State Estimation for Quadruped Robots on Non-Stationary Terrain via Invariant Extended Kalman Filter and Disturbance Observer.

Quadruped robots possess significant mobility in complex and uneven terrains due to their outstanding stability and flexibility, making them highly suitable in rescue missions, environmental monitoring, and smart agriculture. With the increasing use of quadruped robots in more demanding scenarios, ensuring accurate and stable state estimation in complex environments has become particularly important. Existing state estimation algorithms relying on multi-sensor fusion, such as those using IMU, LiDAR, and visual data, often face challenges on non-stationary terrains due to issues like foot-end slippage or unstable contact, leading to significant state drift.

A Comprehensive Survey on Visual Perception Methods for Intelligent Inspection of High Dam Hubs.

There are many high dam hubs in the world, and the regular inspection of high dams is a critical task for ensuring their safe operation. Traditional manual inspection methods pose challenges related to the complexity of the on-site environment, the heavy inspection workload, and the difficulty in manually observing inspection points, which often result in low efficiency and errors related to the influence of subjective factors. Therefore, the introduction of intelligent inspection technology in this context is urgently necessary.

Underwater image enhancement using Divide-and-Conquer network.

Underwater image enhancement has become the requirement for more people to have a better visual experience or to extract information. However, underwater images often suffer from the mixture of color distortion and blurred quality degradation due to the external environment (light attenuation, background noise and the type of water). To solve the above problem, we design a Divide-and-Conquer network (DC-net) for enhancing underwater image, which mainly consists of a texture network, a color network and a refinement network.

The effect of TEMPOL pretreatment on postoperative cognitive function, inflammatory response, and oxidative stress in aged rats under sevoflurane anesthesia.

Introduction: The heterocyclic compound 4-hydroxy-(2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPOL) has a protective effect on neurological function in brain tissues damaged by ischemia and hypoxia. This study explored the effects of TEMPOL pretreatment on postoperative cognitive function in aged rats under sevoflurane anesthesia, focusing on inflammatory response and oxidative stress.

Methods: Sixty male rats were divided into normal control (C), sevoflurane anesthesia (S), TEMPOL pretreatment (T), and sevoflurane anesthesia + TEMPOL pretreatment (ST) groups (15 per group).

Remaining Useful Life Prediction of Rolling Bearings Based on Multi-Scale Attention Residual Network.

The remaining useful life (RUL) prediction of rolling bearings based on vibration signals has attracted widespread attention. It is not satisfactory to adopt information theory (such as information entropy) to realize RUL prediction for complex vibration signals. Recent research has used more deep learning methods based on the automatic extraction of feature information to replace traditional methods (such as information theory or signal processing) to obtain higher prediction accuracy.

Attention-Based Multiscale Feature Pyramid Network for Corn Pest Detection under Wild Environment.

A serious outbreak of agricultural pests results in a great loss of corn production. Therefore, accurate and robust corn pest detection is important during the early warning, which can achieve the prevention of the damage caused by corn pests. To obtain an accurate detection of corn pests, a new method based on a convolutional neural network is introduced in this paper.

Research on Data Fusion Scheme for Wireless Sensor Networks with Combined Improved LEACH and Compressed Sensing.

There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the network.

Expression and clinical significance of Caveolin-1 in prostate Cancer after transurethral surgery.

Background: Prostate cancer is a common malignancy of the male genitourinary system that occurs worldwide. The current research aims to investigate caveolin-1 expression in prostate cancer tissue and its relationship with pathological grade, clinical pathologic staging, and preoperative prostate-specific antigen (PSA) levels.

Methods: From January 2012 to December 2014, samples from 47 patients with prostate cancer who had received transurethral prostatic resection (TURP) and 20 patients with benign prostatic hyperplasia were collected at the First Affiliated Hospital of Guangxi Medical University.