Embedded systems are in the middle of millions of products used daily. Right from mobiles and fitness trackers to pacemakers and cars, these miniature processing entities lurk in the background, to perform. They are also known as sub-systems and are dedicated computer systems which are designed to work on particular functions in other larger systems.
Usually, they are developed in the base with microcontrollers or microprocessors and are designed for the specific characteristics of the application they are intended for. Unlike other computers, embedded systems are specifically designed to perform specific operations, typically in real-time, for example, to regulate the temperature of the micro-oven while baking.
Over the past few years, as technology has advanced quickly, people have used it in many industries and applications. From the healthcare sector to the transportation sector to the manufacturing sector such systems have become relevant in enhancing productivity. Mr. Akshat Bhutiani with his passion for exploring the domains of embedded systems, has contributed immensely to this area, especially in the medical and industrial automation domain.
He has been awarded for his research both in the theoretical and practical spheres. Mr. Bhutiani has also been appointed to the editorial board of the International Journal of Advancements in Computational Technology, where he has judged seven papers related to medical imaging, pointing to his expertise in the field. He has also lent his expertise to the Journal of Mathematical & Computer Applications, where he has reviewed numerous papers focused on image processing techniques. In the professional sphere, he has developed and implemented advanced algorithms to optimize system performance, reduce resource utilization and meet real-time constraints.
Mr. Bhutiani has also designed scalable architectures integrating modern techniques like sensor fusion, and real-time signal processing, using energy-efficient computation and fostered collaboration across multidisciplinary teams to align embedded system solutions with organizational goals, creating a coherent product. In one of his most significant projects, the AI-Based Visual Recognition system for Quality Control, he designed and trained convolutional neural network (CNN) models for real-time defect detection and recognition in high-throughput industrial environments integrating sophisticated sensor fusion techniques that combine visual data with infrared and ultrasonic inputs for enhanced detection accuracy.
Akshat’s work on Real-Time Signal Processing for Biomedical Devices was another significant contribution where he developed lightweight and energy-efficient algorithms optimized for ARM Cortex-M microcontrollers.This ensures a long battery life for wearable devices and optimizes advanced noise filtering and signal denoising techniques which ensure accurate heart rate, ECG, and other biosignal measurements.
The impact of his work is visible. His work in reducing signal processing latency to under 50ms leads to continuous biosignal processing with minimal lag, which is critical for emergency medical alerts. Similarly, diagnostic reliability is improved as there was a reduction in false positives in arrhythmia detection by 20%. He has been conscientious while arriving at these results. For instance, to tackle the limited computational resources that biomedical wearables often face due to their small form factor and reliance on low-power microcontrollers, he developed ultra-lightweight signal processing algorithms tailored for ARM Cortex-M microcontrollers.
These algorithms maintained an accuracy of 90 % in ECG signal detection while operating within the constrained processing environment, ensuring that the device could perform real-time monitoring without performance compromises. Another concern he tackled was managing the high power consumption leading to short battery life. He addressed this by implementing adaptive data sampling techniques which dynamically adjusted based on activity levels and thereby ensuring efficient energy usage.
Looking at the current trends, he anticipates significant developments in edge computing, where data processing occurs directly on the device rather than relying on cloud processing. This shift will reduce latency, enhance privacy, and enable more robust real-time decision-making. However, Mr. Bhutiani feels that smaller, lightweight devices will also face challenges related to hardware limitations.To address this, he foresees a rise in low-power, specialized hardware like neuromorphic chips designed for biomedical signal processing. In addition, to this, he tells us that the ongoing evolution of wireless communication technologies such as 5G will enhance wearables’ ability to provide continuous, real-time updates, enabling more accurate, instantaneous monitoring and intervention.
His firsthand suggestion to organizations developing wearables is to invest early in optimizing power-efficient algorithms and embracing edge computing. This will ensure that their products will not only meet clinical standards but also offer users practical, real-world effective continuous monitoring capabilities. Mr. Akshat Bhutiani’s work in embedded systems is a testament to the potential of intelligent and careful design. By developing and implementing advanced algorithms and techniques, he has demonstrated how embedded systems can address real-world requirements, fostering innovation.
Link to article –
Breakthrough in Embedded Systems: Maximising performance through intelligent design
