304 citations · 335 across the 9 of their papers we have counts for
13 papers
Hybrid Indoor Localization via Reinforcement Learning-based Information Fusion
Mohammad Salimibeni, Arash Mohammadi
The paper is motivated by the importance of the Smart Cities (SC) concept for future management of global urbanization. Among all Internet of Things (IoT)-based communication techn…
JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization
Zohreh Hajiakhondi-Meybodi, Ming Hou, Arash Mohammadi
Ultra-Wideband (UWB) is one of the key technologies empowering the Internet of Thing (IoT) concept to perform reliable, energy-efficient, and highly accurate monitoring, screening,…
AKF-SR: Adaptive Kalman Filtering-based Successor Representation
Parvin Malekzadeh, Mohammad Salimibeni, Ming Hou +2
Recent studies in neuroscience suggest that Successor Representation (SR)-based models provide adaptation to changes in the goal locations or reward function faster than model-free…
TraHGR: Transformer for Hand Gesture Recognition via ElectroMyography
Soheil Zabihi, Elahe Rahimian, Amir Asif +1
Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown significant potential for development of advanced myoelectric-contro…
DF-SSmVEP: Dual Frequency Aggregated Steady-State Motion Visual Evoked Potential Design with Bifold Canonical Correlation Analysis
Raika Karimi, Arash Mohammadi, Amir Asif +1
Recent advancements in Electroencephalography (EEG) sensor technologies and signal processing algorithms have paved the way for further evolution of Brain Computer Interfaces (BCI)…
Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization
Mohammadamin Atashi, Mohammad Salimibeni, Arash Mohammadi
Internet of Things (IoT)-based indoor localization has gained significant popularity recently to satisfy the ever-increasing requirements of indoor Location-based Services (LBS). I…
Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries
Parnian Afshar, Konstantinos N. Plataniotis, Arash Mohammadi
According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest…
From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities
Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis +2
Recent advancements in signal processing and machine learning coupled with developments of electronic medical record keeping in hospitals and the availability of extensive set of m…
Brain Tumor Type Classification via Capsule Networks
Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis
Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults. Consequently, determining the correct type of brain tumor in early…
Improved Explainability of Capsule Networks: Relevance Path by Agreement
Atefeh Shahroudnejad, Arash Mohammadi, Konstantinos N. Plataniotis
Recent advancements in signal processing and machine learning domains have resulted in an extensive surge of interest in deep learning models due to their unprecedented performance…
WAKE: Wavelet Decomposition Coupled with Adaptive Kalman Filtering for Pathological Tremor Extraction
Soroosh Shahtalebi, Seyed Farokh Atashzar, Rajni V. Patel +1
Pathological Hand Tremor (PHT) is among common symptoms of several neurological movement disorders, which can significantly degrade quality of life of affected individuals. Beside…
Performance Constrained Distributed Event-triggered Consensus in Multi-agent Systems
Amir Amini, Arash Mohammadi, Amir Asif
The paper proposes a distributed eventtriggered consensus approach for linear multi-agent systems with guarantees over rate of convergence, resilience to control gain uncertainties…
Distributed Computation of the Conditional PCRLB for Quantized Decentralized Particle Filters
Arash Mohammadi, Amir Asif, Xionghu Zhong +1
The conditional posterior Cramer-Rao lower bound (PCRLB) is an effective sensor resource management criteria for large, geographically distributed sensor networks. Existing algorit…