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Research Interest
Research Interest
Wireless communications
Vehicular communications
6G
Machine learning
RF circuits
Wildlife tracking
Research Experiences
Research Experiences
Supervisor: Dr. MD Farhad Hossain
Research Tools: MATLAB.
Paper: Submitted to the Vehicular Communication journal by Elsevier. (17th August, 2023)
Preprint: https://doi.org/10.48550/arXiv.2308.13833
We propose a new network architecture using Smart Meters as distributed access points of communications, ensuring increased reliability and reduced latency for enhancing V2N communications.
Two distinct algorithms, namely, the maximum SNR (MaxSNR) algorithm and the minimum distance (MinDis) algorithm, are proposed for vehicle-SM associations.
We conduct extensive investigations by developing a MATLAB simulation platform for determining the viability and acceptance of our proposed system model and algorithms.
Various performance metrics, including reliability, latency, and throughput are evaluated under various system settings for validating the effectiveness of the proposed architecture for V2N URLLC operations.
The trivial Base Station-based V2N system model is also simulated and compared to determine the superiority of our proposed system model.
Some figures related to this work work can be found below.
Experiment on Correlation between Fingerprint and Blood Group
Experiment on Correlation between Fingerprint and Blood Group
Supervisor: Shahed Ahmed
Research Tools:Python, ML, DL.
Paper: Google drive
We build a hardware device to make our own database of fingerprint and blood group data for our project.
We try various image processing method on our raw data including Gaussian Blur, Median Blur, Otsu Threshold, Adaptive Threshold Gaussian, Otsu Threshold to Adaptive to Canny to Binary etc.
In our study we extract some fingerprint features by typically involving the identification and extraction of minutiae points, which are the distinctive characteristics of a fingerprint such as ridge endings, bifurcations, and ridge contours.
We have done the tests in our datasets using 80% dataset for the training purposes and 20% datasets for tests. We got 40-45% accuracy when we did the test considering 4 major groups (A,B,AB and O). But we got better results when we did the test using a binary method (considering two groups at a time).
Further we implement a statistical analysis using a one way anova test for this analysis.
Some figures related to this work work can be found below.
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