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Hassan Yakubu Mkindu

Lecturer

College of Engineering and Technology

Electrical Engineering

Biography

Dr. Hassan is a researcher and telecommunications expert with a strong academic background in information and communication engineering. He earned his PhD in Information and Communication Engineering from Harbin Institute of Technology, China, an MSc. in Telecommunication Engineering from the University of Dar es Salaam, Tanzania, and a Bachelor's degree in Electronic and Information Engineering from Wuhan University of Technology, China. His research interests focus on wireless communication, signal processing, artificial intelligence in telecommunications, and next-generation network technologies. With extensive experience in both academia and industry, Dr. Hassan is dedicated to advancing innovative solutions in communication systems.

Research Interest

Dr. Hassan's research interests lie in the fields of Information and Communication Engineering and Electronic and Information Engineering. His work focuses on wireless communication technologies, signal processing techniques, and the integration of artificial intelligence in telecommunications. He explores next-generation network technologies, including 5G, 6G, and IoT, aiming to enhance network performance, reliability, and efficiency. Additionally, his research covers digital signal processing, Image processing and machine learning applications in communication systems. Dr. Hassan is also interested in the design and optimization of advanced communication systems, cognitive radio networks, and satellite communications to support emerging technological innovations.

Contacts

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Projects

Publications

  1. Hassan Mkindu, Longwen Wu, and Yaqin Zhao. Lung Nodule Detection in chest CT images Based on Vision Transformer Network with Bayesian Optimization [J]. Biomedical Signal Processing and Control. (Accession No.: WOS:000966163800001, IF=5.076(2023), JCR Q2)
  2. Hassan Mkindu, Longwen Wu, and Yaqin Zhao. Lung Nodule Detection of CT images Based on Combining 3D-CNN and Squeeze-and-Excitation Networks [J]. Multimedia Tools and Applications. (Accession No.: WOS:000943009100012, IF=2.577(2021), JCR Q2)
  3. Hassan Mkindu, Longwen Wu, Yaqin Zhao and Liang Zhao. Lung Nodule Classification of CT images Based on Deep Learning Algorithms [C]. 2021 International Conference on Imaging, Signal Processing and Communications (ICISPC), IEEE, 2021. (Accession No.: 20220711633259, EI)
  4. Hassan Mkindu, Longwen Wu, Yaqin Zhao and Liang Zhao. Lung Nodule Classification of CT images Using Channel and Spatial Attention CNN with Bayesian Optimization [C]. 2021 Global Reliability and Prognostics and Health Management Conference (PHM), IEEE, 2021. (Accession No.: 20220511542610, EI)
  5. Abdalla, Fakheraldin YO, Longwen Wu, Hikmat Ullah, Guanghui Ren, Alam Noor, Hassan Mkindu, and Yaqin Zhao. Deep convolutional neural network application to classify the ECG arrhythmia [J]. Signal, Image and Video Processing, 14(7), pp.1431-1439. (Accession No.: WOS: 000529455500001，IF=1.794(2019)，JCR Q3)
  6. Hikmat Ullah, Yaqin Zhao, Longwen Wu, Fakheraldin YO Abdalla, and Hassan Mkindu. NSST Based MRI-PET/SPECT Color Image Fusion Using Local Features Fuzzy Rules and NSML in YIQ. Space[C]. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 1-6). IEEE, 2019. (EI. Accession No.: WOS:000568621300020)
  7. Abdalla, Fakheraldin YO, Longwen Wu, Hikmat Ullah, Hassan Mkindu, Yuting Nie, and Yaqin Zhao. ECG arrhythmia discrimination using SVM and nonlinear and non-stationary decomposition [C]. 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 1-6). IEEE, 2019. (EI. Accession No.: WOS:000568621300025)
  8. Hassan Mkindu, Longwen Wu, and Yaqin Zhao. 3D Multi-Scale Vision Transformer for Lung Nodule Detection in Chest CT images [J]. Signal, Image, and Video Processing. (Accession No.: WOS:000914717900001, IF=1.583(2021), JCR Q3)