Faculty Publications
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Publications by NITK Faculty
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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.