Dr. Uttam Kumar Majumder
Research Electronics Engineer at Air Force Research Lab
SPIE Involvement:
Conference Program Committee | Author | Instructor
Publications (35)

Proceedings Article | 19 May 2020
Proc. SPIE. 11393, Algorithms for Synthetic Aperture Radar Imagery XXVII
KEYWORDS: Data modeling, Sensors, Synthetic aperture radar, Gallium nitride, Machine learning, Computer aided design, Target recognition, Automatic target recognition, Model-based design, Solid modeling

Proceedings Article | 11 June 2019
Proc. SPIE. 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI
KEYWORDS: Data modeling, Sensors, Synthetic aperture radar, Image processing, Computing systems, Neural networks, Image classification, Convolution, Analytical research, Target recognition

Proceedings Article | 31 May 2019
Proc. SPIE. 11011, Cyber Sensing 2019
KEYWORDS: Principal component analysis, Detection and tracking algorithms, Visualization, Synthetic aperture radar, Image filtering, Neural networks, Associative arrays, Image classification, Automatic target recognition, Chemical elements

Proceedings Article | 31 May 2019
Proc. SPIE. 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI
KEYWORDS: Convolutional neural networks, Data modeling, Synthetic aperture radar, Image processing, Computing systems, Telecommunications, Neural networks, Image classification, Network architectures, Neurons

Proceedings Article | 14 May 2019
Proc. SPIE. 10988, Automatic Target Recognition XXIX
KEYWORDS: Radar, Information fusion, Image fusion, Data modeling, Cameras, Sensors, Target recognition, Automatic target recognition, Electro optical modeling, Data fusion

Showing 5 of 35 publications
Conference Committee Involvement (20)
Sensors and Systems for Space Applications XIV
11 April 2021 | Orlando, Florida, United States
Signal Processing, Sensor/Information Fusion, and Target Recognition XXX
11 April 2021 | Orlando, Florida, United States
Algorithms for Synthetic Aperture Radar Imagery XXVIII
11 April 2021 | Orlando, Florida, United States
Cyber Sensing 2020
27 April 2020 | Online Only, California, United States
Algorithms for Synthetic Aperture Radar Imagery XXVII
27 April 2020 | Online Only, California, United States
Showing 5 of 20 Conference Committees
Course Instructor
SC1245: Machine Learning Techniques for Radio Frequency Object Classification
The focus of this course will be recent research results, technical challenges, and directions of Deep Learning (DL) based object classification using radar data (i.e., Synthetic Aperture Radar / SAR data). First, we will provide a short overview of machine learning (ML) theory. Then we will provide an example and performance of ML algorithm (i.e., DL method) on video imagery. Finally, we will demonstrate algorithmic implementation and performance of DL algorithms on SAR data (a significant portion of the course time). It is evident that significant research efforts have been devoted to applying DL algorithms on video imagery. However, very limited literature can be found on technical challenges and approaches to execute DL algorithms on radio frequency (RF) data. We will present hands-on implementation of DL-based radar object classification using Caffe and/or TensorFlow tools. Unlike passive sensing (i.e., video collections), Radar enables imaging ground objects at far greater standoff distances and all-weather conditions. Existing non-DL based RF object recognition algorithms are less accurate and require impractically large computing resources. With adequate training data, DL enables more accurate, near real-time, and low-power object recognition system development. We will highlight implementations of DL-based (i.e., Convolution Neural Network (CNN)) SAR object recognition algorithms in graphical processing units (GPUs) and energy efficient computing systems. The examples presented will demonstrate acceptable classification accuracy on relevant SAR data. Further, we will discuss special topics of interest on DL-based RF object recognition as requested by the researchers, practitioners, and students.
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