1690 papers
This paper presents a semi-automated, multiband 3D photogrammetric workflow that integrates visible, ultraviolet, and near-infrared data with PCA and K-means clustering to accurately detect and quantify material changes on complex cultural heritage surfaces, bridging the gap between high-resolution documentation and long-term conservation monitoring.
This study demonstrates that regulating abrasive media viscosity to the 2000–2500 Pa·s range in abrasive flow finishing of additively manufactured waveguides effectively balances boss surface smoothing and edge profile retention by reducing edge removal and improving machining uniformity while maintaining a surface roughness reduction rate above 70%.
This paper presents a portable, fully automated system utilizing telecentric machine vision and motorized control to achieve micron-level precision in intravitreal injection dose preparation, significantly improving accuracy and reproducibility compared to error-prone manual methods.
This paper proposes a low-cost, energy-efficient edge-based intrusion detection system using an ESP32 microcontroller to identify and alert on unauthorized devices in wireless networks, offering a viable alternative to traditional, resource-heavy security solutions for small-scale environments.
This study employs an Explainable AI framework to demonstrate that residential electricity demand in Kenya's prepaid sector is primarily driven by historical consumption patterns rather than weather or tariffs, revealing that simple autoregressive models perform comparably to complex machine learning approaches for accurate forecasting.
This paper argues that while Dutch amphibious housing cannot be directly replicated in Peru due to differing regulatory and economic contexts, a hybrid approach combining engineered buoyant retrofits for existing stilt houses with community-based design and phased financing offers a viable pathway to adapt Peru's flood-prone settlements.
This method article proposes a translational engineering framework that integrates nano-enhanced activated carbon adsorption with multi-model machine-learning sedimentation forecasting to address coupled emerging contaminant and sedimentation challenges, comparing established U.S. practices with a proposed low-cost, modular adaptation for Andean water bodies in Peru while identifying the need for future field validation.
This paper proposes a Digital Twin Residual (DTR)-driven fault diagnostic method for multi-subsystem satellites that incorporates subsystem coupling and transforms raw data into residual features to overcome existing modeling limitations and achieve superior diagnostic performance.
This paper presents a theoretical framework for GNSS multipath detection by modeling normalized early, prompt, and late correlation values as a 3D Gaussian distribution, where the resulting equiprobability ellipsoid's geometric transformations (translation, scaling, and rotation) are analytically linked to multipath parameters to derive a statistically rigorous detection boundary.
This paper extends a statistical-domain GNSS multipath detection framework by mapping normalized correlation vectors into RGB color space to derive interpretable Value, Saturation, and Hue features that complement traditional statistical thresholds and enhance detection performance in urban environments.