Ground-breaking optical wireless power transfer (OWPT) techniques have gained significant attention from both academia and industry in recent decades. Powering remote systems through laser diodes (LDs) to either operate devices or recharge batteries offers several benefits. Remote LDs can remove the burden of carrying extra batteries and can reduce mission time by removing battery swap-time and charging. Apart from its appealing benefits, laser power transfer (LPT) is still a challenging task due to its low transfer efficiency. In this paper, we discuss the necessity and feasibility of OWPT and discuss several projects, working principle, system design, and components. In addition, we show that OWPT is an essential element to supply power to Internet-of-Things (IoT) terminals. We also highlight the impacts of dynamic OWPT. We outline several OWPT techniques including optical beamforming, distributed laser charging (DLC), adaptive-DLC (ADLC), simultaneous lightwave information and power transfer (SLIPT), Thing-to-Thing (T2T) OWPT, and high intensity laser power beaming (HILPB). We also deal with laser selection, hazard analysis, and received photovoltaic (PV) cell selection for OWPT systems. Finally, we discuss a range of open challenges and counter measures. We believe that this review will be helpful in integrating research and eliminating technical uncertainties, thereby promoting progress and innovation in the development of OWPT technologies.
In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include time, frequency, space, and power, so multiple access technologies such as time division multiple access (TDMA), frequency division multiple access (FDMA), space division multiple access (SDMA), code division multiple access (CDMA), and non-orthogonal multiple access (NOMA) are widely used. In semantic communication, which is considered a new paradigm of the next-generation communication system, we extract high-dimensional features from signal sources in a model-based artificial intelligence approach from a semantic perspective and construct a model information space for signal sources and channel features. From the high-dimensional semantic space, we excavate the shared and personalized information of semantic information and propose a novel multiple access technology, named model division multiple access (MDMA), which is based on the resource of the semantic domain. From the perspective of information theory, we prove that MDMA can attain more performance gains than traditional multiple access technologies. Simulation results show that MDMA saves more bandwidth resources than traditional multiple access technologies, and that MDMA has at least a 5-dB advantage over NOMA in the additive white Gaussian noise (AWGN) channel under the low signal-to-noise (SNR) condition.
In this paper, an efficient image encryption scheme based on a novel mixed linear–nonlinear coupled map lattice (NMLNCML) system and DNA operations is presented. The proposed NMLNCML system strengthens the chaotic characteristics of the system, and is applicable for image encryption. The main advantages of the proposed method are embodied in its extensive key space; high sensitivity to secret keys; great resistance to chosen-plaintext attack, statistical attack, and differential attack; and good robustness to noise and data loss. Our image cryptosystem adopts the architecture of scrambling, compression, and diffusion. First, a plain image is transformed to a sparsity coefficient matrix by discrete wavelet transform, and plaintext-related Arnold scrambling is performed on the coefficient matrix. Then, semi-tensor product (STP) compressive sensing is employed to compress and encrypt the coefficient matrix. Finally, the compressed coefficient matrix is diffused by DNA random encoding, DNA addition, and bit XOR operation. The NMLNCML system is applied to generate chaotic elements in the STP measurement matrix of compressive sensing and the pseudo-random sequence in DNA operations. An SHA-384 function is used to produce plaintext secret keys and thus makes the proposed encryption algorithm highly sensitive to the original image. Simulation results and performance analyses verify the security and effectiveness of our scheme.
Modern underwater object detection methods recognize objects from sonar data based on their geometric shapes. However, the distortion of objects during data acquisition and representation is seldom considered. In this paper, we present a detailed summary of representations for sonar data and a concrete analysis of the geometric characteristics of different data representations. Based on this, a feature fusion framework is proposed to fully use the intensity features extracted from the polar image representation and the geometric features learned from the point cloud representation of sonar data. Three feature fusion strategies are presented to investigate the impact of feature fusion on different components of the detection pipeline. In addition, the fusion strategies can be easily integrated into other detectors, such as the You Only Look Once (YOLO) series. The effectiveness of our proposed framework and feature fusion strategies is demonstrated on a public sonar dataset captured in real-world underwater environments. Experimental results show that our method benefits both the region proposal and the object classification modules in the detectors.
With the reduction in manufacturing and launch costs of low Earth orbit satellites and the advantages of large coverage and high data transmission rates, satellites have become an important part of data transmission in air-ground networks. However, due to the factors such as geographical location and people’s living habits, the differences in user’ demand for multimedia data will result in unbalanced network traffic, which may lead to network congestion and affect data transmission. In addition, in traditional satellite network transmission, the convergence of network information acquisition is slow and global network information cannot be collected in a fine-grained manner, which is not conducive to calculating optimal routes. The service quality requirements cannot be satisfied when multiple service requests are made. Based on the above, in this paper artificial intelligence technology is applied to the satellite network, and a software-defined network is used to obtain the global network information, perceive network traffic, develop comprehensive decisions online through reinforcement learning, and update the optimal routing strategy in real time. Simulation results show that the proposed reinforcement learning algorithm has good convergence performance and strong generalizability. Compared with traditional routing, the throughput is 8% higher, and the proposed method has load balancing characteristics.
To solve the problems of incomplete topic description and repetitive crawling of visited hyperlinks in traditional focused crawling methods, in this paper, we propose a novel focused crawler using an improved tabu search algorithm with domain ontology and host information (FCITS_OH), where a domain ontology is constructed by formal concept analysis to describe topics at the semantic and knowledge levels. To avoid crawling visited hyperlinks and expand the search range, we present an improved tabu search (ITS) algorithm and the strategy of host information memory. In addition, a comprehensive priority evaluation method based on Web text and link structure is designed to improve the assessment of topic relevance for unvisited hyperlinks. Experimental results on both tourism and rainstorm disaster domains show that the proposed focused crawlers overmatch the traditional focused crawlers for different performance metrics.
Localization plays a vital role in the mobile robot navigation system and is a fundamental capability for autonomous movement. In an indoor environment, the current mainstream localization scheme uses two-dimensional (2D) laser light detection and ranging (LiDAR) to build an occupancy grid map with simultaneous localization and mapping (SLAM) technology; it then locates the robot based on the known grid map. However, such solutions work effectively only in those areas with salient geometrical features. For areas with repeated, symmetrical, or similar structures, such as a long corridor, the conventional particle filtering method will fail. To solve this crucial problem, this paper presents a novel coarse-to-fine paradigm that uses visual features to assist mobile robot localization in a long corridor. First, the mobile robot is remote-controlled to move from the starting position to the end along a middle line. In the moving process, a grid map is built using the laser-based SLAM method. At the same time, a visual map consisting of special images which are keyframes is created according to a keyframe selection strategy. The keyframes are associated with the robot’s poses through timestamps. Second, a moving strategy is proposed, based on the extracted range features of the laser scans, to decide on an initial rough position. This is vital for the mobile robot because it gives instructions on where the robot needs to move to adjust its pose. Third, the mobile robot captures images in a proper perspective according to the moving strategy and matches them with the image map to achieve a coarse localization. Finally, an improved particle filtering method is presented to achieve fine localization. Experimental results show that our method is effective and robust for global localization. The localization success rate reaches 98.8% while the average moving distance is only 0.31 m. In addition, the method works well when the mobile robot is kidnapped to another position in the corridor.
Low-voltage electrical apparatuses (LVEAs) have many workpieces and intricate geometric structures, and the assembly process is rigid and labor-intensive, and has little balance. The assembly process cannot readily adapt to changes in assembly situations. To address these issues, a collaborative assembly is proposed. Based on the requirements of collaborative assembly, a colored Petri net (CPN) model is proposed to analyze the performance of the interaction and self-government of robots in collaborative assembly. Also, an artificial potential field based planning algorithm (AFPA) is presented to realize the assembly planning and dynamic interaction of robots in the collaborative assembly of LVEAs. Then an adaptive quantum genetic algorithm (AQGA) is developed to optimize the assembly process. Lastly, taking a two-pole circuit-breaker controller with leakage protection (TPCLP) as an assembly instance, comparative results show that the collaborative assembly is cost-effective and flexible in LVEA assembly. The distribution of resources can also be optimized in the assembly. The assembly robots can interact dynamically with each other to accommodate changes that may occur in the LVEA assembly.
In this paper, the problem of controllability of Boolean control networks (BCNs) with multiple time delays in both states and controls is investigated. First, the controllability problem of BCNs with multiple time delays in controls is considered. For this controllability problem, a controllability matrix is constructed by defining a new product of matrices, based on which a necessary and sufficient controllability condition is obtained. Then, the controllability of BCNs with multiple time delays in states is studied by giving a necessary and sufficient condition. Subsequently, based on these results, a controllability matrix for BCNs with multiple time delays in both states and controls is proposed that provides a concise controllability condition. Finally, two examples are given to illustrate the main results.
This work presents a novel design of Ka-band (33 GHz) filtering packaging antenna (FPA) that features broadband and great filtering response, and is based on glass packaging material and through-glass via (TGV) technologies. Compared to traditional packaging materials (printed circuit board, low temperature co-fired ceramic, Si, etc.), TGVs are more suitable for miniaturization (millimeter-wave three-dimensional (3D) packaging devices) and have superior microwave performance. Glass substrate can realize 3D high-density interconnection through bonding technology, while the coefficient of thermal expansion (CTE) matches that of silicon. Furthermore, the stacking of glass substrate enables high-density interconnections and is compatible with micro-electro-mechanical system technology. The proposed antenna radiation patch is composed of a patch antenna and a bandpass filter (BPF) whose reflection coefficients are almost complementary. The BPF unit has three pairs of λg/4 slots (defect microstrip structure, DMS) and two λg/2 U-shaped slots (defect ground structure, DGS). The proposed antenna achieves large bandwidth and high radiation efficiency, which may be related to the stacking of glass substrate and TGV feed. In addition, the introduction of four radiation nulls can effectively improve the suppression level in the stopband. To demonstrate the performance of the proposed design, a 33-GHz broadband filtering antenna is optimized, debugged, and measured. The antenna could achieve |S11|<-10 dB in 29.4-36.4 GHz, and yield an impedance matching bandwidth up to 21.2%, with the stopband suppression level at higher than 16.5 dB. The measurement results of the proposed antenna are a realized gain of ~6.5 dBi and radiation efficiency of ~89%.