Electronics Letters最新文献

In the realms of 5G and the upcoming 6G communications, the millimeter-wave bands offer substantially greater bandwidth. However, due to their propagation characteristics, aligning antennas becomes a challenging issue, particularly at the node side in industrial and vehicular network applications. Angle-scanning antennas can significantly enhance communication reliability or provide new possibilities for spatial multiplexing. Previous studies have predominantly concentrated on frequency-scanning antennas characterized by either low-gain individual units or expensive digital arrays. Consequently, there has been a scarcity of solutions that are both cost-effective and high-gain. In this study, a method is proposed for achieving frequency scanning by extending the feed lines between elements. Based on this approach, a millimeter-wave high-gain serial-feed frequency-scanning antenna is developed. This antenna operates within the 60–64 GHz millimeter-wave ISM (Industrial, Scientific, and Medical) band and is capable of performing a 10$�{}^{\circ }$ scan in the E-plane. Moreover, the antenna maintains a width of less than half a wavelength, allowing for the assembly of multiple antennas into a MIMO array.

The kT/C noise cancellation technique can effectively reduce the digital-to-analog converter (DAC) size for successive approximation register ADCs and thus relax the burden for input drivers and reference buffers. However, the prior kT/C noise cancellation technique suffers from a hard trade-off between the noise, amplifier bandwidth and linearity. Here, an improved kT/C noise cancellation technique is proposed to break the trade-off. It uses presampling to hold the input signal unchanged during the noise cancelling phase, leading to significantly relaxed requirements on the bandwidth and linearity of the amplifier. The proposed technique is verified in a 14-bit 200 MS/s SAR analog-to-digital converter (ADC) with a DAC size of 128 fF. Simulation results show that this work achieves 75.6 dB signal to noise and distortion ratio and consumes 1.56 mW power.

The problem of sparse decoupling radar imaging methods based on deep learning is researched. An improved model-driven learning imaging network with a complex-valued convolution block attention module plugged into each sub-network is proposed. This method can solve the high sidelobe and coupling problem in sparse wideband Multiple-Input Multiple-Output (MIMO) radar. In addition, it can better focus on the target area and capture target information to boost model representation power. Experimental results verify the validity of the proposed method.

This paper presents a kernel extreme learning machine (KELM) integrated with the improved whale optimization algorithm (IWOA) to address the power quality disturbance (PQD) issue in microgrids. First, an adaptive variational mode decomposition method is employed to extract PQD signals in microgrids. Then, the IWOA is utilized to optimize the penalty factor and kernel function parameters for the KELM classifier model, thereby enhancing the performance of the classifier. Furthermore, the test results indicate that the proposed IWOA–KELM achieves high classification accuracy and rapid convergence for complex PQD signals.

This letter introduces an ultra-low-power temperature sensor utilizing dynamic leakage-suppression (DLS) logic and thoroughly analyses its working principle. The sensor effectively tackles the weak pull-up challenge inherent in DLS logic ensuring its compatibility with standard digital logic. By capitalizing on the super cut-off attribute of DLS logic, the frontend of the sensor achieves ultra-low power consumption, without compromising on measurement precision or the breadth of the temperature range. The digital part of the proposed utilizes the output frequency of the sensor's frontend as the clock source, in conjunction with an external 50 Hz reference clock, achieving a low overall power consumption. The frontend of the temperature sensor was fabricated using a 180 nm process, occupying a minimal area of 374 $��\mu �m^2�$. The digital part of the circuit is implemented using FPGA. Following a two-point calibration and system error removal, the sensor, operating at a supply voltage of 0.8 V, demonstrated a $��3�\delta �$ error of $��\pm �0.54�$ $��{}^{\circ }�C�$ across the temperature range of −20 to 125 $��{}^{\circ }�C�$. At 25 $��{}^{\circ }�C�$, the resolutio

This article presents a novel control approach using a two-degree-of-freedom (2-DOF) Smith predictor structure for double integrating plus time delay plants. The PD and PID controllers, integral components of the Smith predictor scheme, serve distinct roles in the control process. The PD controller is responsible for set-point tracking, while both PD and PID controllers contribute to the rejection of load disturbances. Each controller is designed using the pole placement technique, with an appropriate tuning parameter established to balance performance and robustness effectively. A low-pass filter is incorporated to improve the response of the set-point signal by attenuating high-frequency noise. The proposed method's effectiveness is compared with Skogestad internal model control, a well-known PID tuning method, to benchmark performance improvements and demonstrate the advantages of the 2-DOF Smith predictor approach. Simulation and comparison analysis demonstrates that the proposed controller setting is suitable for industrial applications due to its quick set-point tracking, effective load disturbance rejection, and smooth control signal. The study highlights the practical benefits of the 2-DOF Smith predictor in enhancing industrial control system performance and suggests directions for future research, including experimental validation and robustness analysis under varying system parameters.

Automotive antennas are gaining importance due to their increasingly important role for autonomous driving with the development of the Internet of Vehicles. However, over-the-air testing on automotive antennas is difficult owing to its large volume and complex body structure. Plane wave generators can approximate uniform plane waves in the near field of the device under test, which can greatly reduce the measurement distance and thereby decreasing the cost compared with direct far-field solutions. This letter designs three plane wave generators for three quiet zone sizes selected according to the vehicle structure at 5.9 GHz, achieving quiet zone sizes of 1.45 m $��\times �0.23�$ m, 2.9m $��\times �0.56�$ m, and 3.9 m $��\times �1.27�$ m, respectively. Within the three quiet zones, amplitude deviations of 0.82, 0.34, and 0.34 dB and phase deviations of $��9�.�4^{\circ }�$, $��6�.�{72}^{\circ }�$ and $��1�.�{42}^{\circ }�$ are realized, respectively, according to the numerical simulations. Uncertainty analysis is further implemented to investigate the robustness of the designed PWGs proposed in this letter.

Reconstruction-based methods are commonly used in industrial visual anomaly detection. They rely on a well-reconstructed normal mode of the model. However, it is difficult to manage the boundary of generalization. The strength of the model's generalization capability can directly affect the fidelity of the reconstruction, resulting in the occurrence of false positives. To address the above challenges, a novel dual branch reconstruction anomaly detection approach is proposed to control the model generalization capability at two dimensions. It reconstructs abnormal images into normal ones by resolution recovery and denoising branches. Detection results are generated from their comparison. In addition, an innovative channel adjustment module is introduced to improve information exchange between branches. It uses multiple dilated convolutions for interactions over different scales. Simulation experiments demonstrate that the method outperforms most inspection methods on the MVTec AD and MVTec 3D-AD datasets. It also shows good results on the self-generated automotive paint scratches dataset of this study.

Monitoring nocturnal animals in the field is an important task in ecological research and wildlife conservation, but the complexity of nocturnal images and low light conditions make it difficult to cope with traditional image processing methods. To address this problem, researchers have introduced infrared cameras to improve the accuracy of nocturnal animal behaviour observations. Object detection in nighttime images captured by infrared cameras faces several challenges, including low image quality, animal scale variations, occlusion, and pose changes. This study proposes the YOLOv8-night model, which effectively overcomes these challenges by introducing a channel attention mechanism in YOLOv8. The model is more focused on capturing animal-related features by dynamically adjusting the channel weights, which improves the saliency of key features and increases the accuracy rate in complex backgrounds. The main contribution of this study is the introduction of the channel attention mechanism into the YOLOv8 framework to create a YOLOv8-night model suitable for object detection in nighttime images. When tested on nighttime images, the model performs well with a significantly higher mAP (0.854) than YOLOv8 (0.831), and YOLOv8-night scores 0.856 on mAP_l, which is obviously better than YOLOv8 (0.833) in terms of processing large objects. The study provides a reliable technical tool for ecological research, wildlife conservation and environmental monitoring, and offers new methods and insights for the study of nocturnal animal behaviour.

In the domain of modern manufacturing, computer numerical control (CNC) milling machines have emerged as instrumental assets. However, the data they generate is of vast amount, but usually contains redundancies and displays consistent patterns, making it inefficient for deep learning training. This paper proposes a novel sampling algorithm tailored for CNC milling machine data, emphasizing both diversity and efficiency. The proposed method leverages the entropy concept from the information-theoretic perspective to evaluate and enhance data diversity, aiming to achieve efficient learning with high accuracy. This in turn enables to not only facilitates a deeper understanding of CNC data characteristics but also contributes significantly to the optimization of deep learning training processes in the context of CNC milling data.