A Miniaturized and Ultra-Low-Power Wireless Multi-Parameter Monitoring System with Self-Powered Ability for Aircraft Smart Skin.

The aircraft smart skin (ASS) with structural health monitoring capabilities is a promising technology. It enables the real-time acquisition of the aircraft's structural health status and service environment, thereby improving the performance of the aircraft and ensuring the safety of its operation, which in turn reduces maintenance costs. In this paper, a miniaturized and ultra-low-power wireless multi-parameter monitoring system (WMPMS) for ASS is developed, which is capable of monitoring multiple parameters of an aircraft, including random impact events, vibration, temperature, humidity, and air pressure.

A 35 nV/√Hz Analog Front-End Circuit with Adjustable Bandwidth and Gain in UMC 40 nm CMOS for Biopotential Signal Acquisition.

This paper presents a 35 nV/√Hz analog front-end (AFE) circuitdesigned in the UMC 40 nm CMOS technology for the acquisition of biopotential signal. The proposed AFE consists of a capacitive-coupled instrumentation amplifier (CCIA) and a combination of a programmable gain amplifier (PGA) and a low-pass filter (LPF). The CCIA includes a DC servo loop (DSL) to eliminate electrode DC offset (EDO) and a ripple rejection loop (RRL) with self-zeroing technology to suppress high-frequency ripples caused by the chopper.

Method for Correcting Error Due to Self-Heating of Resistance Temperature Detectors Suitable for Metrology in Industry 4.0.

This study contributes to improving the accuracy of temperature measurements with a platinum resistance temperature detector (RTD) by proposing techniques to mitigate the error due to self-heating by the operating current. An assessment of the measurement errors of the platinum RTD was carried out to study ways to improve their accuracy. High accuracy can be achieved by individual calibration using a voltage divider circuit to measure resistance, the substitution method, and the transitional measure.

Two Novel Semi-Quantum Secure Direct Communication Protocols in IoT.

As Internet of Things (IoT) technology continues to advance, there is a growing awareness of IoT security within the industry. Quantum communication technology can potentially significantly improve the communication security of IoT devices. Based on semi-quantum cryptography and utilizing single photons, this paper introduces two semi-quantum secure direct communication (SQSDC) protocols for use in smart door locks.

Real-Time Orbit Determination of Micro-Nano Satellite Using Robust Adaptive Filtering.

Low-performing GPS receivers, often used in challenging scenarios such as attitude maneuver and attitude rotation, are frequently encountered for micro-nano satellites. To address these challenges, this paper proposes a modified robust adaptive hierarchical filtering algorithm (named IARKF). This algorithm leverages robust adaptive filtering to dynamically adjust the distribution of innovation vectors and employs a fading memory weighted method to estimate measurement noise in real time, thereby enhancing the filter's adaptability to dynamic environments.

Novel Cost-Effective and Portable Three-Dimensional Force Measurement System for Biomechanical Analysis: A Reliability and Validity Study.

The application of dynamic data in biomechanics is crucial; traditional laboratory-level force measurement systems are precise, but they are costly and limited to fixed environments. To address these limitations, empirical evidence supports the widespread adoption of portable force-measuring platforms, with recommendations for their ongoing development and enhancement. Taiyuan University of Technology has collaborated with KunWei Sports Technology Co.

Control Method for SynRMs Uses Simple Inductance Identification and Voltage Injection for Current and Angle Control.

The sensorless vector control method of synchronous reluctance motors (SynRMs), based on extended back electromotive force (EMF) or flux observation, has been widely applied in the medium- or high-speed range. However, in the low-speed and low-current range, the extended back-EMF and flux are nearly zero. The use of the current frequency () control method can enable the motor to pass through the low-speed region, thereby ensuring that the back-EMF and flux reach a large value.

A Lightweight ECC-Based Authentication and Key Agreement Protocol for IoT with Dynamic Authentication Credentials.

Due to the openness of communication channels and the sensitivity of the data being collected and transmitted, securing data access and communication in IoT systems requires robust ECC-based authentication and key agreement (AKA) protocols. However, designing an AKA protocol for IoT presents significant challenges, as most IoT sensors are deployed in resource-constrained, unattended environments with limited computational power, connectivity, and storage. To achieve anonymous authentication, existing solutions typically rely on shared temporary public keys to mask device IDs or validate sender certificates, which increases the computational overhead.

Age of Information Analysis for Multi-Priority Queue and Non-Orthoganal Multiple Access (NOMA)-Enabled Cellular Vehicle-to-Everything in Internet of Vehicles.

With the development of Internet of Vehicles (IoV) technology, the need for real-time data processing and communication in vehicles is increasing. Traditional request-based methods face challenges in terms of latency and bandwidth limitations. Mode 4 in cellular vehicle-to-everything (C-V2X), also known as autonomous resource selection, aims to address latency and overhead issues by dynamically selecting communication resources based on real-time conditions.

Cascaded Feature Fusion Grasping Network for Real-Time Robotic Systems.

Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels.

Humanity Test-EEG Data Mediated Artificial Intelligence Multi-Person Interactive System.

Artificial intelligence (AI) systems are widely applied in various industries and everyday life, particularly in fields such as virtual assistants, healthcare, and education. However, this paper highlights that existing research has often overlooked the philosophical and media aspects. To address this, we developed an interactive system called "Human Nature Test".

An Improved Global and Local Fusion Path-Planning Algorithm for Mobile Robots.

Path planning is a core technology for mobile robots. However, existing state-of-the-art methods suffer from issues such as excessive path redundancy, too many turning points, and poor environmental adaptability. To address these challenges, this paper proposes a novel global and local fusion path-planning algorithm.

Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection.

Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection.

Characteristic Analysis and Error Compensation Method of Space Vector Pulse Width Modulation-Based Driver for Permanent Magnet Synchronous Motors.

Permanent magnet synchronous motors (PMSMs) are widely used in a variety of fields such as aviation, aerospace, marine, and industry due to their high angular position accuracy, energy conversion efficiency, and fast response. However, driving errors caused by the non-ideal characteristics of the driver negatively affect motor control accuracy. Compensating for the errors arising from the non-ideal characteristics of the driver demonstrates substantial practical value in enhancing control accuracy, improving dynamic performance, minimizing vibration and noise, optimizing energy efficiency, and bolstering system robustness.

Eye-Tracking Characteristics: Unveiling Trust Calibration States in Automated Supervisory Control Tasks.

Trust is a crucial human factor in automated supervisory control tasks. To attain appropriate reliance, the operator's trust should be calibrated to reflect the system's capabilities. This study utilized eye-tracking technology to explore novel approaches, given the intrusive, subjective, and sporadic characteristics of existing trust measurement methods.

An Experimental Study of the Flexural Bearing Capacity of Reinforced Concrete Beams Damaged by Explosions Using Piezoelectric Smart Aggregates.

Two identically sized RC beams were fabricated to investigate the effects of explosive loads on the flexural behaviour of Reinforced Concrete (RC) beams. One of the beams was subjected to an explosive load to induce post-explosion damage, and subsequently, both beams underwent flexural capacity testing. Integrating piezoelectric smart aggregates (SAas) within the beams facilitated continuous observation of the damage conditions, allowing for the assessment of internal concrete deterioration from explosive impacts to bending failures.

Research Progress on the Measurement Methods and Clinical Significance of Capillary Refill Time.

The monitoring of peripheral circulation, as indicated by the capillary refill time, is a sensitive and accurate method of assessing the microcirculatory status of the body. It is a widely used tool for the evaluation of critically ill patients, the guidance of therapeutic interventions, and the assessment of prognosis. In recent years, there has been a growing emphasis on microcirculation monitoring which has led to an increased focus on capillary refill time.

MFF-Net: A Lightweight Multi-Frequency Network for Measuring Heart Rhythm from Facial Videos.

Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios.

Research on Downhole MTATBOT Positioning and Autonomous Driving Strategies Based on Odometer-Assisted Inertial Measurement.

In response to the current situation of backward automation levels, heavy labor intensities, and high accident rates in the underground coal mine auxiliary transportation system, the mining trackless auxiliary transportation robot (MTATBOT) is presented in this paper. The MTATBOT is specially designed for long-range, space-constrained, and explosion-proof underground coal mine environments. With an onboard perception and autopilot system, the MTATBOT can perform automated and unmanned subterranean material transportation.

Infrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network.

In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features.

LiDAR-Based Negative Obstacle Detection for Unmanned Ground Vehicles in Orchards.

In orchard environments, negative obstacles such as ditches and potholes pose significant safety risks to robots working within them. This paper proposes a negative obstacle detection method based on LiDAR tilt mounting. With the LiDAR tilted at 40°, the blind spot is reduced from 3 m to 0.

Modeling of a Novel T-Core Sensor with an Air Gap for Applications in Eddy Current Nondestructive Evaluation.

Multi-layer conductive structures, especially those with features like bolt holes, are vulnerable to hidden corrosion and cracking, posing a serious threat to equipment integrity. Early defect detection is vital for implementing effective maintenance strategies. However, the subtle signals produced by these defects necessitate highly sensitive non-destructive testing (NDT) techniques.

Advanced Trajectory Planning and Control for Autonomous Vehicles with Quintic Polynomials.

This paper focuses on the design of vehicle trajectories and their control systems. A method based on quintic polynomials is utilized to develop trajectories for intelligent vehicles, ensuring the smooth continuity of the trajectory and related state curves under varying conditions. The construction of lateral and longitudinal controllers is discussed, which includes a tracking error model derived from the two-degree-of-freedom dynamic model of a two-wheeled vehicle and the application of the Frenet coordinate system transformation.

A Review of Asynchronous Byzantine Consensus Protocols.

Blockchain technology can be used in the IoT to ensure the data privacy collected by sensors. In blockchain systems, consensus mechanisms are a key technology for maintaining data consistency and correctness. Among the various consensus protocols, asynchronous Byzantine consensus protocols offer strong robustness as they do not rely on any network timing assumptions during design.

Deep Learning Calculation and Application of Axle Loads in Highway Sensor Data.

Axle load data and traffic survey data are both important outputs of highway sensors. This study targets highways and ordinary national and provincial highways, seeking to calculate axle load spectrum and equivalent axle times across the network. There is often an association in the spatial extent of traffic survey data and axle load detection data in highway networks.