Varun Gupta

1. Electrocardiography (ECG) is a basic tool in contemporary cardiology, widely used for the diagnosis of heart diseases, routine screening, and continuous monitoring of patients [1–5]. This method...

The Quaternion Linear Canonical Transform (QLCT) provides a unified time-frequency-phase representation for multidimensional signals. However, its applications have largely been limited to theoretical studies and image processing, leaving speech an inherently one-dimensional non-stationary signal unexplored. This paper presents a Discrete QLCT (DQLCT) implementation that operates directly on real-valued temporal signals by mapping them into the quaternion domain via analytic signal representation. The transform is derived from first principles, with invertibility proven through discrete exponential orthogonality and validated experimentally on a 9.41 second speech signal. Reconstruction error remains at machine precision (mean $8.36 \times 10^{-13}$) and Itakura-Saito COSH spectral distances stay below $10^{-12}$, confirming lossless forward-inverse transform consistency. This establishes DQLCT as a numerically stable, practically implementable tool for quaternion-based speech analysis.

The objective of the study is to investigate the thermo-electrical performance of photovoltaic thermal (PVT) system under varying volume fraction and mass flow rate (MFR) of various nanofluids such as Cu–water, TiO₂–water, and Cu–TiO₂–water hybrid nanofluid (HNF) as coolants. An HNF is the combination of different nanoparticles mixed in different proportions in the base fluid. The numerical investigation is carried out on the hourly solar irradiance and ambient temperature data for Ghaziabad city, India (28.6692^∘N, 77.4538^∘E). The outcome of the investigation shows that the thermo-electrical performance improves significantly as the MFR and volume concentration of nanoparticles in the base fluid increase. Using Cu–TiO₂–water nanofluid, the average cell temperature obtained is 33.95^∘C, while using TiO₂–water nanofluid and Cu-water nanofluid, the average cell temperature is 40.52^∘C and 35.68^∘C, respectively. The average electrical efficiency obtained using TiO₂–water nanofluid, Cu-water nanofluid, and Cu–TiO₂–water HNF is 12.91%, 13.34%, and 13.55%, respectively. The average thermal efficiency obtained using TiO₂–water nanofluid, Cu-water nanofluid, and Cu–TiO₂–water HNF coolant is 34.23%, 38.72%, and 39.76%, respectively. Further, increasing the MFR of HNF from 0.02[ ]kg/s to 0.1[ ]kg/s, the average cell temperature reduces by 9.2%, correspondingly enhancing the electrical efficiency.

To analyze the physiological information within the acquired EEG signal is very cumbersome due to the possibility of several factors, viz. noise and artifacts, complexity of brain dynamics, and inter-subject variability. To address these issues, this paper compares a U-shaped encoder-decoder network (UNET) and Bat-based UNET signal analysis (BUSA) techniques to classify depression rates in the Electroencephalogram (EEG) datasets. The main objective of including these two techniques is to reveal their effectiveness. It comprises pre-processing, feature extraction, feature selection, and classification stages. The framework excels at noise reduction during pre-processing, enhancing dataset integrity. Feature extraction leverages band power and correlation dimension to extract crucial features. Furthermore, feature selection optimizes classification accuracy by refining the fitness function of bats in the classification layer. The performance of UNET and BUSA are compared based on the following performance evaluating parameters viz. accuracy (Acc), Area Under the Curve (AUC), precision (P), and recall (R) (or sensitivity (Se)). The results indicated that the BUSA technique outperforms the UNET technique.