In an exciting development, Dr Manjula R, Assistant Professor in the Department of Computer Science and Engineering, along with B.Tech. students Mr Adi Vishnu Avula, Mr Jawad Khan, Mr Chiranjeevi Thota, and Ms Venkata Kavyanjali Munipalle, have authored a book chapter titled “Machine Learning Approach to Determine and Predict the Scattering Coefficients of Myocardium Tissue in the NIR Band for In-Vivo Communications – 6G Network in book name “Edge-Enabled 6G Networking: Foundations, Technologies, and Applications”.

This achievement highlights the innovative research and collaboration showcase the dedication and expertise of both faculty and students in the field of computer science and engineering. The book chapter explores the cutting-edge advancements in 6G networking and its potential applications, shedding light on the future of communication technologies.

We congratulate Dr Manjula R and the team of talented students on this significant accomplishment and look forward to seeing more groundbreaking research from them in the future. Stay tuned for more updates on their work and achievements.

Abstract

The accurate calculation of the scattering coefficient of biological tissues (myocardium) is critical for estimating the path losses in prospective 6-G in-vivo Wireless Nano sensor networks (i-WNSN). This research explores machine learning’s potential to promote non-invasive procedures and improve in-vivo diagnostic system’s accuracy while determining myocardium’s scattering properties in the Near Infrared (NIR) frequency. We begin by presenting the theoretical model used to estimate and calculate scattering coefficients in the NIR region of the EM spectrum. We then provide numerical simulation results using the scattering coefficient model, followed by machine learning models such as Linear Regression, Polynomial Regression, Gradient Boost and ANN (Artificial Neural Network) to estimate the scattering coefficients in the wavelength range 600-900 nm.

We next contrast the values provided by the analytical model with those predicted via machine learning models. In addition, we also investigate the potential of machine learning models in producing new data sets using data expansion techniques to forecast the scattering coefficient values of the unavailable data sets. Our inference is that machine learning models are able to estimate the scattering coefficients with very high accuracy with gradient boosting performing better than other three models. However, when it comes to the prediction of the extrapolated data, ANN is performing better than other three models.

Keywords: 6G, In-vivo, Dielectric Constant, Nano Networks, Scattering Coefficient, Machine Learning.

Significance of Book Chapter

The human heart is a vital organ of the cardiovascular system and is very crucial for any living being. However, this organ is prone to several diseases—Cardiovascular Disease (CVD)—an umbrella term. CVDs are the set of the heart diseases that comprises heart attack, cardiac arrest, arrhythmias, cardiomyopathy, atherosclerosis to name a few. CVD alone account for most of the deaths across the globe and is estimated reach 23.3 million deaths due to CVD by 2030. Early detection and diagnosis of CVD is the ultimate solution to mitigate these death rates. Current diagnostic tests include, however not the exhaustive list, ECG, blood test, cardiac x-ray, angiogram.

The limitations of these techniques include bulkiness of the equipment, cost, tests are suggested only when things are in critical stage. To alleviate these issues, we are now blessed with on-body or wearable devices such as smart watches that collect timely information about the cardiac health parameters and notify the user in a real-time. However, these smart watches do not have the capability to directly detect the presence of plaque in the arteries that leads to atherosclerosis. These devices have the capability to track certain health parameters such as heart rate, blood pressure, other activity levels, any deviation in the measured values of these parameters from the normal values might give an indication of cardiac health issues. This requires a formal diagnostics test such as cardiac catheterization or cardiac x-ray leading to the original problem.

Therefore, in this work we aim to mitigate these issues by proposing the usage of prospective medical grade nanorobots—called nanosurgeons, that can provide real-time live information on the health condition of the internal body. Particularly, our work assumes that these tiny nanobots are injected into the cardiovascular system that keep circulating along with the blood to gather health information. Such nanosized robots are typically expected to work in the terahertz band owing to their size. At such high frequency, the terahertz signals are prone to high path losses due to spreading, absorption and scattering of the signal during propagation. Our work aims at understanding these losses, especially the scattering losses, of the terahertz signal in the NIR band (600-900 nm) using the existing models, analytically. Further, to understand the strength of machine learning in predicting these scattering losses, we also carryout simulation work to estimate and predict the scattering losses using Linear Regression, Polynomial Regression, Gradient Boost and Artificial Neural Network (ANN) models.

Our preliminary investigation suggests scattering losses are minimal in NIR band and machine learning can be seen as a potential candidate for perdiction of scattering losses using the available experimental data as well as using data augmentation techniques to predict the scattering losses at those frequencies for which either experimental data is not available or can prevent the use of costly equipment to determine these parameters.

Dr Banee Bandana Das,  Assistant Professor in the Department of Computer Science and Engineering, has achieved a remarkable milestone. The invention titled “An Energy Harvesting System for Node Devices and a Method Thereof” has been granted a patent by the Patent Office Journal, under Application Number: 202241066526. This achievement marks a significant leap forward in the realm of energy harvesting systems, promising a brighter and more secure future for IoT applications.

Abstract

The present invention is broadly related to design of secure and Trojan Resilient energy harvesting system (EHS) for IoT end node devices. The objective is to develop a state-of-the-art energy harvesting system which can supply uninterrupted power to the sensors used in IoT. The EHS is self-sustainable. The higher bias voltages are generated on chip. The system is mainly consisting of security module, power conditioning module, Trojan Resilient module, and load controller module. The power failure of the sensors used in IoT may leads to information loss thereby causing catastrophic situations. An uninterrupted power supply is a must for smooth functioning of the devices in IoT. This invention caters secure power requirements with security issues of IoT end node devices.

Practical Implementation:

The IoT end node devices needs 24*7 power supply and are very sensitive to attacks made by adversaries before and after fabrication. This invention takes care of the power requirement of end node devices with green energy and secure the EHS-IC from adversaries and attacks and therefore can be used by individuals, as powering sensors at remote locations and as part of smart agriculture.

Future research plans:

Design more secure and reliable design for making a IoT smart node smarter and self-Sustainable. Exploring more circuit level techniques and find new way to design more power efficient designs.