Autonomous vehicle systems rely heavily upon depth estimation, which facilitates the improvement of precision and stability in automated decision-making systems. Noteworthily, the technique of monocular depth estimation is critical for one of these feasible implementations. In the area of segmentation of medical images, UNet is a well-known encoder–decoder structure. Moreover, several studies have proven its further potential for monocular depth estimation. Similarly, based on UNet, we aim to propose a novel model of monocular depth estimation, which is constructed from the benefits of classical UNet and residual learning mechanisms and named URNet. Particularly, we employ the KITTI dataset in conjunction with the Eigen split strategy to determine the efficacy of our model. Compared with other studies, our URNet is significantly better, on the basis of higher the precision and lower error rate. Hence, it can deal properly with the depth estimation issue for autonomous driving systems. Full article

Microwave imaging is a high-resolution, noninvasive, and noncontact method for detecting hidden defects, cracks, and objects with applications for testing nonmetallic components such as printed circuit boards, biomedical diagnosis, aerospace components inspection, etc. In this paper, an array of microwave sensors designed based on complementary split ring resonators (CSRR) are used to evaluate the hidden features in dielectric media with applications in nondestructive testing and biomedical diagnosis. In this array, each element resonates at a different frequency in the range of 1 GHz to 10 GHz. Even though the operating frequencies are not that high, the acquisition of evanescent waves in extreme proximity to the imaged object and processing them using near-field holographic imaging allows for obtaining high-resolution images. The performance of the proposed method is demonstrated through simulation and experimental results. Full article

The Korean government is facing growing concern over the increasing levels of fine dust. A significant contribution to this problem comes from coastal vessels. To mitigate this, an electric ship propulsion system has been proposed as a solution to reduce air pollution. The application of a fully electric propulsion system in a ship is challenging due to size, capacity limitations, and the cost investment of the battery system. To address the challenges of battery limitation and initial investment costs, the development and supply of removable battery supply systems (RBSSs) for fully electrified battery powered (F-EBP) car ferries are studied. A permanent magnet synchronous motor (PMSM) for the F-EBP car ferry using a roll-on/roll-off-type RBSS is developed in this work. Firstly, the concept of the F-EBP car ferry is discussed, and the specifications of the electric car ferry propulsion system are provided. Secondly, motor design and electromagnetic analysis are performed using finite-element analysis (FEA), where the heat sources including copper loss, core loss, and PM loss are calculated. Mechanical loss is also considered. Finally, a thermal network of the motor is built considering the lumped-parameter model. The results of the thermal analysis indicate that the motor operates within the safe region and can perform well in rated working conditions. Full article

Text recognition in natural scenes has been a very challenging task in recent years, and rich text semantic information is of great significance for the understanding of a scene. However, text images in natural scenes often contain a lot of noise data, which leads to error detection. The problems of high error detection rate and low recognition accuracy have brought great challenges to the task of text recognition. To solve this problem, we propose a text recognition algorithm based on natural scenes. First, the task of text detection and recognition is completed in an end-to-end way in a framework, which can reduce the cumulative error prediction and calculation caused by cascading, and has higher real-time and faster speed. In addition, we integrate a multi-scale attention mechanism to obtain attention features of different scale feature maps. Finally, we use the efficient deep learning network (EE-ACNN), which combines a convolutional neural network (CNN) with an end-to-end algorithm and multi-scale attention to enrich the text features to be detected, expands its receptive field, produces good robustness to the effective natural text information, and improves the recognition performance. Through experiments on text data sets of natural scenes, the accuracy of this method reached 93.87%, which is nearly 0.96–1.02% higher than that of traditional methods, and which proves the feasibility of this method. Full article

The premise of vehicle intelligent decision making is to obtain vehicle motion state parameters accurately and in real-time. Several state parameters cannot be measured directly by vehicle sensors, so estimation algorithms based on filtering are effective solutions. The most representative algorithm is the Kalman filter, especially the standard unscented Kalman filter (UKF) that has been widely used in vehicle state estimation because of its superiority in dealing with nonlinear filtering problems. However, although the UKF assumes that the noise statistics of the system are known, due to the complex and changeable operating conditions, sensor aging and other factors, these noises vary. In order to realize high-precision vehicle state estimation, a noise-adaptive UKF algorithm is proposed in this article. The maximum a posteriori (MAP) algorithm is used to dynamically update the noise of the vehicle system, and it is embedded into the update step of the UKF to form an adaptive unscented Kalman filter (AUKF). The system will dynamically update the noise when noise statistics are unknown and prevent filter divergence by adjusting the mean and covariance of the estimated noise to improve accuracy. On this basis, the proposed method is verified by the joint simulation of CarSim and Matlab/Simulink, confirming that the AUKF performs better than the standard UKF in estimation accuracy and stability under different degrees of noise disturbance, and the estimation accuracy for the yaw rate, side slip angle and longitudinal velocity is improved by 20.08%, 40.98% and 89.91%, respectively. Full article

A dependable and robust technique for nanomachining is ion implantation. In this work, hydrogen (H) ion implantation was used, for the first time, to passivate p-GaN, except for the gate area, in order to create a normally off p-GaN/AlGaN/GaN high-electron-mobility transistor (HEMT). Ion implantation passivation reduces H ion diffusion in p-GaN, allowing it to withstand temperatures above 350 °C. Through experiments and analyses, the H ion implantation energy and dosage required to passivate p-GaN, by generating Mg-H neutral complexes, were determined to be 20 keV and 1.5 × 10¹³ cm⁻², respectively. After conducting annealing procedures at various temperatures, we discovered that 400 °C was the ideal temperature to effectively obtain a normally off p-GaN HEMT. A threshold voltage of 0.8 V was achievable. The p-GaN HEMT also had a breakdown voltage of 642 V at a gate voltage of 0 V, maximum transconductance of 57.7 mS/mm, an on/off current ratio of 10⁸, an on-resistance of 8.4 mm, and a maximum drain current of 240.0 mA/mm at a gate voltage of 6 V after being annealed at 400 °C. Full article

With the proliferation of low-Earth-orbit (LEO) satellites, existing satellite networks need to be enhanced to better handle time-sensitive flows (TSFs). However, the migration of time-sensitive network (TSN) technology to satellite networks is challenged by the large space–time range and limited on-board resources, particularly in providing differentiated quality-of-service guarantees for multilevel TSFs. To address this issue, we propose a multilevel queue-based TSN technique that uses latency requirements as an indicator for traffic scheduling. This approach improves the time-sensitive transmission capability of services with different requirements in LEO satellite networks. We conducted a simulation evaluation under a Walker constellation, and the results demonstrate that our proposed method could significantly improve network throughput, and reduce the packet loss ratio by 90% and the time-out ratio by 14.5%. Additionally, our proposed mechanism could accommodate more TSFs with acceptable latency requirements. Full article

Massive multiple-input multiple-output (MIMO) is a profitable technique to greatly boost spectral efficiency, which has been embraced by the fifth-generation (5G) and sixth-generation (6G) mobile communication systems. By exploiting appropriate downlink precoding algorithms, base stations (BSs) equipped with a large number of antennas are able to provide service to multiple users as well as several cells at the same time and frequency. However, the mutual coupling effect due to the compact antenna array gives misleading results in massive MIMO communication systems. In this paper, we focus on the mutual coupling effect for massive MIMO systems with maximal ratio transmission (MRT), zero-forcing (ZF), regularize ZF (RZF), and minimum mean square error (MMSE) precoding to solve the mutual coupling problem. Additionally, we construct the closed-form expressions of the spectral efficiency (SE) to evaluate the effect of mutual coupling on system performance. Simulation results validate the effectiveness of the proposed mutual coupling effect assessment method and demonstrate the significant impacts of mutual coupling on massive MIMO system performance. Full article

Extracting complete portrait foregrounds from natural images is widely used in image editing and high-definition map generation. When making high-definition maps, it is often necessary to matte passers-by to guarantee their privacy. Current matting methods that do not require additional trimap inputs often suffer from inaccurate global predictions or blurred local details. Portrait matting, as a soft segmentation method, allows the creation of excess areas during segmentation, which inevitably leads to noise in the resulting alpha image as well as excess foreground information, so it is not necessary to keep all the excess areas. To overcome the above problems, this paper designed a contour sharpness refining network (CSRN) that modifies the weight of the alpha values of uncertain regions in the prediction map. It is combined with an end-to-end matting network for bust matting based on the U-Net target detection network containing Residual U-blocks. An end-to-end matting network for bust matting is designed. The network can effectively reduce the image noise without affecting the complete foreground information obtained by the deeper network, thus obtaining a more detailed foreground image with fine edge details. The network structure has been tested on the PPM-100, the RealWorldPortrait-636, and a self-built dataset, showing excellent performance in both edge refinement and global prediction for half-figure portraits. Full article

As one of the important parameters characterizing the cable’s operation status, an accurate measurement of the current is particularly important for the cable’s reliable operation and status monitoring. Aiming at the problem that the sum of the current phasors of multiphase cables is 0 when running in a steady state, and that the traditional mutual inductance current measurement method cannot be used for the phase current measurement of such cables, or that the insulation layer needs to be damaged, this paper proposes a non-contact measurement method of three-phase cable current, based on the magnetic field decoupling calculation. When combined with the actual parameters of the three-phase cable, the analytical calculation model of the magnetic field distribution of the three-phase cable is established. The relationship between the output ratio of the sensor and the deflection angle is obtained through theoretical derivation, the magnetic field coupling coefficient matrix is determined, and the transfer relationship between the output of the magnetic sensor and the current of each phase is clarified; an array magnetic field sensor is designed, which can sense the information of the adjacent magnetic field independently and is used for the reconstruction of three-phase currents. The effectiveness of the proposed method was tested on the built three-phase four-core cable current measurement test platform. The experimental results show that under the three-phase current balance, the measurement error of phase A, phase B and phase C is less than 2.8%, and the waveform and phase angle of the three-phase current can be well restored, which verifies the three-phase current measurement method proposed in this paper. Full article

Improving the training quality of education and training institutions is one of the important reasons for the sustainable development of training institutions. However, traditional education and training institutions lack supervision, and the process system is not standardized, resulting in a lack of authenticity and reliability of information in the training process. Because most of the traditional education and training systems are centralized, it is difficult to share training data between institutions and relevant departments. Therefore, in view of the problems of data tampering and resource sharing in the field of education and training, combined with the node complexity of education and training scenarios, we propose an education and training blockchain platform suitable for multi-node scenarios to ensure the authenticity of the training data and traceability. In this consortium chain, we design specific ledger structures and corresponding smart contracts to meet different business logic requirements. According to different business data types, we optimize the ledger storage method. To improve chaincode utilization and system transaction throughput, we explore different system configuration rules and provide useful insights. Finally, several experiments are performed to evaluate the security and efficiency of the proposed system. Full article

The combination of blockchain and internet of things (IoT) technology realizes reliable storage of IoT data. However, the data stored on the blockchain (on-chain) face the problem of poor scalability and inefficient retrieval. In this paper, the on-chain data scalability schemes based on transactions and smart contracts are first proposed. Subsequently, on the basis of the above on-chain data scalability scheme based on transactions, an on-chain data index based on skip lists is proposed to improve the retrieval efficiency. The experimental results show that both the on-chain data scalability schemes achieve on-chain data scalability while reducing storage overhead. Meanwhile, the on-chain data index based on skip lists has significantly improved dynamic range retrieval efficiency and reduced the time complexity of single data retrieval to O(log(n)). Full article

This research paper studies and highlights the features of the most popular finite control set model predictive control (FCS-MPC) strategies available in the state of the art, which are the optimal switching vector (OSV-MPC), modulated model predictive control (M2PC), and optimal switching sequence (OSS-MPC) methods. Thus, these strategies are studied experimentally by analyzing the transient and steady state performance using a grid tie conventional three-phase two-level voltage source inverter (VSI) with inductive output filter in a Typhoon HIL real-time simulator (RTS) with a Texas Instruments F28379D digital signal processor (DSP). Hence, quantitative indicators, such as the maximum tracking error, the mean absolute error, the settling time, the total harmonic distortion, the switching frequency spectrum, the switching pattern, and the computational burden are compared with the aim to deduce the best strategy for each criteria. Full article

Positron emission tomography (PET) is a popular research topic. People are becoming more interested in PET images as they become more widely available. However, the partial volume effect (PVE) in PET images remains one of the most influential factors causing the resolution of PET images to degrade. It is possible to reduce this PVE and achieve better image quality by measuring and modeling the point spread function (PSF) and then accounting for it inside the reconstruction algorithm. In this work, we examined the response characteristics of the Metis^TM PET/CT system by acquiring ²²Na point source at different locations in the field of view (FOV) of the scanner and reconstructing with small pixel size for images to obtain their radial, tangential, and axial full-width half maximum (FWHM). An image-based model of the PSF model was then obtained by fitting asymmetric two-dimensional Gaussians on the ²²Na images. This PSF model determined by FWHM in three directions was integrated into a three-dimensional ordered subsets expectation maximization (3D-OSEM) algorithm based on a list-mode format to form a new PSF-OSEM algorithm. We used both algorithms to reconstruct point source, Derenzo phantom, and mouse PET images and performed qualitative and quantitative analyses. In the point source study, the PSF-OSEM algorithm reduced the FWHM of the point source PET image in three directions to about 0.67 mm, and in the phantom study, the PET image reconstructed by the PSF-OSEM algorithm had better visual effects. At the same time, the quantitative analysis results of the Derenzo phantom were better than the original 3D-OSEM algorithm. In the mouse experiment, the results of qualitative and quantitative analyses showed that the imaging quality of PSF-OSEM algorithm was better than that of 3D-OSEM algorithm. Our results show that adding the PSF model to the 3D-OSEM algorithm in the Metis^TM PET/CT system helps to improve the resolution of the image and satisfy the qualitative and quantitative analysis criteria. Full article

The current development of multimodal transport networks focuses on the realization of intelligent transport systems (ITS) to manage the prediction of traffic congestion and urban mobility of vehicles and passengers so that alternative routes can be recommended for transport, especially the use of public passenger transport, to achieve sustainable transport. In the article, we propose an algorithm and a methodology for solving multidimensional traffic congestion objectives, especially for intersections, based on combining machine learning with the templates method—the golden template algorithm with the multiagent game theory. Intersections are modeled as independent players who had to reach an agreement using Nash negotiation. The obtained results showed that the Nash negotiation with multiagents and the golden template modeling have superior results to the model predictive control (MPC) algorithm, improving travel time, the length of traffic queues, the efficiency of travel flows in an unknown and dynamic environment, and the coordination of the agents’ actions and decision making. The proposed algorithm can be used in planning public passenger transport on alternative routes and in ITS management decision making. Full article