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Abstract-Based on state-of-the-art deep reinforcement learning (Deep RL) algorithms, two controllers are proposed to pass a ship through a specified gate. Deep RL is a powerful approach to learn a complex controller which is expected to adapt to different situations of systems. This paper explains how to apply these algorithms to ship steering problem. The simulation results show advantages of these algorithms in reproducing reliable and stable controllers.

Spatial and temporal characteristics of the propagation channel have a significant influence on multiantenna method applicability for fifth-generation- (5G-) enabled Internet of Things (IoT). In this paper, the statistical characteristics of a novel three-dimensional (3D) geometric-based stochastic model for next-generation vehicle-to-vehicle (V2V) multiple-input multiple-output (MIMO) communications under the nonisotropic scattering environment are investigated. In both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions, the proposed model investigates the spatial, frequency, and temporal domain statistical distribution of multipath received signals by using the time-variant transfer function for indoor environments. The probability density function (PDF) of separation distance between the transceiver antennas, angle-of-arrival (AoA), and angle-of-departure (AoD) in the azimuth and elevation planes is derived by using closed-form expressions. For the space, time, and frequency correlation function (STF-CF), a precise analytical expression is derived based on MIMO antenna system. We further determine the effects of several model parameters on the V2V channel performance, such as tunnel width, antenna array spacing, Ricean K -factor, and moving velocity. The statistical characteristics of the MIMO channel model are validated by simulation results, confirming the flexibility and effectiveness of our proposed model in the tunnel scenario.

In this paper, we propose a controller for a bicycle using the DDPG (Deep Deterministic Policy Gradient) algorithm, which is a state-of-the-art deep reinforcement learning algorithm. We use a reward function and a deep neural network to build the controller. By using the proposed controller, a bicycle can not only be stably balanced but also travel to any specified location. We confirm that the controller with DDPG shows better performance than the other baselines such as Normalized Advantage Function (NAF) and Proximal Policy Optimization (PPO). For the performance evaluation, we implemented the proposed algorithm in various settings such as fixed and random speed, start location, and destination location.

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Objective image quality assessment (IQA) is imperative in the current multimedia-intensive world, in order to assess the visual quality of an image at close to a human level of ability. Many parameters such as color intensity, structure, sharpness, contrast, presence of an object, etc., draw human attention to an image. Psychological vision research suggests that human vision is biased to the center area of an image and display screen. As a result, if the center part contains any visually salient information, it draws human attention even more and any distortion in that part will be better perceived than other parts. To the best of our knowledge, previous IQA methods have not considered this fact. In this paper, we propose a full reference image quality assessment (FR-IQA) approach using visual saliency and contrast; however, we give extra attention to the center by increasing the sensitivity of the similarity maps in that region. We evaluated our method on three large-scale popular benchmark databases used by most of the current IQA researchers (TID2008, CSIQ and LIVE), having a total of 3345 distorted images with 28 different kinds of distortions. Our method is compared with 13 state-of-the-art approaches. This comparison reveals the stronger correlation of our method with human-evaluated values. The prediction-of-quality score is consistent for distortion specific as well as distortion independent cases. Moreover, faster processing makes it applicable to any real-time application. The MATLAB code is publicly available to test the algorithm and can be found online 1 . 1