A Review on Wideband High-Gain Low-THz Antennas for Wireless Applications Dhamodharan Srinivasan, M Premkumar, S Deepa Nivethika, P Dhilipkumar, S Parameswari, M Kalpana Chowdary Proceedings of the 18th Indiacom 2024 11th International Conference on Computing for Sustainable Global Development Indiacom 2024, 2024 In this article, the potential of low-terahertz (THz) technology is discussed to present high data rates in future biomedical systems, and also in the 6G mobile system. However, due to the loss, the design of high-gain antennas is crucial to overcome power limitations and compensate for the additional path loss. This article highlights recent developments in wideband and high-gain sub-mm-wave and low-THz antennas and their fabrication technologies. The advancements in recent technologies have enabled the manufacture of antennas with complicated structures with precise accuracy and less expensive. Overall, the paper emphasizes the importance of high-gain antennas in overcoming the challenges associated with low-THz technology and highlights the potential of AM technology in the development of low-cost, high-performance THz antennas.
Energy Conservation of Adiabatic ECRL-Based Kogge-Stone Adder Circuits for FFT Applications P. Dhilipkumar, G. Mohanbabu Intelligent Automation and Soft Computing, 2022 Low Power circuits play a significant role in designing large-scale devices with high energy and power consumption. Adiabatic circuits are one such energy-saving circuits that utilize reversible power. Several methodologies used previously infer the use of CMOS circuits for reducing power dissipation in logic circuits. However, CMOS devices hardly manage in maintaining their performance when it comes to fast switching networks. Adiabatic technology is employed to overcome these difficulties, which can further scale down the dissipation of power by charging and discharging. An Efficient Charge Recovery Logic (ECRL) based adiabatic technology is used here to evaluate arithmetic operations in circuits like inverter, full adder, Carry Look-Ahead adder etc. A better chance at reducing delay in digital circuits is illustrated by developing a Kogge-stone Adder, built using the ECRL technology. The developed circuitry is further integrated into a Fast Fourier Transform (FFT), which demonstrates the circuit’s enhancement into DSP applications. Not only does this design reduce delay in VLSI switching circuits, but also narrows the power dissipation down to a minimum. This technique proved superior to the existing PFAL technique by demonstrating almost 10% less power dissipation with minimal propagation delay. All the circuits have been simulated at 45 nm technology using the Tanner EDA tool.
Application of Multi-Domain Feature for Automated Seizure Detection from EEG Signal Ashokkumar S.R, Premkumar M, Dhilipkumar P, P Manikandan, Naveen P, M. Saravanan 3rd International Conference on Smart Electronics and Communication Icosec 2022 Proceedings, 2022 An automated epilepsy detection method has been proposed by exploiting the multi-domain features with a few learning algorithms. EEG signals are initially preprocessed to remove the redundant data. Then they are divided into 5-second segments, with each segment containing the extraction of multi-domain information from the frequency domain, temporal domain, connectivity, and graph analysis measurements. From the obtained features, most significant features are selected by the Multi Objective Evolutionary (MOE) method. The correlation matrix is obtained through connectivity calculation, and it is converted into binary undirected and weighted graphs through graph theory analysis. For classification, Support Vector Machine (SVM), Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA) has been implemented. Also, the Bayesian optimization (BaO) algorithm has been utilized to optimize SVM parameters. This proposed work is analyzed for EEG signals obtained from the CHB-MIT dataset. The proposed approach resulted in 98.09%, 81.49% and 80.90% accuracy rate for SVM, LDA, and QDA respectively. Conclusively, the SVM classifier outperformed other classifiers in terms of accuracy, Area under the Curve (AUC), sensitivity and specificity with 98.1%, 99.7%, 98.1%, and 98.1% respectively.
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