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Invited ECE Colloquium Talk on Spectrum Coexistence – NC State, Nov 2025
Published:
I will be presenting an invited talk at the NC State ECE Colloquium in November 2025.
Advanced to Ph.D. Candidacy
Published:
portfolio
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
publications
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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talks
Spectrum Coexistence Between Active Technologies and Passive Sensors
Published:
Invited talk given at the NC State ECE Colloquium, titled “Spectrum Coexistence Between Active Technologies and Passive Sensors.”
teaching
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: