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Rekha Chakravarthi
Sathyabama Institute of Science and technology
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Efficient addressing schemes for internet of things Venkatesh Thamarai Kannan; Rekha Chakravarthi
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i4.pp4415-4429

Abstract

The internet of things (IoT) defines the connectivity of physical devices to provide the machine to machine communication. This communication is achieved through various wireless standards for sensor node connectivity. The IoT calls from the formation of various wireless sensor nodes (WSNs) in a network. The existing neighborhood discovery method had the disadvantage of time complexity to calculate the cluster distance. Our proposed method rectifies this issue and gives accurate execution time. This paper proposed mobility management system based on proxy mobile IPv6 as distributed PMIPv6 with constrained application protocol (CoAP-DPMIP) and PMIPv6 with constrained application protocol (CoAP-PMIP). It also provides the optimized transmission path to reduce the delay handover in IoT network. The PMIPv6 described the IPv6 address of mobile sensor device for efficient mobility management. The network architecture explains three protocol layers of open systems interconnection model (OSI model). The OSI layers are data link layer, network layer and transport layer. We have proposed the distance estimation algorithm for efficient data frames transmission. This paper mainly focuses the secure data transmission with minimum loss of error. The evaluation result proved that proposed technique performance with delay, energy, throughput and packet delivery ratio (PDR). Also, it measures the computational time very effectively.
Quantum Machine Learning for Enhancing Signal Processing Applications Kamineni Sairam; Shashikant Deepak; Rekha Chakravarthi; Saumendra Ku. Mohanty; P.S. Raghavendra Rao; Varsha Choudhary; Ankit Punia
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1375

Abstract

In neuroscience and therapeutic practice, electroencephalography (EEG) is a vital instrument for tracking and analysing brain activity. While traditional neural network models, like EEG-Net, have made significant progress in interpreting EEG signals, they frequently encounter difficulties due to the great dimensionality and complexity of the data. Quantum machine learning (QML) techniques offer new ways to improve machine learning models, thanks to recent developments in quantum computing. As a forward-looking approach, we present Quantum-EEG Net (QEEG Net), a novel hybrid neural network that combines quantum computing with the classical EEG Net architecture to improve EEG encoding and analysis. While the results may not always outperform conventional methods, it demonstrates its potential. In order to capture more complex patterns in EEG data and maybe provide computational benefits, QEEG Net integrates quantum layers into the neural network. Using the benchmark EEG dataset, BCI Competition IV 2a, we test QEEG Net and show that it consistently performs better than standard EEG-Net on the majority of participants and has other robustness to noise.
Co-Authors Ankit Punia Kamineni Sairam P.S. Raghavendra Rao Saumendra Ku. Mohanty Shashikant Deepak Varsha Choudhary Venkatesh Thamarai Kannan