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 an IoT smart node smarter and self-Sustainable. Exploring more circuit level techniques and find new way to design more power efficient designs.

In a remarkable academic achievement, Dr Banee Bandana Das, Assistant Professor in Department of Computer Science and Engineering and Dr. Saswat Kumar Ram, Assistant Professor in Department of Electronics and Communication Engineering, have made significant contributions to the field of biometric security. Their paper, titled “Person Identification using Autoencoder-CNN Approach with Multitask-based EEG Biometric,” has been published in the esteemed ‘Multimedia Tools and Applications journal, which is recognised as a Q1 journal with an impressive impact factor of 3.6.

This pioneering work showcases a novel approach to person identification using electroencephalogram (EEG) data. The research leverages the power of Autoencoder-CNN models combined with multitask learning techniques to enhance the accuracy and reliability of EEG-based biometric systems.

The publication of this paper not only underscores the high-quality research conducted at SRM University-AP but also places the institution at the forefront of innovative developments in biometric technology. It is a testament to the university’s commitment to advancing scientific knowledge and providing its faculty with a platform to impact the global research community positively.

Abstract

In this research paper, we propose an unsupervised framework for feature learning based on an autoencoder to learn sparse feature representations for EEG-based person identification. Autoencoder and CNN do the person identification task for signal reconstruction and recognition. Electroencephalography (EEG) based biometric system is vesting humans to recognise, identify and communicate with the outer world using brain signals for interactions. EEG-based biometrics are putting forward solutions because of their high-safety capabilities and handy transportable instruments. Motor imagery EEG (MI-EEG) is a maximum broadly centered EEG signal that exhibits a subject’s motion intentions without real actions. The Proposed framework proved to be a practical approach to managing the massive volume of EEG data and identifying the person based on their different task with resting states.

The title of Research Paper in the Citation Format

Person identification using autoencoder-CNN approach with multitask-based EEG biometric. Multimedia Tools Appl (2024).

Practical implementation/social implications of the research

1. To develop a personal identification system using MI-EEG data.
2. This work is about an Autoencoder-CNN-based biometric system with EEG motor imagery inputs for dimensionality reduction and denoising (extracting original input from noisy data).
3. The designed Autoencoder-CNN-based biometric architecture to model MI-EEG signals is efficient for cybersecurity applications.

Collaborations

1. IIITDM, Kurnool, India
2. National Institute of Technology, Rourkela, India
3. University of North Texas, Denton, USA

Future Research Plan

In the future, different deep learning and machine learning methods can be merged to explore better performance in this EEG-based security field and other signal processing areas. We will investigate the robustness deep learning architectures to design a multi-session EEG biometric system.

Link to The Article

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