Posts by Collection

5G-Radiometer Coexistence Testbed for Spectrum Sharing Research

Hardware testbed for investigating spectrum coexistence between L-band radiometers and 5G wireless communication systems

RFI-Net: Deep Learning-Based RFI Detection in the Time-Frequency Domain

Deep learning-based RFI detection and mitigation for passive sensing using a dual-loss U-Net architecture

HRSpecNet: Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction

End-to-end deep learning framework for noise-robust, high-resolution micro-Doppler spectrogram generation and improved human activity recognition

CNN-Based RFI Detection for SMAP Radiometer

Convolutional neural network approach for robust radio-frequency interference detection in spaceborne L-band radiometer measurements

Deep Learning-Based Radiometer Calibration Framework

Deep learning-based calibration for passive microwave radiometers using physics-informed architectures

SDR-Based Dual-Polarized L-Band Microwave Radiometer for Small UAS Platforms

Compact, software-defined, dual-polarized L-band microwave radiometer for airborne remote sensing from small unmanned aerial systems

Precision Agriculture: Deep Learning-Driven Crop Mapping from Multi-Temporal Sentinel-2

Leveraging Sentinel-2 time-series data and deep learning to extract vegetation phenology metrics and deliver accurate crop mapping for precision agriculture

Deep learning based RFI detection and mitigation for SMAP using convolutional neural networks

Published in RFI Workshop 2022, 2022

Deep learning based RFI detection and mitigation for SMAP using convolutional neural networks presented at RFI Workshop 2022.

Recommended citation: A. M. Alam and A. C. Gurbuz, "Deep learning based RFI detection and mitigation for SMAP using convolutional neural networks," in Proc. RFI Workshop, 2022.

SMAP Radiometer RFI Prediction with Deep Learning using Antenna Counts

Published in IGARSS 2022, Kuala Lumpur, Malaysia, 2022

SMAP Radiometer RFI Prediction with Deep Learning using Antenna Counts presented at IGARSS 2022, Kuala Lumpur, Malaysia.

Recommended citation: A. M. Alam, A. C. Gurbuz and M. Kurum, "SMAP Radiometer RFI Prediction with Deep Learning using Antenna Counts," in IGARSS 2022, Kuala Lumpur, Malaysia.
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Preliminary Snow Water Equivalent Retrieval of SnowEX20 Swesarr Data

Published in IGARSS 2022, Kuala Lumpur, Malaysia, 2022

Preliminary Snow Water Equivalent Retrieval of SnowEX20 Swesarr Data presented at IGARSS 2022, Kuala Lumpur, Malaysia.

Recommended citation: D. R. Boyd et al., "Preliminary Snow Water Equivalent Retrieval of SnowEX20 Swesarr Data," in IGARSS 2022, Kuala Lumpur, Malaysia.
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Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks

Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 10099-10112, 2022

CNN-based approach for detecting RFI in data collected by NASA’s SMAP microwave radiometer.

Recommended citation: A. M. Alam, M. Kurum, and A. C. Gurbuz, "Radio Frequency Interference Detection for SMAP Radiometer Using Convolutional Neural Networks," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 10099-10112, 2022, doi: 10.1109/JSTARS.2022.3223198.
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Software Radio Testbed for 5G and L-Band Radiometer Coexistence Research

Published in IGARSS 2023, Pasadena, CA, USA, 2023

Software Radio Testbed for 5G and L-Band Radiometer Coexistence Research presented at IGARSS 2023, Pasadena, CA, USA.

Recommended citation: W. Al-Qwider et al., "Software Radio Testbed for 5G and L-Band Radiometer Coexistence Research," in IGARSS 2023, Pasadena, CA, USA.
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High-Resolution Radio Frequency Interference Detection in Microwave Radiometry Using Deep Learning

Published in IGARSS 2023, Pasadena, CA, USA, 2023

High-Resolution Radio Frequency Interference Detection in Microwave Radiometry Using Deep Learning presented at IGARSS 2023, Pasadena, CA, USA.

Recommended citation: A. M. Alam et al., "High-Resolution Radio Frequency Interference Detection in Microwave Radiometry Using Deep Learning," in IGARSS 2023, Pasadena, CA, USA.
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SDR Based Agile Radiometer with Onboard RFI Processing on a Small UAS

Published in IGARSS 2023, Pasadena, CA, USA, 2023

SDR Based Agile Radiometer with Onboard RFI Processing on a Small UAS presented at IGARSS 2023, Pasadena, CA, USA.

Recommended citation: M. M. Farhad et al., "SDR Based Agile Radiometer with Onboard RFI Processing on a Small UAS," in IGARSS 2023, Pasadena, CA, USA.
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An Advanced Testbed for Passive/Active Coexistence Research: A Comprehensive Framework for RFI Detection, Mitigation, and Calibration

Published in USNC-URSI NRSM 2024, Boulder, CO, USA, 2024

An Advanced Testbed for Passive/Active Coexistence Research: A Comprehensive Framework for RFI Detection, Mitigation, and Calibration presented at USNC-URSI NRSM 2024, Boulder, CO, USA.

Recommended citation: A. M. Alam et al., "An Advanced Testbed for Passive/Active Coexistence Research," in USNC-URSI NRSM, Boulder, CO, 2024.
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Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features

Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 748-765, 2024

Deep learning-based calibration method for microwave radiometers using minimal reference data and spectral features.

Recommended citation: A. M. Alam, M. Kurum, M. Ogut, and A. C. Gurbuz, "Microwave Radiometer Calibration Using Deep Learning With Reduced Reference Information and 2-D Spectral Features," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 748-765, 2024, doi: 10.1109/JSTARS.2023.3333268.
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Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed

Published in IEEE DySPAN 2024, Washington, DC, USA, 2024

Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed presented at IEEE DySPAN 2024, Washington, DC, USA.

Recommended citation: A. M. Alam et al., "Evaluation of Conventional Radio Frequency Interference Detection Algorithms in the Presence of 5G Signals in a Controlled Testbed," in IEEE DySPAN, 2024.
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Deep Learning-Based Direction-of-Arrival Estimation with Covariance Reconstruction

Published in IEEE Radar Conference (RadarConf24), Denver, CO, USA, 2024

Deep Learning-Based Direction-of-Arrival Estimation with Covariance Reconstruction presented at IEEE Radar Conference (RadarConf24), Denver, CO, USA.

Recommended citation: A. M. Alam, C. O. Ayna, S. Biswas, J. T. Rogers, J. E. Ball and A. C. Gurbuz, "Deep Learning-Based Direction-of-Arrival Estimation with Covariance Reconstruction," in RadarConf24, Denver, CO, USA, 2024.
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Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Enhanced Activity Recognition

Published in IEEE Radar Conference (RadarConf24), Denver, CO, USA, 2024

Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Enhanced Activity Recognition presented at IEEE Radar Conference (RadarConf24), Denver, CO, USA.

Recommended citation: S. Biswas, A. M. Alam and A. C. Gurbuz, "Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Enhanced Activity Recognition," in RadarConf24, Denver, CO, USA, 2024.
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HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification

Published in IEEE Transactions on Radar Systems, vol. 2, pp. 484-497, 2024

HRSpecNET reconstructs high-resolution micro-Doppler signatures for improved human activity recognition (HAR) using radar data.

Recommended citation: S. Biswas, A. M. Alam, and A. C. Gurbuz, "HRSpecNET: A Deep Learning-Based High-Resolution Radar Micro-Doppler Signature Reconstruction for Improved HAR Classification," in IEEE Transactions on Radar Systems, vol. 2, pp. 484-497, 2024, doi: 10.1109/TRS.2024.3396172.
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SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms

Published in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 9389-9402, 2024

A compact, dual-polarized L-band microwave radiometer system built on software-defined radio (SDR) for deployment on small unmanned aerial systems (UAS).

Recommended citation: M. M. Farhad, A. M. Alam, S. Biswas, M. A. S. Rafi, A. C. Gurbuz, and M. Kurum, "SDR-Based Dual Polarized L-Band Microwave Radiometer Operating From Small UAS Platforms," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 9389-9402, 2024, doi: 10.1109/JSTARS.2024.3394054.
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A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence

Published in IEEE Access, vol. 12, pp. 131522-131540, 2024

An open-source testbed and dataset enabling experimental research on spectrum coexistence between passive sensing and wireless communication systems.

Recommended citation: A. M. Alam, M. Farhad, W. Al-Qwider, A. Owfi, M. Koosha, N. Maston, F. Afgah, V. Marojevic, M. Kurum, A. C. Gurbuz, "A Physical Testbed and Open Dataset for Passive Sensing and Wireless Communication Spectrum Coexistence," in IEEE Access, vol. 12, pp. 131522-131540, 2024, doi: 10.1109/ACCESS.2024.3453774.
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A Deep Learning Approach for High-Accuracy Radiometer Calibration Using SMAP Satellite Data

Published in IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024

A Deep Learning Approach for High-Accuracy Radiometer Calibration Using SMAP Satellite Data presented at IGARSS 2024 - IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece.

Recommended citation: A. M. Alam, M. Kurum, M. Ogut and A. C. Gurbuz, "A Deep Learning Approach for High-Accuracy Radiometer Calibration Using SMAP Satellite Data," in IGARSS 2024, Athens, Greece, pp. 6272-6276, 2024.
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RFI-Net: Enhancing Passive Sensing through Deep Learning Based Time-Frequency Domain Radio Frequency Interference Detection and Mitigation

Published in IEEE Transactions on Geoscience and Remote Sensing, 2025

RFI-Net: A deep learning-based approach for robust time-frequency domain RFI detection and mitigation in passive sensing systems.

Recommended citation: A. M. Alam, M. Kurum, and A. C. Gurbuz, "RFI-Net: Enhancing Passive Sensing through Deep Learning Based Time-Frequency Domain Radio Frequency Interference Detection and Mitigation," submitted to IEEE Transactions on Geoscience and Remote Sensing, 2025.
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Spectrum Coexistence Between Active Technologies and Passive Sensors

Published: November 01, 2025

Invited talk given at the NC State ECE Colloquium, titled “Spectrum Coexistence Between Active Technologies and Passive Sensors.”

Undergraduate Teaching – DIU (2019–2021)

Undergraduate Courses, Daffodil International University, 2021

Between Fall 2019 and Spring 2021 at Daffodil International University (DIU), I taught 24 undergraduate course sections in Electrical and Electronic Engineering, including both lecture and lab courses. Total student enrollment: 467.

Mentored Teaching Fellow – ECE 301, Fall 2025

Undergraduate Course, North Carolina State University, 2025

Selected for NC State’s competitive Mentored Teaching Fellowship (MTF), I co-taught ECE 301: Linear Systems (54 students) under the mentorship of Dr. Ali Gurbuz. My responsibilities included: