Spatial & Temporal Agnostic Deep-Learning Based Radio Fingerprinting

Radio fingerprinting is a technique that validates wireless devices based on their unique radio frequency (RF) signals. This method is highly feasible because RF signals carry distinct hardware variations introduced during manufacturing. The security and trustworthiness of current and future wireless networks heavily rely on radio fingerprinting. In addition to identifying individual devices, it can also differentiate mission-critical targets. Despite significant efforts in the literature, existing radio fingerprinting methods require improved robustness, scalability, and resilience. This study focuses on the challenges of spatial-temporal variations in the wireless environment. Many prior approaches overlook the complex numerical structure of the in-phase and quadrature (I/Q) data by treating real and imaginary components separately. This approach results in the loss of essential information encoded in the signal\u27s phase and amplitude, leading to lower accuracy. This thesis proposes several enhancements. First, we treat the entire complex structure of the I/Q data as a single input to a complex-valued convolutional neural network (CVNN), thereby improving the model\u27s accuracy. Second, conduct extensive experiments to determine optimal pre-processing parameters, ensuring that over-optimistic conclusions about RF fingerprinting performance are avoided. Third, we compare various activation functions and transfer learning-based fine-tuning and a triplet network to address the variations the wireless environment introduces in scenarios involving different locations and times. We use the concept of a ``device rank\u27\u27 metric to perform device identification with certainty based on RF fingerprinting. Our work concretely proves that CVNN outperforms CNN for radio fingerprinting. Concatenated Rectified Linear Units (CReLU) activation function and fine-tuning-based transfer learning perform the best for cross-location and time device fingerprinting. Adviser: Nirnimesh Ghos

Transcriptome profiling of in vitro-matured oocytes from a korean native cow (hanwoo) after cysteamine supplementation

This study elucidated the molecular markers that decrease oocyte quality during in vitro culture, restricting optimal developmental potential. Here, we evaluated the transcriptomic differences between cysteamine-treated and non-treated bovine cumulus oocyte complexes (COCs) after 22 h of co-culture in the maturation media using RNA sequencing. In total, 39,014 transcripts were sequenced between cysteamine-treated and non-treated mature COCs. We evaluated the relative expression of 21,472 genes, with 59 genes showing differential expression between the two COC groups. The cysteamine-treated group had 36 up-regulated gene transcripts and 23 down-regulated gene transcripts. Moreover, gene ontology (GO) enrichment analysis revealed that multiple biological processes were significantly enriched after cysteamine supplementation. Differentially expressed genes appeared to maintain normal oocyte physiology, regulation of apoptosis, differentiation, ossification or bone formation, cardiac and muscle physiology, hormonal secretion, and membrane construction for further embryonic development. In conclusion, cysteamine affects the mRNA level of COCs during oocyte maturation by upregulating potential molecular markers and downregulating genes that affect further embryonic development.N