The yielding and displaying of the 3D images of object have been investigated for many years. The methods of designing non-spherical or complicated wavefront are important for fabrications of different optical elements with low aberrations, and also very useful for the synthesis of rainbow holograms of the 3D true-color objects. The display of 3D wavefront can be realized by several methods. In this paper the technique and system of holographic synthesis of computer designed-3D wavefront are described. The 2D amplitude distribution of light wavefront of object in the recording plane can be created by computer 3D image design and by sampling the wavefront in the angles. Then, these amplitude distributions after modified by computer are output into the holographic recording systems by means of high resolution liquid crystal display (LCD) plate to form the synthetic holographic master. The master is converted into image-plane rainbow hologram in the holographic systems. The advantages of the technique, compared with the CGH and conventional holographic method, are obvious. The LCD output is simple and fast. The wavefronts of true-color object can also be fabricated by this system. The different coding holographic gratings and elements, the true-color 3D rainbow holograms can be made successfully with the technique combined with spatial frequency-coding and color- controlling technology.
Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution.Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies.It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action.However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible.To address these issues, a novel adaptive traffic signal timing scheme is proposed.It's based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated.In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively.Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications.The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios.Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.
In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.
In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter $V$ . A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as $[\mathcal {O}(1/V),\mathcal {O}(V)]$ . Since the value of $V$ can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.
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