Automatic detection of artifacts in EEG by combining deep learning and histogram contour processing
This paper introduces a simple approach combining deep learning and histogram contour processing for automatic detection of various types of artifact contaminating […]
Data-Driven Predictive Scheduling in Ultra-Reliable Low-Latency Industrial IoT
To date, model-based reliable communication with low latency is of paramount importance for time-critical wireless control systems. In this work, we study […]
Proxy Experience Replay
Traditional distributed deep reinforcement learning (RL) commonly relies on exchanging the experience replay memory (RM) of each agent. Since the RM contains […]
Hierarchical User Clustering for mmWave-NOMA Systems
Non-orthogonal multiple access (NOMA) and mmWave are two complementary technologies that can support the capacity demand that arises in 5G and beyond […]
Massive autonomous UAV path planning
This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source […]
Remote UAV Online Path Planning via Neural Network-Based Opportunistic Control
This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV’s […]
Framework for the Identification of Rare Events via Machine Learning and IoT Networks
This paper introduces an industrial cyber-physical system (CPS) based on the Internet of Things (IoT) that is designed to detect rare events […]
Massive MIMO Detection Techniques
Massive multiple-input multiple-output (MIMO) is a key technology to meet the user demands in performance and quality of services (QoS) for next […]
A part power set model for scale-free person retrieval
Recently, person re-identification (re-ID) has attracted increasing research attention, which has broad application prospects in video surveillance and beyond. To this end, […]
Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning
Objective: To assess the ability of radiography-based bone texture variables in proximal femur and acetabulum to predict incident radiographic hip osteoarthritis (rHOA) […]