Faculty Publications
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Publications by NITK Faculty
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Item Vision in Versatility: Dual CCD-CMOS Imaging with Compressed Sensing for Sustainable IoT Surveillance Drones(Institute of Electrical and Electronics Engineers Inc., 2024) Gambheer, R.; Bhat, M.S.In the evolving IoT technology landscape, deploying surveillance drones with advanced imaging for security and efficiency is crucial, particularly in low-light conditions. Traditional imaging relies on either Charge-Coupled Device (CCD) sensors for their low-light prowess or Complementary Metal-Oxide-Semiconductor (CMOS) sensors for their energy efficiency. Our research introduces a novel approach by combining CCD and CMOS sensors into a single hardware platform. This allows for smart switching based on ambient light, optimizing energy use and improving image quality in varied environments. We tackle the high energy use of CCD sensors and the inconsistent performance of CMOS sensors under different lighting by applying compressed sensing (CS) techniques. These are designed to lower energy, bandwidth, and storage needs, making CCD sensors more efficient in the dark. Additionally, we've developed optimized sparse reconstruction algorithms to enhance this dual-sensor system's performance in IoT networks, ensuring high image quality with less resource use. This dual-sensor approach is a breakthrough in using CCD sensors for night surveillance, supporting sustainable IoT goals by saving energy and extending drone lifespans. Our research, backed by theoretical analysis, simulations, and empirical tests, proves our algorithms' ability to reconstruct high-quality images efficiently. Introducing this dual-sensor solution represents a significant step in sustainable IoT surveillance, offering potential for widespread use in various settings. © 2024 IEEE.Item Work Integrated Learning in Engineering Education: Bridging Theory and Practice(Institute of Electrical and Electronics Engineers Inc., 2024) Gambheer, R.; Shripathi Acharya, U.S.Work Integrated Learning (WIL) is a pivotal approach that bridges the gap between theoretical knowledge and practical application, significantly enhancing the employability and skillsets of engineering graduates. This paper delves into diverse WIL models, their integration into engineering curricula, and their profound impact on student learning outcomes. Through detailed case studies from various engineering institutes of higher learning, we illustrate successful implementations and effective assessment methods. Furthermore, we propose strategic recommendations for fostering robust industry-academia collaborations. Authored collaboratively by industry professionals and academic researchers, our findings underscore WIL's critical role in producing industry-ready engineers and offer a comprehensive framework for embedding WIL into engineering education programs. Additionally, our paper explores the concept of the Professor of Practice, an innovative role that involves industry experts teaching within university settings. This concept emphasizes the value of incorporating seasoned industry professionals into academic environments to bridge the gap between theoretical knowledge and practical application. By analyzing various case studies, we demonstrate how Professors of Practice contribute to enhancing educational outcomes and fostering closer industry-academia collaborations. This model not only enriches the curriculum but also provides students with direct insights into current industry practices and emerging technologies. Our comprehensive analysis highlights the importance of integrating WIL and the Professor of Practice model to cultivate an educational ecosystem that is both academically rigorous and aligned with industry demands. These initiatives collectively aim to produce graduates who are not only knowledgeable but also adept at navigating the complexities of the engineering profession. © 2024 IEEE.Item CCD Sensor Based Cameras for Sustainable Streaming IoT Applications With Compressed Sensing(Institute of Electrical and Electronics Engineers Inc., 2023) Gambheer, R.; Bhat, M.S.This paper presents a comprehensive study of compressed sensing (CS) techniques applied to Charge Coupled Device (CCD) and Complementary Metal-Oxide Semiconductor (CMOS) sensor-based cameras. CS is a powerful technique for reducing the number of measurements required to capture high-quality images while maintaining a high signal-to-noise ratio (SNR). In this study, we propose a novel CS method for CCD and CMOS sensor-based cameras that combines a new sampling scheme with a sparsity-inducing transform and a reconstruction algorithm to achieve high-quality images with fewer measurements. This paper focuses on an efficient CCD image capturing system suitable for embedded IoT applications. Hardware implementation has been done for proof of concept with an onboard Field Programmable Gate Array (FPGA) performing the compression. This hardware module is used over a wireless network to transmit and receive images under different test conditions with both CMOS and CCD sensors. For each use case, Peak Signal to Noise Ratio (PSNR), average power, and memory usage are computed under different ambient lighting conditions from dark to very bright. The results show that, a 640× 480 CCD sensor with compressed sensing with a sparsity of 0.5, provides 13% power saving and 15% memory saving compared to uncompressed sensing in no-light condition, resulting in 25.76 dB PSNR. Whereas, in no light condition, CMOS sensor does not capture any image at all. These results shows that the CCD image capturing system with compressed sensing can be conveniently used for embedded IoT applications. The data recovery from wireless sensor network is done at a central office where computing time and processing power resources are not constrained. The weight of the CCD camera is approximately 100 grams with modular build approach. © 2013 IEEE.Item Optimized Compressed Sensing for IoT: Advanced Algorithms for Efficient Sparse Signal Reconstruction in Edge Devices(Institute of Electrical and Electronics Engineers Inc., 2024) Gambheer, R.; Bhat, M.S.In the rapidly advancing field of the Internet of Things (IoT), the capability to process data in real-time within edge devices that have limited computational and energy resources remains a significant challenge. Traditional methods of data acquisition and processing often fail to meet these demands, leading to inefficiencies and compromised data integrity. Addressing this critical gap, our paper introduces three innovative compressed sensing algorithms specifically designed for IoT applications: Structured Random Compressed Sampling Matching Pursuit (SRCoSaMP), Sparse Adaptive Reconstruction Scheme (SPARS), and Real Time Sparse IoT (RTSI). These algorithms are specially designed to process data quickly and effectively, despite the limited resources available on edge devices. We delve into the intricate design and mathematical foundations of each algorithm, emphasizing their adaptability, real-time processing capabilities, and energy efficiency. Empirical evaluations demonstrate their superior performance in terms of real-time data processing efficiency, recovery accuracy, and computational resource management. The findings of our research mark a significant step forward in the domain of IoT data processing, offering robust solutions that ensure data integrity with minimal data samples. © 2013 IEEE.