Showing 3 results for Musa
Godday Biowei, Sulaiman Adeniyi Adekola, Kamoli Amusa,
Volume 20, Issue 2 (June 2024)
Abstract
This paper investigates the dynamics of mmWave at the free space-human skin interface. The four Fresnel equations tailored for parallel and perpendicular polarizations, are employed in the analysis. The research reveals that for human tissue with relative permittivities (18.99, 15.51, 13.35, 11.69, 10.40) and conductivities (22.48, 27.09, 29.76, 31.79, 33.38) S/m, when exposed to 5G mmWave frequencies (24, 30, 35, 40, 45) GHz, respectively, exhibits Brewster angles of (79º, 78º, 77º, 76º, 75º), respectively. Additionally, it is shown that Brewster angles exist between 60º and 80º which aligns with existing literature using Gabriel’s skin model. To further validate obtained results, use is made of the results of the Gabriel’s skin model at (40, 60, 80, 100) GHz with the respective permittivities and conductivities, to generate new power reflection coefficients for the parallel and perpendicular polarizations for the sake of comparative analysis. First, comparisons of the curves for the Gabriel’s skin model reported in the literature with this work, show fairly good agreements. Second, the Brewster angles of (78º, 76º, 74º, 73º) obtained from this work, for the respective frequencies compare favorably with (75º, 74º, 70º, 69º) extracted from Gabriel’s skin model curves reported in the literature, with all values falling within the expected range of 60º to 80º.
Godday Biowei, Sulaiman Adeniyi Adekola, Kamoli Akinwale Amusa,
Volume 20, Issue 3 (September 2024)
Abstract
Presented in this paper is an evaluation of human tissue penetration by millimeter wave (mmW) energy, particularly at 30, 35, 40 and 45 GHz. Numerical simulations show that the penetration depths in the tissue are (0.1000, 0.0937, 0.08869 and 0.08882) mm at the aforementioned frequency, respectively. It is also demonstrated that all mmW at those frequencies attenuate to zero at the epidermis which is the layer adjacent to the skin surface, without getting into the dermis which is the next layer. Crucially, these discoveries present fresh, previously unmentioned data within the current research literature. Furthermore, at the lower frequency of 24 GHz, computer simulations presented show that the propagating wave penetrates deeper (depth of 0.12 mm) and attenuates to zero at the dermis. This shows that the depth of penetration increases further at lower frequencies which strongly conforms to the principles of physical reasoning, thereby bolstering the reliability of the findings presented in this paper. The results collectively indicate that the absorption of mmW into the human tissue have limited significance when assessing compliance with electromagnetic field standards at mmW frequencies. It is reinforced in this paper why the human skin reduces the harmful effects of ultra-violet radiation. To lend credence to our formulation, certain aspects of the results obtained in this investigation when compared with similar results in the literature, show good agreements.
Kausar Ahmed, Bibhor Regan Gomes, S.m. Jobair Hossain, Emran Khan Musa, Fuyad Hasan Bhoyan, Md Humaion Kabir Mehedi, Jia Uddin,
Volume 22, Issue 3 (September 2026)
Abstract
Fires in indoor spaces such as residential and office buildings pose significant threats to human lives and property, causing substantial damage each year. Early and accurate fire detection plays a critical role in mitigating these risks and ensuring timely responses. However, conventional methods such as smoke sensors, temperature indicators, and standalone computer vision models suffer from limitations like false alarms, delayed detection, and high hardware demands. To address these challenges, we propose a novel three-layer verification framework for indoor fire detection to reduce false alarms, integrating smoke sensors, computer vision, and temperature monitoring into a multi-modal validation framework. The process begins with smoke sensors detecting potential fire incidents. The custom-trained YOLOv11n computer vision model verifies the detection using predefined thresholds, allowing immediate response without waiting for temperature escalation. If the computer vision model does not confirm the fire, the system initiates a temperature check as a final validation layer. Experimental evaluation of our model demonstrates a significantly high precision of 0.979 and a recall of 0.971. This layered approach ensures comprehensive detection, balancing reliability and resource efficiency. Our proposed hybrid AI-physical systematic framework demonstrates significant potential in reducing false alarms, improving detection accuracy, and prioritizing methodological scalability over industrial hardware. It lays the foundation for more reliable and energy-efficient fire safety solutions in smart buildings and industrial safety applications.
