https://journal.utripoli.edu.ly/index.php/Alqalam/issue/feed AlQalam Journal of Medical and Applied Sciences 2023-01-14T15:53:09+00:00 Ahmed Atia editorajmas@gmail.com Open Journal Systems <p><strong>AlQalam Journal of Medical and Applied Sciences</strong> (AJMAS), eISSN:2707-7179, is an international indexed peer-reviewed open access journal published in English on behalf of the <a href="https://utripoli.edu.ly/">University of Tripoli Alahlia</a>-Libya and dedicated to promotion of high-quality research in all fields of medical and applied sciences. The journal publishes original research articles, review articles, case reports/study, short communication, overview and letter to the editor that reflects important advances in clinical and basic studies of all aspects of medical, bio-medical and applied sciences.</p> <p><br /><strong><em>Editoral Office in Charge</em></strong><br /><strong>Dr. Ahmed Atia</strong><br />editorajmas@gmail.com <br />00218910737929</p> https://journal.utripoli.edu.ly/index.php/Alqalam/article/view/210 Emitters Classification based on Steady State BT Signal Features 2022-12-22T21:45:47+00:00 Aysha Ali adelagila@gmail.com Alghannai Aghnaiya elganai.1962@gmail.com Abdussalam Ali abdussalam.a.ahmed@gmail.com <p><strong>Aims</strong>. The aims of this study were to verify the enhancement wireless networks’ security, and the identification of specific emitters. <strong>Methods</strong>. A novel technique was introduced to enhance the security of wireless network. The technique was built up based on the use of radio frequency (RF) fingerprinting for Bluetooth (BT) signals. Five emitters represented as mobile phones were considered to classify them. Two hundred and fifty BT signals were collected from these emitters. Portions of steady state of the BT signals studied. The applied methods were a detection method based on energy envelope was used to detect the portions, and the variational mode decomposition (VMD) method was applied to generate the intrinsic mode functions (IMFs). Features which represented as statistical information were extracted from the obtained IMFs. The Tree classifier was applied to the extracted feature to classify the introduced emitters. <strong>Results</strong>. The results demonstrated a high percent of correct classifications (93.5% through 100%). <strong>Conclusion</strong>. The results demonstrated the robustness of the features, the functionality, and the activeness of the introduced method; consequently, a high degree of wireless network security was achieved.</p> 2023-01-14T00:00:00+00:00 Copyright (c) 2023 AlQalam Journal of Medical and Applied Sciences