Kaja Mohideen Sultan Mohideen

This chapter provides a comprehensive overview of the transformative role of artificial intelligence (AI) in addressing the structural and operational challenges faced by modern hospitals. As healthcare systems worldwide grapple with increasing patient loads, workforce shortages, and rising operational complexities, AI technologies offer new opportunities to streamline workflows, enhance decision-making, and improve overall hospital performance. As hospitals continue to embrace AI, understanding its integration and optimization will be crucial for delivering high-quality, technology-driven healthcare services.

Steganography conceals information, with audio files increasingly used in this practice. The Least Significant Bit algorithm in audio steganography requires numerous samples to embed text files effectively. This study proposes an efficient methodology for embedding text files into a digital audio file. The approach also involves embedding the index value of an audio file, which contains hidden text files in another digital audio file. The proposed algorithms are the Bit Comparison and Substitution Algorithm for embedding text files in audio files and the Bit Comparison and Retrieval Algorithm for extracting text files from audio files. Additionally, a Least Significant Bit Algorithm with a secret key (Ks) embeds the index value of the audio file. The proposed algorithm achieves an average mean squared error of 0.0513, a peak signal-to-noise ratio of 41.13732041dB, and a bit error rate of 0.000114 for embedding 2400 bytes of text files in audio files. These performance metrics demonstrate that the proposed algorithm quantitatively outperforms the traditional least significant bit algorithm by requiring fewer audio samples to embed text files. The proposed algorithm embeds 2400 bytes of text into 2400 audio samples, with each byte corresponding to a single audio sample. Each byte of the text file is embedded by modifying one bit per audio sample, resulting in 2400 difference bits between audio and stego audio files.

Active and passive sonar are the two types of empirical underwater acoustic channel models (UWACMs). Passive sonar UWACMs have applications in military, ocean exploration, and search and rescue (SAR) activities. However, high transmission loss (TL), multipath propagation, and ambient noise pose significant challenges to signal-to-noise ratio (SNR) and communication effectiveness. To address these challenges, this paper develops a UWACM based on the passive sonar equation method to determine SNR in deep-sea environments, specifically for SAR operations. Determining SNR involves characterizing signal propagation in terms of TL. Existing models lack analysis of TL and SNR for various deep-sea multipath propagation scenarios relevant to SAR applications. Therefore, this paper analyses TL and SNR for both direct and various multipath propagation modes, including surface reflection (SR), surface duct (SD), bottom bounce (BB), convergence zone (CZ), deep sound channel (DSC), and reliable acoustic paths (RAPs) in the deep sea. This work aims to quantify the detection capabilities of underwater location beacons (ULBs) under various deep-sea scenarios and configurations. By analyzing ULB signal propagation characteristics, this research seeks to address key challenges related to ULB performance and ultimately improve SAR operations. The results of the proposed model significantly correlate with existing literature, confirming its accuracy.

In this paper, a compact frequency and polarization agile patch antenna is presented. The antenna comprises a square patch with truncated corners along with a triangular slot etched at the center of the patch. Pin diodes are used to bridge the gap between the truncated corners and center triangular region with a radiating square patch. By appropriately biasing the pin diodes the antenna achieves frequency and polarization diversity. The antenna achieves five distinct states with three linear polarization (LP) states operating at three different frequencies and two circular polarization (LHCP/RHCP) states operating at the same frequency. The antenna gives a −10 dB impedance bandwidth of 95 MHz (1.825–1.92 GHz) in the GSM band for LP state 1, 115 MHz (2.365–2.48 GHz) in the ISM band for LP state 2, 145 MHz (2.08–2.225 GHz) in UMTS band for LP state 3, 100 MHz (2.1–2.2 GHz) UMTS band for CP state 4 and 5. The antenna attains a measured peak gain of ∼4.8 dBi at LP states and ∼4.54 dBi at CP states in its operating band.