https://shodhratna.thapar.edu:8443/jspui/handle/123456789/91 2025-09-08T05:00:49Z https://shodhratna.thapar.edu:8443/jspui/handle/tiet/173 Title: Design a metamaterial based applicator for hyperthermia cancer treatment Authors: Sharma, Nitika; Singh, Hari Shankar; Khanna, Rajesh; Kaur, Amanpreet; Agarwal, Mayank Abstract: This research describes a metamaterial-based applicator with and without water bolus for cancer treatment. The metamaterial based applicator consists of a double spiral antenna and a slotted square shape artificial magnetic conductor (SSA) structure. The antenna is designed on a low-cost FR4 substrate with dimensions of 32 × 32 × 3.27 mm3 and the SSA unit cell which behaves as a metamaterial, is designed on an RT-duroid substrate with dimensions of 15 × 15 × 0.76 mm3 (size of the unit cell). The 4 × 4 unit cells of the SSA structure are optimized to direct the maximum field toward the cancer tissue. The proposed applicator (antenna + SSA structure) is tested for hyperthermia treatment using a heterogeneous phantom (skin, fat, and muscle layers) and the human-mimicked model with an air medium and a water bolus layer. The applicator's performance is evaluated in terms of specific absorption rate (SAR), penetration depth (PD), and effective field size (EFS). Further, thermal analysis is performed, with 1.5 W of input power at the antenna port, and the maximum temperature rise of 44 0C is obtained. The cancer tissue (tumor) temperature ranges between 41 °C to 45 °C, sufficient for effective hyperthermia therapy. Finally, the suggested metamaterial based applicator is built and tested in the presence of a phantom and head tissue simulating liquid. © 2024 Elsevier Ltd 2025-01-01T00:00:00Z https://shodhratna.thapar.edu:8443/jspui/handle/tiet/163 Title: Deep learning-based port-classification approach incorporating LSTM network for high-throughput data center interconnect Authors: Kaur, Harpreet; Kaler, Rajinder Singh Abstract: This paper presents a solution to the throughput challenges of interconnecting systems in data centers that process big data every second. The Long-Short-Term-Memory (LSTM)-based neural network is implemented. It discovers the available switching ports and classifies them for short distances and minimum error to receive data with high throughput and low latency. The structure is promising by showing a prediction accuracy of 96.88% and a classification accuracy of 99.7% with a precision of 99.6%. The specificity of the model turns out to be 98.6%. The neural network training is done on the comparative analysis, with training samples varying between 50–2000 epochs, to know the best epochs to achieve a higher accuracy rate. The comparison of various parameters w.r.t. word length presents a solution to minimize cross-interference by controlling the word length between 10–14 bits at the input. The bit error rate of 10–18 and extinction ratio of 19.5 dB show the effective error-free transmission of bits. It is observed that the minimum log of bit error rate and eye-opening factor show immediate improvement of -3 and 10% respectively when the switching of the signal is done by using the information created by the neural network model. The small switching time of 0.35 ns and throughput of 96 Tbps prove the applicability of the proposed neural network-based structure for low-latent high-throughput configurations. 2024-01-01T00:00:00Z https://shodhratna.thapar.edu:8443/jspui/handle/tiet/162 Title: Porphyrin based-optical fiber sensor for pattern recognition of chlorides and nitrates Authors: Yadav, Mukti; Kundu, Kaustubh; Kaler, Rajinder Singh; Kundu, T. Abstract: The paper introduces a novel approach to develop a photonic device for chemical applications using an evanescent wave absorption technique-based optical fiber probe layered with a Tetraamine porphyrin (TAPP) monolayer exploiting the optical properties of TAPP. The interactions between the TAPP and nitrate and chloride ions in the solution phase were monitored and the changes in the absorption spectra of TAPP molecules were recorded. Distinct optical patterns emerge, and ion-dependent shifts in the Soret and Q-bands are analyzed for eight samples of nitrates and chlorides. Principal Component Analysis (PCA) is employed to discern ion groups based on the spectral pattern recognition. These fiber probes exhibit sensitivity to nitrate and chloride ions, with enhanced selectivity, robustness, and a rapid response time of 2–3 seconds. The study demonstrates the emer-gence of porphyrin-based optical fiber sensors as a useful sensor technology in environmental monitoring. 2024-04-12T00:00:00Z