Reproducible EEG-based brain–computer interface (BCI) experiments require traceability from the configured protocol through runtime execution to acquisition context and, where relevant, the physical stimulus delivered to the participant. This s...
In this article, we propose a vision-guided robotic arm system that integrates deep learning, digital twin, and cybersecurity to enhance the maintenance of quadruped robots. The proposed system comprises two core components. (1) A YOLOv11-based objec...
This study presents a model-based feedforward method for reducing velocity errors during the vertical travel of a magnetically coupled wall-climbing robot. Permanent magnets on opposite sides of the wall maintain magnetic coupling, while a spring-pre...
A low-temperature post-processing Al-migration module is used to achieve separate absorption, charge, and multiplication (SACM) functionality in vertical Ge-on-Si photodiodes without dedicated charge-layer formation by implantation or epitaxy. During...
Short-term outages of real-time state space representation (SSR) products degrade precise point positioning (PPP) accuracy. We propose a multi-GNSS PPP stochastic model refinement method based on historical statistics of additional signal-in-space ra...
Carbon nanotube (CNT) biosensors offer sensitive electrical and near-infrared optical readouts, yet performance in defined buffers does not necessarily translate to reliable measurements in complex biological media. Ionic screening, nonspecific adsor...
Fast-scan cyclic voltammetry (FSCV) is a widely used electrochemical technique for real-time monitoring of neurotransmitters; however, quantitative interpretation remains dependent on calibration curves generated in simplified in vitro environments t...
The Internet of Underwater Things (IoUT) supports marine applications such as environmental monitoring, offshore exploration, disaster prevention, and underwater infrastructure inspection. However, limited acoustic bandwidth, long propagation delays,...
Remote micro-deformation monitoring of large structures and geological hazards presents significant challenges for conventional phase-interferometric radar due to limitations in range, transmitter leakage, and multi-target identification. This study...
Part annotation of 3D objects is important for part-level analysis, but local geometric evidence may not determine a unique semantic label. We propose an ontology-based annotation refinement framework that represents multiple semantic interpretations...
Estimating joint pressure distribution is crucial for understanding how pathologies disrupt natural biomechanics and for optimizing rehabilitation outcomes. While numerous computational, cadaveric, and “in vivo” studies have investigated...
The widespread use of small unmanned aerial vehicles (UAVs) has increased the need for reliable short-range detection in complex environments. Interactions between the vehicle surface, air, and suspended particles can accumulate charge and generate a...
To overcome the low accuracy and inefficiency of the conventional influence coefficient method for micromotor rotor balancing, this paper proposes a rotor unbalance identification approach based on a long short-term memory (LSTM) neural network. Synt...
Mechanical stiffness is the displacement-dependent component of mechanical impedance. In joint coordinates, it describes the local mapping from small angular displacements to changes in joint torque; when using Cartesian coordinates, endpoint stiffne...
Coal-mine excavation generates dust and water mist that impair optical sensing; millimeter-wave radar supports surrounding-range perception for assisted autonomous guidance, while changing conditions can cause predictor mismatch and unfavorable onlin...
RGB-D simultaneous localization and mapping (SLAM) remains challenging in glass-rich environments with many glass walls and windows because depth measurements in glass regions are often missing, incorrect, or associated with background surfaces. Exis...
This methodological paper presents a data-efficient, explainable artificial intelligence (XAI) pipeline for non-destructive testing (NDT) of reinforced concrete (RC) under sparse-data conditions, addressing three closely connected challenges: cross-p...
When a mobile robot shares space with people, it should keep extra distance from them rather than treat them as ordinary obstacles. This study proposes a human-aware navigation method that detects a person by fusing a low-cost monocular RGB camera wi...
Residual tension in tendons embedded in prestressed concrete girders cannot be measured with conventional elasto-magnetic sensors, because those sensors encircle the strand and must be installed before casting. This article evaluates a system that ma...
Automatic modulation recognition (AMR) is essential for spectrum sensing and non-cooperative wireless monitoring. Although multi-scale temporal representations provide complementary information from received in-phase and quadrature (I/Q) signals, the...
Disaster-related social media posts combine images and text, but the benefits of large language model (LLM)-derived representations for crisis classification remain uncertain. We evaluate three pretrained vision–language pipelines—CLIP, L...
Robotic tactile perception benefits from short interactions and compact feature representations. This study investigates whether nine-channel magnetic, angular-rate, and gravity (MARG) signals can be fused for texture classification using shortened s...
This study proposes adaptive Studentized multi-kernel maximum mean discrepancy (MMD) for cross-condition machinery fault diagnosis. To account for sampling variation in mini-batch alignment, the multi-kernel MMD objective is normalized by empirical v...
Direction-of-arrival (DoA) estimation degrades under low signal-to-noise ratio, coherent multipath, and array imperfections. Coherent replicas are especially damaging because they collapse the rank of the signal covariance. We propose ReconUNet, a U-...
Ultrasound computed tomography provides speed-of-sound (SoS) images that complement conventional pulse–echo ultrasound, but estimating spatial acoustic structure from transmission data remains computationally demanding. We investigate a physics...
Conventional sensor-based sorting systems often rely on handcrafted image-processing features, which are highly task-specific, require expert knowledge, and are difficult to transfer to new sorting tasks. In contrast, deep learning enables automated...
Speed cameras are typically placed at high-crash locations to deter speeding. However, their deterrent effect extends beyond the installation site, as drivers moderate their speed in surrounding areas through halo and spillover effects. This study fo...
Quality control of exterior surfaces, particularly painted finishes, is a determining factor in the perceived quality of a device, which impacts the purchasing decision. Automating this process reduces potential human mistakes and improves efficiency...
The right ventricular outflow tract (RVOT) is an anatomically complex and spatially variable cardiac region that is difficult to characterise using isolated manual measurements. Although three-dimensional reconstruction techniques enable detailed vis...
Overtaking planning and decision-making are central to performance in autonomous racing. Limited maneuvering space on narrow closed circuits makes high-speed overtaking difficult and increases the risk of failed overtaking attempts and collisions. To...
Resistive switching devices are attractive candidates for nonvolatile memory and sensory information storage in intelligent sensor nodes, but their use near sensors remains limited by switching variability and resistance state instability. In this wo...
Detecting people from unmanned aerial vehicles (UAVs) in coastal environments is challenging because targets are often small, distant, and observed against complex backgrounds. This case study presents an application-driven system-level comparison of...
Ground-point classification (GPC) is essential for light detection and ranging (LiDAR)-derived terrain and vegetation products, yet most of the well-established GPC algorithms often require parameter calibration across varying terrain, vegetation, po...
Discrepancies between geometric priors and LiDAR observations can limit pose tracking of non-cooperative targets. This paper proposes online reference template adaptation that incorporates observations selected through registration-quality, incident-...
Modernized equipment features a multi-component structure, which is characterized by complex degradation behaviors, noisy monitoring measurements, and considerable uncertainty in life prediction, making accurate remaining useful life (RUL) forecastin...
Pneumatic ejection has dominated sensor-based sorting for decades, yet diffuse air jets cause bycatch at high mass throughput, and compressed-air generation accounts for a large share of operating cost. We propose (i) a grid-based sorting architectur...
Wearable gait assessment using foot-mounted inertial measurement units (IMUs) can extend mobility evaluation beyond the laboratory, but performance is limited by integration drift, magnetic disturbances, and gait event detection sensitivity during ou...
Reliable short-term wind power forecasts are difficult to obtain because power records are strongly non-stationary and hybrid networks are highly sensitive to parameter choices. This study proposes a Pearson–HOA–VMD–CNN–iTrans...
As a prevalent meteorological hazard across China, severe thunderstorm winds originate directly from storm cells. With the dense networking of Doppler weather radars in recent years, networked radar observations have become an essential technical sup...
MedROAD V3 is a real-time FHIR R4 streaming platform that turns clinical sensor streams into calibrated deterioration risk, evaluated retrospectively on a cardiac ICU cohort. FHIR event sourcing, Kafka and Faust assemble an 86-element feature vector...
This study develops a deep multimodal learning framework for lane-level traffic-speed estimation using five sensing sources: vehicle detection systems (VDS), closed-circuit television (CCTV), light detection and ranging (LiDAR), remote traffic microw...
The Eclipse Arrowhead Framework provides a service-oriented architecture for distributed industrial automation through service discovery, authorization, orchestration, and quality-of-service-aware service selection. While the framework formally defin...
Internet of Things (IoT) and Internet of Medical Things (IoMT) sensor deployments are pushing security decision-making from centralised servers towards resource-constrained edge gateways, where heavyweight deep learning detectors exceed the available...
Hybrid inertial navigation systems (HINSs) integrate physical, stabilized platforms, strapdown attitude algorithms, and rotation modulation technology, offering unique performance advantages. However, the non-ideal orthogonality of the gimbal frames...
Airborne electromagnetic (AEM) measurements are vulnerable to mixed environmental noise, and rapid signal decay hampers weak transient recovery. We propose WL-CNN-BiLSTM, integrating dual-stage wavelet filtering, parallel multi-scale convolutions, re...
Moisture-induced deformation, cracking, and biological deterioration threaten wooden cultural heritage, making reliable, nondestructive moisture assessment essential for preventive conservation. The microwave method has been recognized as an emerging...
Low-light image enhancement (LLIE) is fundamentally a restoration problem rather than a brightness adjustment task, as it aims to recover the underlying scene information from images captured under low signal-to-noise ratio conditions. However, most...
Public Snap Map activity, a short-lived stream of stories pinned to places, can serve as an opportunistic urban sensor, and this study measures what it reports and what it requires to report reliably. From more than 1.18 million public snaps collecte...
Bluetooth Low Energy (BLE) devices are ubiquitous across consumer, industrial, and healthcare IoT ecosystems, yet their resource-constrained nature typically precludes conventional cryptographic authentication, leaving device identification as a secu...
Low-cost multi-modal sensors make high-resolution exposomic and urban-health monitoring affordable, but their data are only as useful as they are trustworthy. Measurement bias, cross-sensitivity, temporal drift and domain shift between devices and si...
