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A. M. Alam
Also published under:Ahmed Manavi Alam
Affiliation
Mississippi State University, Mississippi State, MS, USA
Topic
Electromagnetic Interference,Convolutional Layers,Short-time Fourier Transform,Soil Moisture Active Passive,Deep Learning,Microwave Radiometer,Anechoic Chamber,Convolutional Neural Network,Radiometric Measurements,Brightness Temperature,Deep Learning Architectures,Low-noise Amplifier,Performance Metrics,Second Moment,Soil Moisture,Time-frequency Analysis,5G New Radio,Convolutional Block,Deep Learning Models,Internal Calibration,Level 1B,Quadrature Phase Shift Keying,RF Front-end,Radiometric Calibration,Resource Block,Signal-to-noise Ratio Levels,Spectroscopic,Subcarrier Spacing,Unmanned Aerial Systems,Action Recognition,Advanced Driver Assistance Systems,American Sign Language,Artificial Neural Network,Confusion Matrix,Convolutional Neural Network Layers,Convolutional Neural Network Model,Deep Learning Framework,Detection Probability,Earth Observation,External Calibration,False Alarm Rate,Feature Maps,Frequency Band,Frequency Domain,Frequency Resolution,Horizontal Polarization,Human Activity Recognition,Input Features,Internal Temperature,Level 1a,
Biography
Ahmed Manavi Alam (Graduate Student Member, IEEE) received the B.S. degree in electrical and electronic engineering from Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh, in 2019, and the M.S. degree in electrical and computer engineering from Mississippi State University, MS, USA, in 2024, where he is currently pursuing the Ph.D. degree in electrical and computer engineering. He is also a Research Assistant with the Information Processing and Sensing (IMPRESS) Laboratory. He was a machine learning Intern with the High-Performance Computing Collaboratory, in Summer 2023. He was an audio machine learning and DSP Research Co-Op with Bose Corporation, in Summer 2024. His research interests include algorithm development of deep learning-based inverse problems, digital signal processing, machine learning for remote sensing, and physics-aware deep learning. He is a student member of the IEEE Geoscience and Remote Sensing Society (GRSS) and the Communications Society (ComSoc). He was a finalist at the IGARSS 2022 Student Paper Competition. He was the winner of the Spring 2024 Graduate Research Symposium organized by Mississippi State University. He was a recipient of the National Academy of Sciences (NAS) Fellowship.