The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.
Developing novel EV chargers is crucial for accelerating Electric Vehicle (EV) adoption, mitigating range anxiety, and fostering technological advancements that enhance charging efficiency and grid integration. These advancements address current challenges and contribute to a more sustainable and convenient future of electric mobility. This paper explores the performance dynamics of a solar-integrated charging system. It outlines a simulation study on harnessing solar energy as the primary Direct Current (DC) EV charging source. The approach incorporates an Energy Storage System (ESS) to address solar intermittencies and mitigate photovoltaic (PV) mismatch losses. Executed through MATLAB, the system integrates key components, including solar PV panels, the ESS, a DC charger, and an EV battery. The study finds that a change in solar irradiance from 400 W/m2 to 1000 W/m2 resulted in a substantial 47% increase in the output power of the solar PV system. Simultaneously, the ESS shows a 38% boost in output power under similar conditions, with the assessments conducted at a room temperature of 25°C. The results emphasize that optimal solar panel placement with higher irradiance levels is essential to leverage integrated solar energy EV chargers. The research also illuminates the positive correlation between elevated irradiance levels and the EV battery's State of Charge (SOC). This correlation underscores the efficiency gains achievable through enhanced solar power absorption, facilitating more effective and expedited EV charging.