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: Research involving the design and analysis of slanted polarized antennas using inverted resonators. Biomedical & Engineering Research

This paper presents a comprehensive performance analysis of millimeter-wave communication systems in the presence of interference. Sinha Namrata and her co-authors derive closed-form expressions for the outage probability and average symbol error rate.

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Abstract (150–200 words) This paper presents a robust deep learning framework for early detection and classification of faults in three-phase induction motors using vibration and stator-current signals. We design a data-preprocessing pipeline that includes resampling, denoising with wavelet thresholding, and time–frequency feature extraction via short-time Fourier transform (STFT) and continuous wavelet transform (CWT). A convolutional neural network (CNN) processes spectrogram/CWT images while a parallel 1D-CNN processes raw waveform data; features are fused and fed to fully connected layers for multi-class fault classification (bearing defects, rotor bar faults, eccentricity, healthy). We evaluate the model on an industrial testbed and the publicly available CWRU and Paderborn datasets, achieving average accuracy >98%, F1-score >0.97, and robust performance under variable loads and noise. Ablation studies quantify the contribution of each sensor modality and preprocessing step. The proposed method is computationally efficient for edge deployment and includes guidelines for transfer learning to adapt to new motor types.

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Sinha Namrata's research interests lie at the intersection of wireless communication systems, network protocols, and cybersecurity. Her work aims to design, develop, and optimize novel communication protocols and architectures for next-generation wireless networks. She has made significant contributions to the field, including the development of energy-efficient routing protocols, secure communication schemes, and advanced network architectures.

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