Browsing by Author "Jahan, Md Saroar"

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    A Meandered Line Patch Antenna at Low Frequency Range for Early Stage Breast Cancer Detection
    (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2021) Al Rakib, Md Abdullah; Ahmad, Shamim; Kabir Khan, Md. Humayun; Haque, Mainul; Faruqi5, Tareq Mohammad; Jahan, Md Saroar; Mim, Jhuma Kabir
    Every year a concerning number of women are affected by breast cancer which is one of the deadliest and common types of cancers. Breast cancer is curable at early stages. For detecting breast cancer, there are several methods such as MRI, Mammography, Tomography, Ultrasound, and biopsy are available in medical technology. Still, none of them are as easy and efficient as a microwave imaging technique, in this method, the antenna plays an important role. Therefore, this paper focuses on developing an antenna at a low-frequency range for microwave imaging techniques to detect cancerous tissue inside the breast. For this, the antenna parameters, i.e., return loss, VSWR, directivity, current density, and specific absorption rate were studied, by setting the antenna over without tumor and with tumor breast as upside-down, to ensure the compatibility of the antenna for the technique as well as for the patient’s body. A 5mm radius cancerous tumor was created inside the breast with dielectric conductivity of 4 and relative permittivity of 50. Cancerous cells were detected by reading the antenna parameters’ comparison between the healthy breast and the affected breast. The whole study was conducted by using CST MICROWAVE STUDIO SUITE 2020.
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    Road Condition Detection and Crowdsourced Data Collection for Accident Prevention: A Deep Learning Approach
    (IEEE, 2023-11-21) Jahan, Md Saroar; Islam, Mominul; Hossain, Md Sanjid; Mim, Jhuma Kabir; Oussalah, Mourad; Akter, Nasrin
    Bangladesh is one of the countries struggling to prevent road accidents, which is a global cause for concern. An early warning system that indicates road conditions can contribute to the prevention task. For this purpose, a deep-learning based approach using a Convolutional Neural Network (CNN) to learn from random road images the safety factor is developed. This results in a three-class categorization: (i) Severely risky roads, (ii) Mildly risky roads, and (iii) Normal roads. The application of deep learning techniques in this study yields an accuracy of 95.5% in detecting problematic road conditions. Furthermore, based on the study’s findings, a mobile application has been developed. The app enables real-time crowdsourced data collection of road conditions and provides a platform for users to share this information in real-time with other drivers, thereby, contributing to prevent accidents and raise awareness among drivers and users by pinpointing the location of the risky road. Finally, crowdsourced data has been reused to update the trained model, which further improves the classifier accuracy.