http://heritageit.dspaces.org/jspui/handle/123456789/1064 2026-07-14T17:37:58Z http://heritageit.dspaces.org/jspui/handle/123456789/10962 Title: A novel Alzheimer detection rapid-testing low-cost technique by a gate engineered gate stack dual-gate FET device Authors: Kolay, Anirban; Kumar, Amitesh Abstract: This study explores a quick, low-cost method to detect Alzheimer's disease (AD) by evaluating the accomplishment of a Gate-Stack (GS) Field Effect Transistor (FET). We investigate Single-Metal (SM), Dual-Metal (DM), and Tri-Metal Double Gate (DG) configurations, where cavities have been created by etching the oxide layer underneath the gate to immobilize grey matter samples collected through Solid-phase microextraction (SPME). Healthy and AD-affected grey matter have different dielectric characteristics at high frequencies. The dielectric constant of the etched nanocavities changes when the sample, which was formerly filled with air, is immobilized in the nanocavities. The alteration in the device drain current as well as performance at 2.4 GHz has been connected to the specimen's modified dielectric constant. To distinguish between the grey matter samples from AD patients and healthy individuals, the of the suggested device along with the variation in device drain current, has been utilized as the foundation for the identification. The SM configuration has been examined by varying the cavity orientation and gate oxide stacking. To monitor the functioning of the suggested devices, the gate metal of the DM and TM devices has been altered, and a comparison has been made between SM, DM, and TM structures. The other recorded work from literature has been compared with the suggested detection technique. To ascertain whether the sample is impacted by AD, the proposed method can be used as a point of care (POC) diagnosis. 2025-04-01T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/7412 Title: Identification of System Model for a Piezoelectric Energy Harvesting Device Authors: Sil, Subhobrata; Bhadra, Reetwik; Koley, Anirban Abstract: The piezoelectric effect describes the property of some crystalline materials to polarize when subjected to a mechanical deformation thereby generating a potential difference, and at the same time to deform in an elastic manner when traversed by electrical current Discovered in 1880 by French physicists Jacques and Pierre Curie, the piezoelectric effect is defined as the linear electromechanical interaction between the mechanical and electrical state such that electric charge is accumulated in response to the applied mechanical stress. Piezoelectric crystals use piezoelectric effect to convert mechanical strain into electric current or voltage that is used to power up low power devices. It is an efficient way of energy harvesting and aims at providing clean energy solutions to everyday needs. In this paper COMSOL Multiphysics 5.6 has been used to develop the system model of a cantilever shaped piezoelectric energy harvesting device. The mathematical model developed can be later utilized to study the various responses of the device. 2022-04-01T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/7411 Title: Cross-wavelet assisted convolution neural network (AlexNet) approach for phonocardiogram signals classification Authors: Datta, Saibal Abstract: The exponential growth of a multitude of cardiovascular diseases, leading to life frightening conditions, makes fast and accurate computer-aided techniques that are relevant and important to identify these lethally medical anomalies. The authors aim to build up an accurate scheme that analyzes the Phonocardiogram (PCG) signal and find out whether the patient’s heart works normally or required any special intervention for further diagnosis. This paper has a universal, object – to aid doctors and medical personnel, and an indispensable technique like a Cross-wavelet transform (XWT) assisted Convolution neural network (CNN) utilizing the AlexNet model to detect abnormal heart sounds which are the symbol of cardiovascular disease. A pre-trained AlexNet model has been used and fine-tuned to improve system performance. Convolution neural network (Alex Net architecture) utilizes the Cross-wavelet spectrum image as an input, to prevent and protect individuals from fatal medical conditions. The proposed method is applied both on the raw PCG data and on the PCG data after removing the noise. The results of the study show that our technique attained an accuracy of 98% and 97.89% when processed with the raw and the de-noised PCG dataset to distinguish abnormal heart sound from normal ones and outperformed all existing methods. The authors used the same dataset and evaluated three classic classifiers – LVQ, LS-SVM and PNN to compare the results with the proposed classifier. Also, the works which have been published by the previous researchers are sited and compared with our proposed approach. 2021-01-01T00:00:00Z http://heritageit.dspaces.org/jspui/handle/123456789/7410 Title: Cross wavelet transform as a new prototype for classification of EEG signals Authors: Datta, Saibal Abstract: The main objective of this paper is to develop a computerised method that could be used to classify electroencephalogram (EEG) signals automatically and potentially help doctors, researchers and other medical personnel to detect epileptic signals accurately from a subject’s EEG recordings. In this paper, a Cross-Wavelet Transform (XWT) based feature extraction algorithm coupled with a few learning based classification techniques, like the Probabilistic Neural Network (PNN), the Least-Square Support Vector Machine (LS-SVM) and the Learning Vector Quantization (LVQ) is proposed to classify the EEG signals and compare the accuracy of the identification of epileptic activities. Benchmark EEG signals from the Bonn University are utilised to classify the EEG signals into the binary classes viz. Normal and Epileptic subjects. Also, a ternary classification model with categories being signals from healthy volunteers with their eyes open and eyes closed, signals from epileptic subjects during the seizure-free interval measured from within and outside the seizure generating zone of the brain and signals from epileptic subjects experiencing seizures has been put forward. The performance of the above-mentioned three supervised classification algorithms is compared by using the same training and testing datasets during stimulation. The accuracy of classification is obtained to be approximately 99%, 97.5%, 98.5% and 98.2%, 96.4%, 94% for binary and multiclass classification, respectively, using the PNN, LS-SVM and LVQ based classifier. 2018-01-01T00:00:00Z