Research Progress of MEMS Gas Sensors: A Comprehensive Review of Sensing Materials.

The MEMS gas sensor is one of the most promising gas sensors nowadays due to its advantage of small size, low power consumption, and easy integration. It has been widely applied in energy components, portable devices, smart living, etc. The performance of the gas sensor is largely determined by the sensing materials, as well as the fabrication methods.

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).

A Spatial Crowdsourcing Engine for Harmonizing Volunteers' Needs and Tasks' Completion Goals.

This work addresses the task allocation problem in spatial crowdsensing with altruistic participation, tackling challenges like declining engagement and user fatigue from task overload. Unlike typical models relying on financial incentives, this context requires alternative strategies to sustain participation. This paper presents a new solution, the Volunteer Task Allocation Engine (VTAE), to address these challenges.

CabbageNet: Deep Learning for High-Precision Cabbage Segmentation in Complex Settings for Autonomous Harvesting Robotics.

Reducing damage and missed harvest rates is essential for improving efficiency in unmanned cabbage harvesting. Accurate real-time segmentation of cabbage heads can significantly alleviate these issues and enhance overall harvesting performance. However, the complexity of the growing environment and the morphological variability of field-grown cabbage present major challenges to achieving precise segmentation.

Flexible Piezoresistive Film Pressure Sensor Based on Double-Sided Microstructure Sensing Layer.

Flexible thin-film pressure sensors have garnered significant attention due to their applications in industrial inspection and human-computer interactions. However, due to their ultra-thin structure, these sensors often exhibit lower performance, including a narrow pressure response range and low sensitivity, which constrains their further application. The most commonly used microstructure fabrication methods are challenging to apply to ultra-thin functional layers and may compromise the structural stability of the sensors.

Research on Tool Wear Monitoring Technology Based on Variational Mode Decomposition and Back Propagation Neural Network Model.

Accurately predicting tool wear during the machining process not only saves machining time and improves efficiency but also ensures the production of good-quality parts and automation. This paper proposes a combined variational mode decomposition (VMD) and back propagation (BP) neural network model (VMD-BP), which maps spindle power to tool wear. The model is trained using both historical and real-time data.

Multi-Granularity Temporal Embedding Transformer Network for Traffic Flow Forecasting.

Traffic flow forecasting is integral to transportation to avoid traffic accidents and congestion. Due to the heterogeneous and nonlinear nature of the data, traffic flow prediction is facing challenges. Existing models only utilize plain historical data for prediction.

Style Transfer of Chinese Wuhu Iron Paintings Using Hierarchical Visual Transformer.

Within the domain of traditional art, Chinese Wuhu Iron Painting distinguishes itself through its distinctive craftsmanship, aesthetic expressiveness, and choice of materials, presenting a formidable challenge in the arena of stylistic transformation. This paper introduces an innovative Hierarchical Visual Transformer (HVT) framework aimed at achieving effectiveness and precision in the style transfer of Wuhu Iron Paintings. The study begins with an in-depth analysis of the artistic style of Wuhu Iron Paintings, extracting key stylistic elements that meet technical requirements for style conversion.

Research on Parameter Compensation Method and Control Strategy of Mobile Robot Dynamics Model Based on Digital Twin.

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces.

Gas Sensor for Efficient Acetone Detection and Application Based on Au-Modified ZnO Porous Nanofoam.

Toxic acetone gas emissions and leakage are a potential threat to the environment and human health. Gas sensors founded on metal oxide semiconductors (MOS) have become an effective strategy for toxic gas detection with their mature process. In the present work, an efficient acetone gas sensor based on Au-modified ZnO porous nanofoam (Au/ZnO) is synthesized by polyvinylpyrrolidone-blowing followed by a calcination method.

Lane Detection Based on ECBAM_ASPP Model.

With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module.

Enhance the Concrete Crack Classification Based on a Novel Multi-Stage YOLOV10-ViT Framework.

Early identification of concrete cracks and multi-class detection can help to avoid future deformation or collapse in concrete structures. Available traditional detection and methodologies require enormous effort and time. To overcome such difficulties, current vision-based deep learning models can effectively detect and classify various concrete cracks.

Corrosion Monitoring in Automotive Lap Joints Based on Imaging Methods of Lamb Waves.

Corrosion damage presents significant challenges to the safety and reliability of connected vehicles. However, traditional non-destructive methods often fall short when applied to complex automotive structures, such as bolted lap joints. To address this limitation, this study introduces a novel corrosion monitoring approach using Lamb wave-based weighted fusion imaging methods.

Knowledge Graph-Based In-Context Learning for Advanced Fault Diagnosis in Sensor Networks.

This paper introduces a novel approach for enhancing fault diagnosis in industrial equipment systems through the application of sensor network-driven knowledge graph-based in-context learning (KG-ICL). By focusing on the critical role of sensor data in detecting and isolating faults, we construct a domain-specific knowledge graph (DSKG) that encapsulates expert knowledge relevant to industrial equipment. Utilizing a long-length entity similarity (LES) measure, we retrieve relevant information from the DSKG.

Adaptive Memory-Augmented Unfolding Network for Compressed Sensing.

Deep unfolding networks (DUNs) have attracted growing attention in compressed sensing (CS) due to their good interpretability and high performance. However, many DUNs often improve the reconstruction effect at the price of a large number of parameters and have the problem of feature information loss during iteration. This paper proposes a novel adaptive memory-augmented unfolding network for compressed sensing (AMAUN-CS).

Blade Vibration Difference-Based Circumferential Fourier Fitting Algorithm for Synchronous Vibration Parameter Identification of Rotation Blades.

Blades are the core components of rotating machinery, and the blade vibration status directly impacts the working efficiency and safe operation of the equipment. The blade tip timing (BTT) technique provides a solution for blade vibration monitoring and is currently a prominent topic in research on blade vibration issues. Nevertheless, the non-stationary factors present in actual engineering applications introduce inaccuracies in the BTT technique, resulting in blade vibration measurement errors.

Modelling, Analysis and Validation of Hydraulic Self-Adaptive Bearings for Elevated Floating Bridges.

Conventional floating bridge systems used during emergency repairs, such as during wartime or after natural disasters, typically rely on passive rubber bearings or semi-active control systems. These methods often limit traffic speed, stability, and safety under dynamic conditions, including varying vehicle loads and fluctuating water levels. To address these challenges, this study proposes a novel Hydraulic Self-Adaptive Bearing System (HABS).

Joint Fault Diagnosis of IGBT and Current Sensor in LLC Resonant Converter Module Based on Reduced Order Interval Sliding Mode Observer.

LLC resonant converters have emerged as essential components in DC charging station modules, thanks to their outstanding performance attributes such as high power density, efficiency, and compact size. The stability of these converters is crucial for vehicle endurance and passenger experience, making reliability a top priority. However, malfunctions in the switching transistor or current sensor can hinder the converter's ability to maintain a resonant state and stable output voltage, leading to a notable reduction in system efficiency and output capability.

Force Feedback Method for Statically Indeterminate Steel Structure Construction Based on Staged Temperature Measurement.

Structural design usually adopts uniform temperature action. However, during the actual construction of the structure, the temperature field acting on the structure is inhomogeneous. Therefore, the simulation of the construction of statically indeterminate steel structures considering only the uniform temperature field cannot truly reflect the temperature action after structural molding and the evolution of the stress performance of the temporary stress system of structural construction.

Reanalysis on Performance of Microwave Phase Detector for Multisignals.

Microwave phase detectors (MPDs) are key components of instantaneous frequency measurement (IFM) receivers and phase interferometer direction finding (PIF-DF) receivers. In conventional analyses, there is seldom a major quantitative discussion of MPD characterization when multiple signals arrive at the same time, which is often the case in complex and noisy electromagnetic environments. We have reanalyzed the characteristics of MPDs with respect to filter effects acting on more than two RF signals and differential amplifiers, which are not considered in conventional analyses.

Evaluating and Enhancing Face Anti-Spoofing Algorithms for Light Makeup: A General Detection Approach.

Makeup modifies facial textures and colors, impacting the precision of face anti-spoofing systems. Many individuals opt for light makeup in their daily lives, which generally does not hinder face identity recognition. However, current research in face anti-spoofing often neglects the influence of light makeup on facial feature recognition, notably the absence of publicly accessible datasets featuring light makeup faces.

Unmanned-Aerial-Vehicle-Assisted Secure Free Space Optical Transmission in Internet of Things: Intelligent Strategy for Optimal Fairness.

In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them.

Error Separation Method for Geometric Distribution Error Modeling of Precision Machining Surfaces Based on K-Space Spectrum.

The geometric error distributed on components' contact surfaces is a critical factor affecting assembly accuracy and precision instrument stability. Effective error separation methods can improve model accuracy, thereby aiding in performance prediction and process optimization. Here, an error separation method for geometric distribution error modeling for precision machining surfaces based on the K-space spectrum is proposed.

GeometryFormer: Semi-Convolutional Transformer Integrated with Geometric Perception for Depth Completion in Autonomous Driving Scenes.

Depth completion is widely employed in Simultaneous Localization and Mapping (SLAM) and Structure from Motion (SfM), which are of great significance to the development of autonomous driving. Recently, the methods based on the fusion of vision transformer (ViT) and convolution have brought the accuracy to a new level. However, there are still two shortcomings that need to be solved.

Design and Study of Pulsed Eddy Current Sensor for Detecting Surface Defects in Small-Diameter Bars.

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar.