https://dspace.iiti.ac.in:8080/jspui/handle/123456789/9540 2026-05-12T17:06:24Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18101 Title: Studies on design and development of multifunctional reconfigurable frequency selective surfaces for advanced electromagnetic applications Authors: Patinavalasa, Megh Sainadh Abstract: Over the past few decades, research on frequency selective surfaces (FSSs) has grown rapidly because of their importance in stealth technology, radar systems, and electromagnetic (EM) shielding. An FSS is a periodic array of metallic elements that controls EM wave behavior such as transmission, reflection, and absorption, based on its geometry, polarization, and angle of incidence. Conventional FSS designs are restricted to a single fixed function, which limits their practical use. With the rapid expansion of wireless communication systems and modern radar platforms, there is a growing demand for compact and multifunctional reconfigurable frequency selective surfaces (RFSSs) that can perform various EM functions like absorption, transmission, and reflection- either simultaneously or selectively within a single structure. To realize this flexibility, FSS designs are engineered using various reconfigurable techniques, exhibiting different EM responses in real-time scenarios. In advanced communication and sensing environments, where different EM functionalities are required across distinct frequency bands or at different time instants within the same band, such RFSS designs hold strong potential. For example, an RFSS with switchable transmission-reflection characteristics can selectively permit desired signals in the transmissive state for adaptive wireless links, while it can reject or redirect unwanted signals in the reflective state improving link security. 2026-04-01T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18100 Title: Studies on high-gain millimeter wave antennas for 5g and non-terrestrial network (NTN) applications Authors: Mishra, Priyank Abstract: The continuous advancement in wireless communication technologies has significantly accelerated the need for high-speed, low-latency, and reliable data transmission. With the emergence of the fifth generation (5G) and upcoming 6G mobile communication systems, the pressure on the existing frequency spectrum has intensified. To accommodate the increasing user demands and enable massive device connectivity, the industry has turned toward the millimeter-wave (mm-wave) frequency bands, particularly around 28 GHz and 38 GHz, which offer significantly larger bandwidths compared to sub-6 GHz counterparts. However, mm-wave communication introduces several inherent challenges. These include high free-space path loss, poor material penetration, short communication range, and susceptibility to atmospheric absorption. To overcome these limitations and ensure robust system performance, antenna systems operating in this spectrum must be carefully designed to deliver high gain, compact size, low mutual coupling in MIMO configurations, efficient beam directionality, and radiation safety especially for applications involving wearable and body-mounted devices. 2026-03-19T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/18099 Title: Automated electromyogram signal classification frameworks based on singular spectrum analysis variants Authors: Kumar, Makam Kiran Abstract: The early diagnosis of neuromuscular disorders and the development of assistive technologies rely heavily on the accurate analysis of electromyogram (EMG) signals, which are inherently non-stationary, noisy, and complex. This thesis introduces novel frameworks that integrate advanced singular spectrum analysis (SSA) variants—namely, automatic SSA (Auto-SSA), sliding mode SSA (SM-SSA), and multivariate SM-SSA (MSSA)—with quantum convolutional neural networks (QCNNs) to enhance classification performance and robustness in biomedical signal processing tasks. The proposed frameworks are validated across three key applications. For amyotrophic lateral sclerosis (ALS) detection, intramuscular EMG signals from benchmark datasets were decomposed using Auto-SSA, an adaptive method that selects decomposition parameters automatically, extracting most significant features of the signal via particle swarm optimization (PSO). The resultant QCNN classifier achieved a testing accuracy of 98.50% on 200 training samples, outperforming conventional classifiers. For eye movement detection, extraocular muscle EMG signals were analyzed using SM-SSA, which segments signals into overlapping frames for automated decomposition, followed by neighborhood component analysis (NCA)-based feature selection. The QCNN framework yielded a remarkable 98.70% accuracy on a publicly available six-class eye movement dataset with 256 training samples. For human activity recognition (HAR), multichannel surface EMG signals were processed using MSSA, which preserves inter-channel dependencies through channel-aligned decomposition. Multi-domain features—spanning time, frequency, and entropy characteristics—were extracted and refined using minimum redundancy maximum relevance (MRMR) before classification with a 10-qubit QCNN. The HAR framework achieved superior classification accuracies of 98.81%, 98.78%, and 98.86% for aggressive, normal, and combined activity classes, respectively, using an extensive dataset comprising 20 physical actions 2026-03-22T00:00:00Z https://dspace.iiti.ac.in:8080/jspui/handle/123456789/17956 Title: Integrated nanophotonic devices for switching applications [RESTRICTED THESIS-01 Year] Authors: Kumar, Santosh Abstract: [Abstract is restricted for 01 Year, due to IPR related issue] 2026-02-27T00:00:00Z