Title 1: Sparsely Connected CNN for Efficient Automatic Modulation Recognition
Target: IEEE Transactions on Vehicular Technology
Status: Published with doi: 10.1109/TVT.2020.3042638
Title 2: Automatic Modulation Recognition using Deep CNN with Multilevel Fusion Mechanism (Pr)
Target: IEEE Signal Processing Letters
Title 3: Deep Learning for Object Detection Using Micro-Doppler Signatures in Autonomous Vehicles (Pr)
Target: KICS winter 2021
Title 4: Convolutional Neural Network for Drone Detection and Classification on Edge Devices (Pr)
Target: Signal Processing (Elsevier)
Title 1
[1]. Waiting for the publication in a specific issue and volume number.
Title 2
[1]. Experiments and finetuned the architecture.
[3]. Investigated hyperparameter optimization.
Title 3
[1]. Literature review & modification of the architecture.
[2]. Finalized simulation.
Title 4
[1]. Literature review
[2]. Investigated the feasibility of methodology.
Title 1
[1]. Waiting for the publication in a specific issue and volume number.
Title 2
[1]. Continue with Experiments.
[2]. Finetuning a network architecture.
[3]. Continue with the Investigation on hyperparameter optimization.
[4]. Finalize the network architecture.
Title 3
[1]. Finalizing & submit by Tuesday.
[2]. Start the extension for submission into the journal.
Title 4
[1]. Continue with the methodology.
Anti-Drone
[1]. Literature review on the low-complex Deep learning architectures.
[2]. Prepared the data collection scenario based on the open-source datasets.
[1]. Literature review on the low-complex Deep learning architectures.
[2]. Start pre-processing the open-source dataset.
[3]. To continue fixing the LabVIEW communication design toolkit license issues.
[1]. Master MATLAB communication, deep learning, and optimization toolboxes.
[2]. Improve the algorithm of title [2] and finalize experiments.
[3]. Finish the LabVIEW USSRP module and Start RF data collection for the Anti-drone project.
[1]. To submit at least one paper to the SCI journal.
[2]. Attend international and domestic conferences recommended by NSL.
[3]. Acquiring knowledge and skills of NSL technical tools.