In this paper, we present an unprecedented metamaterial absorber design exhibiting exceptional characteristics in electromagnetic wave absorption. The proposed bent Y-shaped structure, fabricated on an FR-4 substrate with copper patches, showcases remarkable performance across a diverse frequency spectrum. Through exhaustive simulations in CST, this design manifests eight distinct resonant frequencies, achieving absorption rates exceeding 90 % at each resonance. The resonances, strategically spanning from L-band (3.728 GHz) through S-band, C-band, X-band, Ku-band, and K-band up to 22.664 GHz, signify unparalleled versatility and efficacy in mitigating electromagnetic radiation. It investigates the equivalent circuit parameters of a proposed metamaterial absorber design, focusing on inductance (L), capacitance (C), and resistance (R). This paper investigates the applications of UWB devices at 3.728 GHz and Doppler navigation aids at the 13.4 GHz frequency as regulated by the Federal Communications Commission. It includes a discussion on near-zero refractive Index Metamaterials (NZRIM), highlighting their potential utilization in achieving extraordinary control over wave behaviour. Notably, the absorber's inherent polarization insensitivity fortifies its adaptability in various applications. Additionally, the metamaterial exhibits near-zero or negative permittivity, altering electric response, while simultaneously demonstrating permeability absolute zero throughout all frequency bands sparking new avenues for exploration and challenging conventional electromagnetic theories.
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.