the field . Here, we present a fully engineered Broadband Coherent Raman Platform – CORAL designed to achieve state-of-the-art performance in multiplex SRS with unprecedented ease of use and long-term reproducibility. CORAL comprises an all-fiber dual-wavelength self-synchronized laser and a detection unit based on a compact multichannel lock-in amplifier, ensuring shot-noise-limited SRS performance over the entire CH spectrum (2800-3100 cm⁻¹), parallelizing detection across 38 spectral channels in 2 μs. Additionally, the system is equipped with an epi-detection module for TPEF and SHG signals. Moreover, CORAL combines a broadband label-free approach for chemometric analysis of biological specimens with artificial intelligence tools, enabling users to unleash the full power of hyperspectral data. Such a system finds broad application in biomedical sectors where traditional exogenous labeling is a limiting factor, such as in live cell imaging, metabolomics, and histopathology. Zhang, C. & Cheng, J.-X. Perspective: Coherent Raman scattering microscopy, the future is bright. APL Photonics vol. 3 090901 (2018). Fu, D. et al. Quantitative Chemical Imaging with Multiplex Stimulated Raman Scattering Microscopy. Journal of the American Chemical Society vol. 134 3623–3626 (2012). De la Cadena, A. et al. Broadband stimulated Raman imaging based on multi-channel lock-in detection for spectral histopathology. APL Photonics vol. 7 076104 (2022). Speaker(s): Dr Matteo Negro Virtual: https://events.vtools.ieee.org/m/482302

Brain stimulation shows significant potential in treating neurological disorders, but the challenge lies in personalizing these therapies effectively. Traditionally, identifying the optimal stimulation parameters, such as amplitude, frequency, and pulse width, requires extensive trial-and-error testing, which is both time-consuming and costly. To streamline this process, we developed an active learning framework that efficiently identifies the most effective relationships between stimulation parameters and brain responses, reducing the need for numerous experiments. We conducted three types of validation for our framework: in silico experiments using synthetic data from a Parkinson’s disease model, in silico tests with real data from a non-human primate model, and in vivo tests through real-time optogenetic stimulation in rats. In each scenario, our active learning models demonstrated superior performance over traditional random sampling methods, achieving significantly lower errors in predicting brain responses. This innovative approach enhances the efficiency and efficacy of research and clinical applications in brain stimulation, offering a more cost-effective pathway to developing personalized therapies for neurological disorders. Speaker(s): Dr Mohammed Sendi Virtual: https://events.vtools.ieee.org/m/480226