Remote sensing image change detection (RSCD) aims to identify differences between remote sensing images of the same location at different times. However, due to the significant variations in the size and appearance of objects in real-world scenes, existing RSCD algorithms often lack strong capabilities in extracting multiscale features, thereby failing to fully capture the characteristics of changes. To address this issue, a multiscale remote sensing change detection network (MSNet) and a multiscale RSCD dataset (MSRS-CD) are proposed. A multiscale convolution module (MSCM) is investigated, and combined with MSCM, an encoder capable of capturing features of different sizes is designed to efficiently extract multiscale semantic change features. A global multiscale feature fusion module is designed to achieve global multiscale feature fusion and obtain multiscale high-level semantic change features. As existing RSCD datasets lack rich scale information and often focus on change targets of specific sizes, a new dataset, MSRS-CD, is constructed. This dataset consists of 842 pairs of images with a resolution of 1024×1024 pixels, featuring uniformly distributed change detection target sizes. Comparative experiments are conducted with ten other state-of-the-art algorithms on the MSRS-CD dataset and another public dataset, LEVIR-CD. Experimental results demonstrate that MSNet achieves the best performance on both datasets, with an F1 score of 75.74% on the MSRS-CD dataset and 91.41% on the LEVIR-CD dataset.

In the processing of high-resolution remote sensing images, multi-scale feature fusion techniques are commonly employed to construct change detection models, aiming to capture the details and characteristics of target objects at different scales. However, current change detection methods often neglect the subtle features of small-scale targets and edge information during feature fusion. To address this issue, this paper proposes a Deep Feature Fusion Network (DFFNet) for full-scale change detection in remote sensing images. DFFNet enhances the accuracy of boundary information in change regions and enables the extraction of full-scale change features. By integrating phased difference extraction techniques with an enhanced attention mechanism, a Dual Temporal Difference Enhancement Module (DT-DEM) is designed to comprehensively extract change detection information across scales. Additionally, combining an inverted pyramid technique with an attention mechanism, a Screening Function Inverted Pyramid Network (S-FIPN) is proposed, which efficiently extracts and fuses full-scale features while significantly improving the extraction of subtle changes and weak edge features.Comparison experiments with ten state-of-the-art algorithms on four publicly available datasets, namely, LEVIR-CD, WHU-CD, NJDS and MSRS-CD, show that DFFNet achieves the best F1 values on all datasets, which are 92.50%, 90.55%, 81.04% and 77.44%, respectively.These results underscore the superiority and effectiveness of DFFNet in full-scale feature extraction and edge information retrieval.

YOLOv5 is currently one of the mainstream algorithms for object detection. In this paper, we propose the FRL-YOLO model specifically for river floating object detection. The algorithm integrates the Fasternet block into the C3 module, conducting convolutions only on a subset of input channels to reduce computational load. Simultaneously, it effectively captures spatial features, incorporates reparameterization techniques into the feature extraction network, and introduces the RepConv design to enhance model training efficiency. To further optimize network performance, the ACON-C activation function is employed. Finally, by employing a structured non-destructive pruning approach, redundant channels in the model are trimmed, significantly reducing the model’s volume. Experimental results indicate that the algorithm achieves an average precision value (mAP) of 79.3%, a 0.4% improvement compared to yolov5s. The detection speed on the NVIDIA GeForce RTX 4070 graphics card reaches 623.5 fps/s, a 22.8% increase over yolov5s. The improved model is compressed to a volume of 2 MB, representing only 14.7% of yolov5s.

The internal electric field coupling noise of a quartz flexible accelerometer (QFA) restricts the improvement of the measurement accuracy of the accelerometer. In this paper, the internal electric field coupling mechanism of a QFA is studied, an electric field coupling detection noise model of the accelerometer is established, the distributed capacitance among the components of the QFA is simulated, the structure of the detection noise transfer system of different carrier modulation differential capacitance detection circuits is analyzed, and the influence of each transfer chain on the detection noise is discussed. The simulation results of electric field coupling detection noise show that the average value of detection noise can reach 41.7 μg, which is close to the effective resolution of the QFA, 50 μg. This confirms that electric field coupling detection noise is a non-negligible factor affecting the measurement accuracy of the accelerometer. A method of adding a high-pass filter to the front of the phase-shifting circuit is presented to suppress the noise of electric field coupling detection. This method attenuates the average value of the detected noise by about 78 dB, and reduces the average value of the detected noise to less than 0.1 μg, which provides a new approach and direction for effectively breaking through the performance of the QFA.

A three-degree-of-freedom (3-DOF) piezoelectric ultrasonic transducer is a critical component in elliptical and longitudinal ultrasonic vibration-assisted cutting processes, with its geometric structure directly influencing its performance. This paper proposes a structural optimization method based on a convolutional neural network (CNN) and non-dominated sorting genetic algorithm II (NSGA2). This method establishes a transducer lumped model to obtain the electromechanical coupling coefficients (X-$k_e$ and Z-$k_e$) and thermal power (X-P) indicators, evaluating the bending and longitudinal vibration performance of the transducer. By creating a finite element model of the transducer with mechanical losses, a dataset of different transducer performance parameters, including the tail mass, piezoelectric stack, and dimensions of the horn, is obtained. Training a CNN model with this dataset yields objective functions for the relationship between different transducer geometric structures and performance parameters. The NSGA2 algorithm solves the X-$k_e$ and Z-$k_e$ objective functions, obtaining the Pareto set of the transducer geometric dimensions and determining the optimal transducer geometry in conjunction with X-P. This method achieves simultaneous improvements in X-$k_e$ and Z-$k_e$ of the transducer by 22.33% and 25.89% post-optimization and reduces X-P to 18.97 W. Furthermore, the finite element simulation experiments of the transducer validate the effectiveness of this method.