In the blood sample management pipeline environment, we have innovatively improved the traditional A-star algorithm to enhance the efficiency of mobile robots. This study employs a grid environmental modeling approach to accurately simulate medical testing laboratories. On the grid map, we utilize an 8-neighbor search method for path planning to accommodate the complex structure within the laboratory. By introducing an improved evaluation function and a bidirectional search strategy, we have successfully reduced the number of search nodes and significantly improved path search efficiency. Additionally, we eliminate redundant nodes in the path, smooth the path using cubic uniform B-spline curves, remove unnecessary inflection points, and further optimize the motion trajectory of the robot. The experimental results of the path planning simulation under different scenarios and specifications show that the improved A-star algorithm has higher search efficiency and traverses fewer nodes compared to the traditional A-star algorithm and the bidirectional A-star algorithm. Overall, the simulation experiments verify the feasibility of the improved A-star algorithm, which can better meet the needs of actual mobile robots in real medical testing laboratories.

The performance of widely linear beamforming (WLB) is superior to adaptive beamforming, but it is limited by the uniform linear array geometry and non-uniform noise. In this paper, to overcome these limitations together, we propose a framework for widely linear beamforming in coprime array with non-uniform noise (WLB-CANUN). We subtract the non-uniform noise component from the coprime array sample covariance matrix, and vectorize the resulted matrix to create the difference co-array (DCA). Since the DCA is not uniform, we interpolate it and recover its signal by formulating the atomic norm minimization problem with the Toeplitz and orthogonal subspace constraints.The pseudo sample covariance matrix of coprime array does not contain the non-uniform noise component, which can be directly vectorized to create the sum co-array (SCA). Due to the non-uniformity of SCA, we interpolate it and recover its signal by formulating another atomic norm minimization problem with the Hankel and orthogonal subspace constraints. The directions of non-circular signals can be estimated by the traditional subspace method, which are utilized to estimate their non-circular coefficients. A least square optimization problem using the sample and pseudo sample covariance matrices of coprime array is formulated and solved to estimate the powers of non-circular signals. The interference-plus-noise covariance matrix (INCM), pseudo INCM and augmented INCM of coprime array are reconstructed, so that the ultimate augmented weight vector can be calculated. Simulation results indicate that the proposed WLB-CANUN method overcomes the limitations of WLB in coprime array with non-uniform noise, and enhances the performance compared to the existing WLB methods.

Relative position awareness is a vital premise for the implementation of emerging intelligent transportation systems. However, commercial Global Satellite Navigation Systems (GNSS) receivers do not satisfy the requirements of these applications. Fortunately, Cooperative Positioning (CP) systems, based on inter-vehicle communications, have improved performance of relative positioning in a Vehicular Ad Hoc Network (VANET). CP techniques rely primarily on measurements from the Global Positioning System (GPS) to deliver measurements or positions that describe the location of individual vehicles. In urban environments, the reduced quality or complete unavailability of GPS measurements challenges the effectiveness of any CP algorithm. In this paper, a new enhanced tightly–coupled CP technique is presented by adding the measurements from low-cost inertial sensors and the Doppler shift of the carrier of Dedicated Short-Range Communications (DSRC) signals. In the enhanced CP method proposed here, vehicles communicate their Inertial Measurement Unit (IMU) data and GPS measurements. Each vehicle fuses the GPS measurements and IMU data and the inter-node range-rates based on the Doppler shift of the carrier of DSRC signals. Based on analytical and experimental results, in a full GPS coverage environment, the new tight integration CP outperforms tight CP with Inertial Navigation System (INS), tight CP and differential GPS by at least by 6%, 15%, and 28%, respectively. In a GPS outage, the performance improvement can be up to 60%, 55%, and 66% respectively.

Transmission line fault detection using drones provides real-time assessment of the operational status of transmission equipment, and therefore it has immense importance in ensuring stable functioning of the transmission lines. Currently, identification of transmission line equipment relies predominantly on manual inspections that are susceptible to the influence of natural surroundings, resulting in sluggishness and a high rate of false detections. In view of this, in this study, we propose an insulator defect recognition algorithm based on a YOLOv5 model with a new lightweight network as the backbone network, combining noise reduction and target detection. First, we propose a new noise reduction algorithm, i.e., the adaptive neighborhood-weighted median filtering (NW-AMF) algorithm. This algorithm employs a weighted summation technique to determine the median value of the pixel point’s neighborhood, effectively filtering out noise from the captured aerial images. Consequently, this approach significantly mitigates the adverse effects of varying noise levels on target detection. Subsequently, the RepVGG lightweight network structure is improved to the newly proposed lightweight structure called RcpVGG-YOLOv5. This structure facilitates single-branch inference, multi-branch training, and branch normalization, thereby improving the quantization performance while simultaneously striking a balance between target detection accuracy and speed. Furthermore, we propose a new loss function, i.e., Focal EIOU, to replace the original CIOU loss function. This optimization incorporates a penalty on the edge length of the target frame, which improves the contribution of the high-quality target gradient. This modification effectively addresses the issue of imbalanced positive and negative samples for small targets, suppresses background positive samples, and ultimately enhances the accuracy of detection. Finally, to align more closely with real-world engineering applications, the dataset utilized in this study consists of machine patrol images captured by the Unmanned Aerial Systems (UAS) of the Yunnan Power Supply Bureau Company. The experimental findings demonstrate that the proposed algorithm yields notable improvements in accuracy and inference speed compared to YOLOv5s, YOLOv7, and YOLOv8. Specifically, the improved algorithm achieves a 3.7% increase in accuracy and a 48.2% enhancement in inference speed compared to those of YOLOv5s. Similarly, it achieves a 2.7% accuracy improvement and a 33.5% increase in inference speed compared to those of YOLOv7, as well as a 1.5% accuracy enhancement and a 13.1% improvement in inference speed compared to those of YOLOv8. These results validate the effectiveness of the proposed algorithm through ablation experiments. Consequently, the method presented in this paper exhibits practical applicability in the detection of aerial images of transmission lines within complex environments. In future research endeavors, it is recommended to continue collecting aerial images for continuous iterative training, to optimize the model further, and to conduct in-depth investigations into the challenges associated with detecting small targets. Such endeavors hold significant importance for the advancement of transmission line detection.

Nowadays, benefiting from its strong capability of nondestructive detection, the ground-penetrating radar (GPR) has been applied to detect and reconstruct underground targets and has drawn lots of attention both in military and civilian fields. However, in the processing of GPR imaging, due to the dispersion errors caused by random distribution of various particles in soil, conventional imaging methods have disadvantages of low signal-noise ratio (SNR), low resolution, and unbalanced amplitude. In this article, to achieve high resolution and high veracity on underground targets’ 3-D reconstruction, we improved the conventional reverse time migration (RTM) algorithm in perspective of medium constitutive relationship. Besides, we extended the improved RTM method in 3-D environments and reconstructed the 3-D structure of several underground targets. To make the RTM algorithm suitable for stepped frequency continuous wave (SFCW) GPR system, we generated three excitation signal models and analyzed the effect of different excitation signals on imaging performance. Finally, through the quantitative analysis of simulation and on-vehicle experimental results, we found that the 3-D images generated by improved RTM method had higher resolution, smaller measurement error, and higher veracity than those of conventional RTM method.

An Ultra-wideband Antenna with funnel-shaped slots and rectangle copper sheets loaded was proposed for ground penetrating radar applications covering the frequency range from 250 MHz to 780 MHz. The performances of the proposed antenna were modeled and optimized by using finite difference time domain methods and measured in both time and frequency domains. The proposed antenna had a bandwidth of 102.9% with voltage standing-wave ratio (VSWR) < 2 and a pick gain over 3 dBi, even when the antenna was bent according to the shape of antenna carrier. In addition, a reconfiguration antenna array with switching matrix, which could be loaded up to 12 antennas, was proposed, where the scanning strategy could be programmed according to the requirements. On this basis, the VSWR of antenna array with and without switching matrix were compared, and the performances of bent and non-bent antenna were also compared in the far-field region. For the radiation pattern, the back-lobe level of bent antennas in E-plane was much lower than non-bent antennas, and the main-lobe of bent antennas in H-plane were rotated. For the peak realized gain, although the antenna gain decreases with increasing frequency, the rate of decline of bent antenna gain is significantly less than that of non-bent antenna gain. Finally, the fabricated antennas could be loaded on a SFCW-GPR system for detecting the depth of cable wells under the road.