B
Balaji Srinivasan
Researcher at Indian Institute of Technology Madras
Publications - 195
Citations - 1293
Balaji Srinivasan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Fiber laser & Fiber Bragg grating. The author has an hindex of 15, co-authored 179 publications receiving 845 citations. Previous affiliations of Balaji Srinivasan include Indian Institutes of Technology & University of New Mexico.
Papers
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Patent
Tunable bragg gratings and devices employing the same
TL;DR: In this paper, the use of a selective voltage input to control the phase, frequency and/or amplitude of a propagating wave in the waveguide is described, and the structure and methods of manufacturing are described.
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Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring
Jagadeeshwar L. Tabjula,Srijith Kanakambaran,Sheetal Kalyani,Prabhu Rajagopal,Balaji Srinivasan +4 more
TL;DR: The feasibility of detection of delamination is experimentally demonstrated, whose size is comparable to the ultrasonic wavelength with probability of detection better than 90% using <1% of the total number of samples required for conventional imaging, even under conditions wherein the SNR is as low as 5 dB.
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High-power "Watt-level" CW operation of diode-pumped 2.7 aem fiber lasers using efficient cross-relaxation and energy transfer mechanisms.
TL;DR: The demonstration of high power (660 mW) CW operation of a diode-pumped mid-IR Er fiber laser is reported by using efficient depopulation of the lower laser level via enhanced cross-relaxation between Er ions and energy transfer to Pr ions.
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Orbital angular momentum beam excitation using an all-fiber weakly fused mode selective coupler
Shankar Pidishety,Srinivas Pachava,Patrick Gregg,Siddharth Ramachandran,Gilberto Brambilla,Balaji Srinivasan +5 more
TL;DR: Experimental results showing the excited OAM mode purity of up to 75% measured through the standard ring technique not only demonstrate the proof of concept but also provide a baseline for further improvement.
Posted Content
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
TL;DR: A generalized deep learning-based framework for histopathology tissue analysis is proposed that has state-of-the-art performance across all these tasks and is ranked within the top 5 currently for the challenges based on these datasets.