In view of the limited number of extracted sound features, the lack of in-depth analysis of applicable sound features, and the lack of in-depth study of the selection basis and optimization process of classification models in the existing broiler sound classification or recognition research, the author proposes a recognition method for broiler sound signals based on multi-domain sound features and classification models. The implementation process is divided into the training stage and the testing stage. In the training stage, the experimental area is built, and multiple segments of broiler sound signals are collected and filtered. Through sub-frame processing and endpoint detection, the combinations of start frames and end frames of multiple sound types in broiler sound signals are obtained. A total of sixty sound features from four aspects of time domain, frequency domain, Mel-Frequency Cepstral Coefficients (MFCC), and sparse representation are extracted from each frame signal to form multiple feature vectors. These feature vectors are labeled manually to build the data set. The min-max standardization method is used to process the data set, and the random forest is used to calculate the importance of sound features. Then, thirty sound features that contribute more to the classification effect of the classification model are retained. On this basis, the classification models based on seven classification algorithms are trained, the best-performing classification model based on k-Nearest Neighbor (kNN) is obtained, and its inherent parameters are optimized. Then, the optimal classification model is obtained. The test results show that the average classification accuracy achieved by the decision-tree-based classifier (abbreviated as DT classifier) on the data set before and after min–max standardization processing is improved by 0.6%, the average classification accuracy achieved by the DT classifier on the data set before and after feature selection is improved by 3.1%, the average classification accuracy achieved by the kNN-based classification model before and after parameter optimization is improved by 1.2%, and the highest classification accuracy is 94.16%. In the testing stage, for a segment of the broiler sound signal collected in the broiler captivity area, the combinations of the start frames and end frames of multiple sound types in the broiler sound signal are obtained through signal filtering, sub-frame processing, endpoint detection, and other steps. Thirty sound features are extracted from each frame signal to form the data set to be predicted. The optimal classification model is used to predict the labels of each piece of data in the data set to be predicted. By performing majority voting processing on the predicted labels of the data combination corresponding to each sound type, the common labels are obtained; that is, the predicted types are obtained. On this basis, the definition of recognition accuracy for broiler sound signals is proposed. The test results show that the classification accuracy achieved by the optimal classification model on the data set to be predicted is 93.57%, and the recognition accuracy achieved on the multiple segments of the broiler sound signals is 99.12%.

Arteries in the upper limb play important roles in the circulation system of the human body. In particular, the radial artery has received considerable attention in traditional Chinese medicine for thousands of years. Here, a 3D model for the arm arteries has been created uncomplicated, in a Chinese adult’s left hand, from the magnetic resonance imaging data, using professional modeling software to restore the basic structure of the arm artery in human body, before being imported to Ansys software for simulation. Blood model has been only simulated, and using the blood density of constant parameter and viscosity using the Carreau fluid model, and using viscous-laminar model of Fluent to obtain the velocity profile, static pressure and shear stress in the brachial, interosseous, ulnar, radial and palmar arch arteries. In particular, the brachial and bifurcations have the high pressure and velocity profiles. The simulation results obtained here are also validated by those published in the literature and proved the ulnar artery prevails over the radial artery as a blood supplier to the vessels in the wrist and hand.

Single-channel electroencephalography (EEG) signals are more susceptible to electro-oculography (EOG) interference, which could be attributed to the acquisition device of the single-channel. To realize EOG artifacts separation in this paper, the blind deconvolution (BD) model was investigated based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The CEEMDAN method was firstly used to decompose the EEG data contained artifacts into several intrinsic mode functions (IMF). After that, the modal component used as the observed signal was provided to the BD model, which was formed by the source signal of the EEG signal and the EOG artifacts. Consequently, we successfully realized the separation of EEG signal and EOG artifacts by the constructing cost function iteratively, and our results demonstrated that the separation effect of this method on EOG artifacts is better than previous studies. Further, the correlation coefficient of real-life data after CEEMDAN-BD algorithm processing reaches 0.81. Moreover, the modal aliasing problem was solved with most of the original EEG signal components retained. In a word, this novel method provides theory and practice references for the processing of EEG signals and other physiological signals.

To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naive bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.

In order to solve the problems of low efficiency and poor accuracy in measuring the geometrical parameters of braided pipe mesh by traditional methods, an improved method for measuring the geometrical parameters of braided pipe is proposed. The method acquires the braided pipe grid image by a CMOS camera and reduces the influence of color and noise on the image quality by using preprocessing means such as grayscaling and bilateral filtering. Subsequently, the region of interest of the woven tube grid structure is extracted from the background using an iterative method, and the average filament diameter is calculated by Canny edge detection and a modified Hough transform. Next, the skeleton wire segment contours are obtained using the skeleton operator, and the contours are further optimized by the RDP algorithm to retain only the straight line segments. The co-linear straight line segments are connected to form a complete mesh line by a contour merging operation. Finally, the mesh size is indirectly obtained using the improved least-squares method based on feature classification. Through experimental verification, the method has a practical value as it improves the average wire diameter accuracy by 3% and the mesh size accuracy by 1% compared with the traditional machine vision measurement method. Received: 18 February 2025 | Revised: 9 April 2025 | Accepted: 21 April 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data availability statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Hufeng Zou: Conceptualization, Methodology, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Quanyu Wu: Validation, Formal analysis, Resources, Writing – review & editing, Project administration. Zehan Ye: Investigation, Resources, Writing – original draft, Writing – review & editing. Jiaqi Fan: Software. Lingjiao Pan: Writing – review & editing. Xiaojie Liu: Supervision.

This paper proposes a tightly-coupled lidar-inertial odometry simultaneous localization and mapping (SLAM) framework, aiming to achieve high-precision and high-robustness position estimation and high-quality map construction in robot operation scenarios. In the feature extraction process, we innovatively adopt a face-based sampling strategy combined with uniform point cloud filtering to effectively optimize the spatial distribution characteristics of the point cloud data. When using the lidar odometry to correct the inertial measurement unit (IMU) data, we designed a lidar odometry confidence model based on the degree of degradation assessment, realized the optimization of the sensor fusion parameters through the dynamic noise covariance adjustment mechanism, and corrected the system state in real time by re-propagating the inertial data. In the optimization stage, we modify the residual computation model to appropriately increase the corner point matching weight and suppress the far-point noise. The proposed method is extensively evaluated and tested on the M2DGR dataset covering indoor and outdoor multi-scenes. Our scheme demonstrates advantages in terms of robustness and long-term error accumulation.