Titlebook: Machine Learning and Intelligent Communications; Third International Limin Meng,Yan Zhang Conference proceedings 2018 ICST Institute for C

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Real-Time Drone Detection Using Deep Learning Approachs in real time. In this paper, we design a real-time drone detector using deep learning approach. Specifically, we improve a well-performed deep learning model, i.e., You Only Look Once, by modifying its structure and tuning its parameters to better accommodate drone detection. Considering that a ro

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Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Mobile Edge Computinguire a satisfactory task offloading and resource allocation decision for each user so as to minimize energy consumption and delay. In this paper, we propose a deep reinforcement learning-based approach to solve joint task offloading and resource allocation problems. Simulation results show that the

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RFID Data-Driven Vehicle Speed Prediction Using Adaptive Kalman Filter First of all, when the vehicle moves through a RFID tag, the reader needs to acquire the state information (i.e., current speed and time stamp) of the last vehicle across the tag, and meanwhile transmits its state information to this tag. Then, the state space model can be formulated according to t

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Speed Prediction of High Speed Mobile Vehicle Based on Extended Kalman Filter in RFID Systemrs. To this end, through using RFID (Radio Frequency Identification) data, this paper proposes a vehicle speed prediction algorithm based on Extended Kalman Filter (EKF). Specifically, the proposed algorithm works as follows. First, the RFID reader equipped in the vehicle acquires the state informat

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