Biomedical signal processing has advanced to the point that tools and methods are now available to doctors to diagnose and track medical conditions connected to pregnancy. However, it is extremely difficult for researchers to look into novel procedures and approaches to uncover underlying pathological abnormalities associated with high-risk pregnancies due to the scarcity of high-quality medical databases of pregnant women. In this study, a LabVIEW software environment is used to precisely design a bio-physiological signal generator (BPSG) for use in feto-maternal health assessment applications. McSharry’s dynamical ECG model served as inspiration for the methods utilized to create the proposed time-domain mathematical model. The BPSG is capable of generating various realistic synthetic signals like respiration signal, pulse plethysmography (PPG) signal, phonocardiography (PCG) signal, maternal ECG (MECG) signal, fetal ECG (FECG) signal, abdominal ECG (AECG) signa,l and umbilical blood flow (UBF) velocimetry signals with corresponding Doppler indices. It is possible to create synthetic signals for both healthy and unhealthy conditions. Synthetic signal facilitates the testing and calibration of new diagnostic procedures, denoising algorithms, feature extraction processes, and instrumentation, all of which contribute to the prompt prediction of an overall health state of expectant mother.

This paper presents a novel vision impairment assistive device to improve mobility and independence. This device consists of Arduino Nano microcontroller technology that powers the Satellite/GPS-based stick, which tracks and navigates in real-time. Arduino Nano’s adaptability and compactness enable our portable, affordable white cane replacement. Satellite signals let the stick locate the user, compute the best routes, and provide aural navigation cues through speakers or headphones. The obstacle detection sensors notify users of adjacent risks, improving safety. The proposed device is a stable and user-friendly technology that delivers a potential answer to visually impaired navigation issues after rigorous development and testing.

Fuel cells (FCs) are increasingly attracting attention for their efficient conversion of chemical energy into electricity without the need for combustion. Their high efficiency and versatility make them a promising technology across various applications. Researchers are actively exploring ways to optimize FC systems to meet specific energy needs. Among the different types of fuel cells, solid oxide fuel cells (SOFCs) stand out as a promising clean energy technology that generates electricity through electrochemical reactions. However, accurately modeling SOFCs, which is essential for reducing design costs, presents a challenge due to their complex and nonlinear characteristics. An ideal model should be adaptable to varying operating pressures and temperatures. This research introduces a novel approach for optimal SOFC model identification using a differential evolutionary mutation Fennec fox algorithm (DEMFFA). A real-world case study demonstrates the superior effectiveness of DEMFFA compared to existing methods. Additionally, a sensitivity analysis evaluates the influence of temperature and pressure on the model, with results indicating that the proposed method achieves higher efficiency than other approaches. The sum of the square error of the proposed algorithm is 1.18E-11 followed by the parent algorithm, Fennec fox algorithm (FFA) (1.24E-09), and some of the compared algorithms. The computational time of the proposed algorithm is 1.001 s, followed by the parent algorithm FFA (1.199 s) and some of the compared algorithms. DEMFFA offers significant potential, enhancing renewable energy, minimizing SOFC's environmental impact, and improving real-world applications like distributed power generation and hydrogen integration.

Electrical impedance tomography (EIT) is a non-invasive technique that is used to estimate the electrical properties of a medical or non-medical object through the boundary data of the object. It used to achieve functional imaging of different objects by measuring electrical conductivity and impedance parameters. In this paper, a novel image reconstruction algorithm is presented, which is based on graphical user interface (GUI) developed on MATLAB software platform. EIT imaging algorithm consists of a forward problem and an inverse problem. The forward problem is formulated with the conductance matrix, and a non-iterative inverse method is used to estimate the conductivity distribution. Image display and data analysis are implemented and controlled directly in the GUI. The numerical simulations and phantom experiments have been carried out to evaluate the performance of the proposed algorithm and other previous research data through quantitative parameters. The obtained result shows satisfactory and comparable results to other EIT imaging algorithm.