Faculty Publications
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Item Investigation of CMOS Based Integration Approach Using DAI Technique for Next Generation Wireless Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Roy, G.M.; Kanuajia, B.K.; Dwari, S.; Kumar, S.; Song, H.This research work investigates a CMOS based low noise amplifier (LNA) using differential active inductor with eight-shaped patch antenna for next generation wireless communication. The proposed work conceded into three different phases. The first phase proposes LNA architecture which includes multistage cascode amplifier with a gate inductor gain peaking technique. The ground approach for this architecture employs active inductor technique that includes two stages of differential amplifier. The proposed novel technique leads to give incremental in inductance by using of common mode feedback resistor and lowers the undesirable parasitic resistance effect. Additionally, this technique offers gain enhanced noise cancellation and achieves a frequency band of around 5.7 GHz. The proposed architecture includes single stage differential AI and enhances the bandwidth up to 6.8 GHz with peak gain of 21 dB at 7.8 GHz. The noise figure and stability factor are achieved which is reasonably good at 1 dB. The proposed architecture is design and optimized on advanced design RF simulator using 0.045 µm CMOS process technology. While in second phase, a narrow band eight-shaped patch antenna is designed which provides operating band range from 5.8 to 6.5 GHz with 6.2 GHz resonating frequency. Highest peak gain of 15 dB and maximum radiation power of 42.5 dBm is succeed by proposed antenna. The final phase provides integration strategy of LNA with antenna and achieves desired gain of nearly 21 dB with minimum NF of 1.2–1.5 dB in the same band. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.Item Classification and grade prediction of kidney cancer histological images using deep learning(Springer, 2024) Chanchal, A.K.; N, S.; Lal, S.; Kumar, S.; Saxena, P.U.P.Renal Cell Carcinoma (RCC) is the most common malignant tumor (85%) of kidney cancer and has a complex histological pattern and nuclear structure. The manual diagnosis of kidney cancer or any other cancer from histopathology image depends on the knowledge and experience of pathologists, and the pathologist’s experience influences the results. According to studies, the kind of histology in kidney cancer is related to the prognosis and course of treatment. Since the kind of histology, molecular profile, and stage of the disease all affect how the disease is treated, there is an essential need to develop an automated system that can precisely analyze the histopathological images of the disease. This work demonstrates how a deep learning framework can be used to predict and classify associated grades of RCC from provided haematoxylin and eosin (H &E) images. The proposed model focuses on two important tasks- First to capture and extract associated features from the H &E images of five different grades. Second, to classify the new set of unseen H &E images into five separate grades using the obtained features. The proposed architecture has been tested and experimented on two independent datasets containing H &E stained histopathology images. The proposed architecture has been examined using the following performance metrics namely precision, recall, F1 - score, accuracy, Floating-point operations (FLOPs), and the total number of parameters. The obtained results show that the proposed architecture attains better results over seven state-of-the-art deep learning architectures on two different H &E stained histopathology image datasets. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.