Gait phase analysis is widely used in disease diagnosis, rehabilitation training, and other fields by studying the characteristics of human gait. It systematically evaluates human body’s skeletal muscles and nerves with combined disciplines. Microelectromechanical system (MEMS) inertial sensors are extensively used in attitude detection because of its high-precision, portability, and good real-time performance in time-domain analysis. In this article, we presented a multi-degree-of-freedom MEMS gait detection method, which resolved the problems of single sensor and limited gait phase. We designed a sensor-based gait signal acquisition system, in which gait data acquisition program and feature analysis algorithm were compiled to verify the feasibility of the proposed method. We performed coordinate transformation and corrected position information to eliminate the gait phase detection error caused by random noise interference. Acceleration and angular velocity information were collected from 20 experimenters. We applied an adaptive threshold gait phase detection algorithm to classify the gait information collected by single sensor. To improve the results of gait phase classification, we used multisensor redundant measurement to analyze characteristics of five gait phases. The acceleration and angular velocity information collected by the three sensors placed at instep, ankle, and thigh were input into support vector machine (SVM). The classification results of the five gait phases are approximately 90%. Lastly, we built a human body structure model to simulate human motion in real time, realizing the real-time gait phase detection, which proves effectiveness of the proposed algorithm.

With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency.

Accurate estimation of soil total nitrogen (TN) and soil organic matter (SOM) is important for sustainable soil fertility assessment and precision nutrient management. Visible–near-infrared hyperspectral sensing provides a rapid and non-destructive solution, but its inversion accuracy is strongly affected by spectral preprocessing, especially under small-sample conditions. To reduce dependence on a manually selected preprocessing operator, this study proposes a homologous preprocessing representation fusion framework based on greedy concatenation (HPRF–GC). The framework constructs multiple homologous spectral views from the same raw spectrum, selects informative views through cross-validation-guided greedy forward selection, and concatenates the selected views before random forest or support vector regression. A self-built in situ hyperspectral dataset was collected from two representative black calcareous Mollisol farms in Heilongjiang Province, China, including 200 composite samples measured with a GaiaField Pro V10 imager at 5 m height under midday illumination using white reference calibration. On this dataset, HPRF–GC reduced RMSE by 3.61% for TN–RF, 9.94% for TN–SVR, 0.87% for SOM–RF, and 7.15% for SOM–SVR compared with the strongest single-preprocessing baseline, while introducing only a modest training-time overhead. On the public LUCAS 2015 dataset, HPRF–GC achieved competitive TN prediction performance, with an $R^2$ of 0.890 and an RMSE of 1.191 under RF. These results indicate that HPRF–GC provides a lightweight, interpretable and reproducible strategy for reducing preprocessing selection sensitivity in small-sample soil hyperspectral inversion.

This paper studies the functional connection of brain regions by using network analysis method. Through the analysis of network attributes, the influence of different emotions on brain functional network connection and the construction method of brain functional network are learned. In this paper, we used Electroencephalogram (EEG) signals to construct the binary brain networks based on correlation and phase synchrony. Firstly, the global network attributes under different emotions were studied, and the results obtained by different network construction methods were compared. We find that the brain functional networks with different definitions share common characteristics in the global attribute change. Secondly, the node attributes were analyzed. The results show that the node attributes of brain functional network, such as degree, betweenness centrality and local efficiency, can reflect the change of emotion. This paper provides clues for the study of emotional brain network analysis.

Soil organic matter (SOM) is a fundamental indicator of soil health and a major component of the global carbon cycle; its accurate quantification is essential for sustainable agriculture. Conventional chemical assays yield only point-based soil measurements and miss the spatial distribution of soil elements; airborne hyperspectral remote sensing has emerged as a promising approach for the quantitative measurement and characterization of SOM. Inversion models translate hyperspectral data into quantitative SOM estimates. However, existing models rely solely on a single preprocessing pathway, limiting their ability to fully exploit available spectral information. We address these limitations by developing a marginal contribution-driven spectral fusion network (MC-SFNet) that conducts feature-level fusion of heterogeneous preprocessing outputs within a physics-guided deep architecture. Moreover, the combination of data-driven fusion and the Kubelka–Munk (KM) model yields more physically interpretable spectral features, advancing beyond prior purely data-driven methods. We validated MC-SFNet on a self-constructed remote sensing, high-throughput hyperspectral dataset comprising 200 black soil samples from Northeastern China (400–1000 nm, 256 bands). Experimental results show that our network reduces the RMSE by 10.7% relative to the prevailing generalized hyperspectral soil-inversion model. The proposed method provides a novel preprocessing pathway for forthcoming airborne high-throughput hyperspectral missions to extract soil-specific spectral information more effectively and further enhance large-scale SOM retrieval accuracy.