Fedbkd: Heterogenous federated learning via bidirectional knowledge distillation for modulation classification in iot-edge system

P Qi, X Zhou, Y Ding, Z Zhang… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Benefit from the rapid evolution of artificial intelligence and wireless communication
technology, diverse Internet of Things (IoT) devices with edge computing ability have widely …

A novel deep learning and polar transformation framework for an adaptive automatic modulation classification

P Ghasemzadeh, S Banerjee… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Automatic Modulation Classification (AMC) is an approach to identify an observed signal's
most likely modulation scheme without any a priori knowledge of the intercepted signal. In …

Artificial intelligence-driven real-time automatic modulation classification scheme for next-generation cellular networks

Z Kaleem, M Ali, I Ahmad, W Khalid, A Alkhayyat… - IEEE …, 2021 - ieeexplore.ieee.org
Automatic modulation classification (AMC) can play an important role in the timely
identification of suspicious and unwanted signal activities to enable secure communication …

A spatial-diversity MIMO dataset for RF signal processing research

P Ghasemzadeh, M Hempel… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The procedure of classifying a detected signal's modulation scheme with no a priori
information is known as automatic modulation classification (AMC). AMC has presented …

A robust graph convolutional neural network-based classifier for automatic modulation recognition

P Ghasemzadeh, M Hempel… - … and Mobile Computing …, 2022 - ieeexplore.ieee.org
The procedure of automatically recognizing the modulation scheme of the received signal
without any knowledge of the communications parameters employed by the transmitter has …

A trustworthy model of recommender system using hyper-tuned restricted boltzmann machine

GK Jha, M Gaur, P Ranjan, HK Thakur - Multimedia Tools and …, 2023 - Springer
The rapid and ubiquitous digital revolution has led to acceleration towards a digitally
connected world where accepting recommendations digitally has become a part of our e …

Introduction to Neuromorphic Computing Systems

LJ Ahmed, S Dhanasekar, KM Sagayam… - … Systems for Industry …, 2023 - igi-global.com
The process of using electronic circuits to replicate the neurobiological architectures seen in
the nervous system is known as neuromorphic engineering, also referred to as …

A novel graph neural network-based framework for automatic modulation classification in mobile environments

P Ghasemzadeh - 2023 - search.proquest.com
Automatic modulation classification (AMC) refers to a signal processing procedure through
which the modulation type and order of an observed signal are identified without any prior …

Modeling and Performance Evaluation of an RF Transceiver System at 160 MHz for Railroad Environments

P Ghasemzadeh, M Hempel… - ASME/IEEE Joint …, 2022 - asmedigitalcollection.asme.org
With the continued proliferation of wireless applications across North America's railroad
industry, it is vital to explore performance capabilities and constraints across different …

Detecting dark cars in railroad operations using multi-antenna beamforming for long-distance discovery and identification of aei tags

P Ghasemzadeh, S Banerjee, M Hempel… - 2020 International …, 2020 - ieeexplore.ieee.org
One recurring problem that the North American railroad industry faces is the tracking of lost
railroad cars. Railroad cars are often parked on track sidings of train stations or train depot …