In the Mobile Robotics domain, the ability of robots to locate themselves is one of the most important events. By locating, mobile robots can obtain information about the environment and continuously track their position and direction. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is applied most often in robot localization, a two-dimensional environment probabilistic localization system to improve the problems such as high computational complexity and hijacking of mobile robots that exist in the traditional MCL method. The proposed method is based on 2D laser information, range finder information, and AMCL to accomplish the localization task. Furthermore, an optimized AMCL algorithm is proposed to increase the accuracy of localization in terrain that is easy to fail to locate, have a chance to locate successfully when a localization error occurs, and apply the optimized AMCL to the mobile robot system. From the experimental results, we know that the improved AMCL algorithm can enhance the positioning accuracy of the robot effectively, which has better practicality than the original AMCL.

Sound signals have been widely applied in various fields. One of the popular applications is sound localization, where the location and direction of a sound source are determined by analyzing the sound signal. In this study, two microphone linear arrays were used to locate the sound source in an indoor environment. The TDOA is also designed to deal with the problem of delay in the reception of sound signals from two microphone arrays by using the generalized cross-correlation algorithm to calculate the TDOA. The proposed microphone array system with the algorithm can successfully estimate the sound source’s location. The test was performed in a standardized chamber. This experiment used two microphone arrays, each with two microphones. The experimental results prove that the proposed method can detect the sound source and obtain good performance with a position error of about 2.0~2.3 cm and angle error of about 0.74 degrees. Therefore, the experimental results demonstrate the feasibility of the system.

The vehicle license plate detection plays a key role in Intelligent Transportation Systems. Detecting license plates such as cars, trucks, and vans is useful for law enforcement, surveillance, and toll booth operations. How to detect license plates quickly and accurately is crucial for license plate recognition. However, the uneven light condition or the oblique shooting angle of the license plate to be detected changes dramatically complex in real-world capture scenarios, and the detection difficulty increases. At the same time, distance, lighting, angle, and other requirements are quite high, which seriously affects the detection performance. Therefore, an improved YOLOv7 integrating the parameter-free attention module SimAM for license plate detection was proposed, namely, YOLO-SLD. Without modifying the original ELAN architecture, which is the key component of YOLOv7, a SimAM mechanism was added at the end of the ELAN to better extract license plate features and increase computational efficiency. More importantly, the SimAM module does not require any parameters to be added to the original YOLOv7 network, reducing model computation, and simplifying the calculation process. The performance of the detection model with different attention mechanisms was tested on the CCPD dataset for the first time, and the proposed method was proven to be effective. The experimental result shows that the YOLO-SLD model has a higher detection accuracy and is more lightweight with mAP at 0.5 with the overall improvement in accuracy from 98.44% of the original YOLOv7 model to 98.91%, an increase of 0.47% in accuracy. The accuracy of the CCPD test subset in dark and light images is improved from 93.5% to 96.7%, an increase of 3.2% in accuracy. The parameter size of the model is reduced by 1.2 million parameters compared to the original YOLOv7 model. Its performance is better than the other prevalent license plate detection algorithms.

This paper proposes a simple and small-dimensioned antenna that can provide X band and Ku band for the low-earth-orbiting (LEO) satellite system in an Internet of vehicles system. The antenna is designed on the substrate Arlon DiClad 880. The antenna structure consists of an inverted triangle geometry and an inverted U-shaped slot. The dimensions of the antenna are 12.5 × 5 mm², and the area of the substrate is 30 × 13 × 0.254 mm³. The antenna is easy to make, and the manufacturing cost is low. The measurement results of the reflection coefficient (lower than −10 dB) of the antenna show that the working frequency band can cover the X-band (10.87–12.76 GHz) and the Ku band (15.19–16.02 GHz). The measured and simulated results are fairly similar. The efficiency of the antenna in the X-band is about 50–80.8%. The efficiency of the antenna in the Ku-band is about 50–74%. The gains of the antennas are about 3.34–6.08 dBi and 3.50–4.65 dBi in the X-band and Ku band, respectively, and the highest gain is 6.08 dBi. The antenna design can realize the features of low cost and small dimensions in autonomous vehicles and vehicle networking communication system equipment and achieve good wireless transmission capabilities from vehicles to the base station in the IOV.

The paper proposes a four-directional beamforming switched antenna system based on Butler matrix for 28 GHz mmWave. It is made up of a 90-degree coupler, a crossover, and a 45-degree phase shifter. A 4×4 Butler matrix is combined with a 28 GHz patch antenna to form a Butler network antenna. Four directions of the radiation beam can be changed when the input position is changed. From the simulation and measurement results, the measured reflection coefficients cover 27.9-29.3 GHz at all four ports with a standard reflection coefficient of -10 dB and four different angles of 15°, -46°, +44° and -13° for switching beam direction. The antenna’s compact size of 39.1 × 45.6 mm² (3.65λ ₀ ×4.26λ ₀ ) makes it ideal for use in millimeter wave bands for 5G NR n258, and n261 applications, while also providing a high gain of 11.5 dBi.