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Wireless Power Transfer publishes original research and industrial developments relating to wireless power.
Chief Editor Jiafeng Zhou is Senior Lecturer at the Department of Electrical Engineering and Electronics, University of Liverpool, UK. His research interests include wireless power transfer, energy harvesting, microwave power amplifiers, filters and metamaterial devices.
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Stage Spectrum Sensing Technique for Cognitive Radio Network Using Energy and Entropy Detection
The radio spectrum is one of the world’s most highly regulated and limited natural resources. The number of wireless devices has increased dramatically in recent years, resulting in a scarcity of available radio spectrum due to static spectrum allocation. However, many studies on static allocation show that the licensed spectrum bands are underutilized. Cognitive radio has been considered as a viable solution to the issues of spectrum scarcity and underutilization. Spectrum sensing is an important part in cognitive radio for detecting spectrum holes. To detect the availability or unavailability of primary user signals, many spectrum sensing techniques such as matched filter detection, cyclostationary feature detection, and energy detection have been developed. Energy detection has gained significant attention from researchers because of its ease of implementation, fast sensing time, and low computational complexity. Conventional detectors’ performance degrades rapidly at low SNR due to their sensitivity to the uncertainty of noise. To mitigate noise uncertainty, Shannon, Tsallis, Kapur, and Renyi entropy-based detection has been used in this study, and their performances are compared to choose the best performer. According to the comparison results, the Renyi entropy outperforms other entropy methods. In this study, two-stage spectrum sensing is proposed using energy detection as the coarse stage and Renyi entropy-based detection as the fine stage to improve the performance of single-stage detection techniques. Furthermore, the performance comparison among conventional energy detection, entropy-based detection, and the proposed two-stage techniques over AWGN channel are performed. The parameters such as probability of detection, false alarm probability, miss-detection probability, and receiver operating characteristics curve are used to evaluate the performance of spectrum sensing techniques. It has been shown that the proposed two-stage sensing technique outperforms single-stage energy detection and Renyi entropy-based detection by 11 dB and 1 dB, respectively.
Optimal Path Planning for Wireless Power Transfer Robot Using Area Division Deep Reinforcement Learning
This paper aims to solve the optimization problems in far-field wireless power transfer systems using deep reinforcement learning techniques. The Radio-Frequency (RF) wireless transmitter is mounted on a mobile robot, which patrols near the harvested energy-enabled Internet of Things (IoT) devices. The wireless transmitter intends to continuously cruise on the designated path in order to fairly charge all the stationary IoT devices in the shortest time. The Deep Q-Network (DQN) algorithm is applied to determine the optimal path for the robot to cruise on. When the number of IoT devices increases, the traditional DQN cannot converge to a closed-loop path or achieve the maximum reward. In order to solve these problems, an area division Deep Q-Network (AD-DQN) is invented. The algorithm can intelligently divide the complete charging field into several areas. In each area, the DQN algorithm is utilized to calculate the optimal path. After that, the segmented paths are combined to create a closed-loop path for the robot to cruise on, which can enable the robot to continuously charge all the IoT devices in the shortest time. The numerical results prove the superiority of the AD-DQN in optimizing the proposed wireless power transfer system.
Intelligent Power Grid Video Surveillance Technology Based on Efficient Compression Algorithm Using Robust Particle Swarm Optimization
Companies that produce energy transmit it to any or all households via a power grid, which is a regulated power transmission hub that acts as a middleman. When a power grid fails, the whole area it serves is blacked out. To ensure smooth and effective functioning, a power grid monitoring system is required. Computer vision is among the most commonly utilized and active research applications in the world of video surveillance. Though a lot has been accomplished in the field of power grid surveillance, a more effective compression method is still required for large quantities of grid surveillance video data to be archived compactly and sent efficiently. Video compression has become increasingly essential with the advent of contemporary video processing algorithms. An algorithm’s efficacy in a power grid monitoring system depends on the rate at which video data is sent. A novel compression technique for video inputs from power grid monitoring equipment is described in this study. Due to a lack of redundancy in visual input, traditional techniques are unable to fulfill the current demand standards for modern technology. As a result, the volume of data that needs to be saved and handled in live time grows. Encoding frames and decreasing duplication in surveillance video using texture information similarity, the proposed technique overcomes the aforementioned problems by Robust Particle Swarm Optimization (RPSO) based run-length coding approach. Our solution surpasses other current and relevant existing algorithms based on experimental findings and assessments of different surveillance video sequences utilizing varied parameters. A massive collection of surveillance films was compressed at a 50% higher rate using the suggested approach than with existing methods.
Optimization of a Two-Layer 3D Coil Structure with Uniform Magnetic Field
Conventional magnetically coupled resonant wireless power transfer systems are faced with resonant frequency splitting phenomena and impedance mismatch when a receiving coil is placed at misaligned position. These problems can be avoided by using uniform magnetic field distribution at receiving plane. In this paper, a novel 3D transmitting coil structure with improved uniform magnetic field distribution is proposed based on a developed optimization method. The goal is to maximize the average magnetic field strength and uniform magnetic field section of the receiving plane. Hence, figures of merit (FoM1 and FoM2) are introduced and defined as product of average magnetic field strength and length or surface along which uniform magnetic field is generated, respectively. The validity of the optimization method is verified through laboratory measurements performed on the fabricated coils driven by signal generator at operating frequency of 150 kHz. Depending on the allowed ripple value and predefined coil proportions, the proposed transmitting coil structure gives the uniform magnetic field distribution across 50% to 90% of the receiving plane.
Inhomogeneous Winding for Loosely Coupled Transformers to Reduce Magnetic Loss
Wireless power transfer has been proved promising in various applications. The homogeneous winding method in loosely coupled transformers incurs unnecessary intense magnetic field distribution in the center and causes extra magnetic loss. An inhomogeneous winding method is proposed in this paper, and a relatively homogeneous magnetic field distribution inside the core is achieved. This paper investigated the magnetic loss of homogeneous winding and inhomogeneous winding for wireless power transfer. A theoretical model was built to evaluate magnetic loss under inhomogeneous winding. The coupling coefficient and magnetic loss were investigated individually and comparisons were made between different width ratio combinations. Theoretical analysis was validated in experiments.
An Autonomous Wireless Sensor Node Based on Hybrid RF Solar Energy Harvesting
The widespread deployment of the Internet of Things (IoT) requires the development of new embedded systems, which will provide a diverse array of different intelligent functionalities. However, these devices must also meet environmental, maintenance, and longevity constraints, while maintaining extremely low-power consumption. In this work, a batteryless, low-power consumption, compact embedded system for IoT applications is presented. This system is capable of using a combination of hybrid solar and radiofrequency power sources and operates in the 900 MHz ISM band. It is capable of receiving OOK or ASK modulated data and measuring environmental data and can transmit information back to the requester using GFSK modulated data. The total consumption of the system during its sleep state is 920 nW. Minimum power required to operate is −15.1 dBm or 70 lux, when using only radiofrequency or solar powering, respectively. The system is fully designed with components off the shelf (COTS).