Alkali-Activated Foams Coated with Colloidal Ag for Point-of-Use Water Disinfection

Alkali-activated foams are ceramic-like materials prepared at near-ambient temperature. This study investigates them for point-of-use water disinfection, thus providing an alternative to ceramic filters fired at a high temperature. Alkali-activated foams with different compositions were characterized for the porosity, mechanical strength,...

Volumetric Pothole Detection from UAV-Based Imagery

Road networks are essential elements of a community's infrastructure and need regular inspection. Present practice requires traffic interruptions and safety risks for inspectors. The road detection system based on vehicle-mounted lasers is also quite mature, offering advantages such as high-precision defect detection, high automation, and...

A Thermoelectric Energy Harvesting System Assisted by A Piezoelectric Transducer Achieving 10-mV Cold-Startup and 82.7% Peak Efficiency

This article presents a 10mV-startup-voltage thermoelectric energy harvesting system, assisted by a piezoelectric generator (PEG) as a cold starter. It exploits the fact that when a thermoelectric energy harvesting system is implemented in a place where kinetic energy is also present, the PEG starter can provide a clock signal to start the...

Asymptotic convergence analysis and influence of initial guesses on composite Anderson acceleration

Although Anderson acceleration AA(m) has been widely used to speed up nonlinear solvers, most authors are simply using and studying the stationary version of Anderson acceleration. The behavior and full potential of the non-stationary version of Anderson acceleration methods remain an open question. Motivated by the hybrid linear solver...

Rapid extraction of pavement aggregate gradation based on point clouds using deep learning networks

Usage of asphalt mixture with poor gradation will most likely lead to pavement deficiency. There is a growing need for rapid and non-destructive methods to extract pavement aggregate gradation. In this study, a deep learning-based method that utilizes point clouds data for gradation extraction was proposed. Firstly, a data enhancement...

Flight Path Setting and Data Quality Assessments for Unmanned-Aerial-Vehicle-Based Photogrammetric Bridge Deck Documentation

Imagery from Unmanned Aerial Vehicles can be used to generate three-dimensional (3D) point cloud models. However, final data quality is impacted by the flight altitude, camera angle, overlap rate, and data processing strategies. Typically, both overview images and redundant close-range images are collected, which significantly increases the...

Sparsity-based Human Activity Recognition with PointNet using a Portable FMCW Radar

Radar-based solutions have attracted great attention in human activity recognition (HAR) for their advantages in accuracy, robustness, and privacy protection. The conventional approaches transform radar signals into feature maps and then directly process them as visual images. While effective, these image-based methods may not be the best...

Continuous Occupancy Mapping in Dynamic Environments Using Particles

Particle-based dynamic occupancy maps were proposed in recent years to model the obstacles in dynamic environments. Current particle-based maps describe the occupancy status in discrete grid form and suffer from the grid size problem, wherein a large grid size is unfavorable for motion planning while a small grid size lowers efficiency and...

Reconstructing compact building models from point clouds using deep implicit fields

While three-dimensional (3D) building models play an increasingly pivotal role in many real-world applications, obtaining a compact representation of buildings remains an open problem. In this paper, we present a novel framework for reconstructing compact, watertight, polygonal building models from point clouds. Our framework comprises three...

Composite Anderson acceleration method with two window sizes and optimized damping

In this article, we propose and analyze a set of fully nonstationary Anderson acceleration (AA) algorithms with two window sizes and optimized damping. Although AA has been used for decades to speed up nonlinear solvers in many applications, most authors are simply using and analyzing the stationary version of AA (sAA) with fixed window size...

Plot-level reconstruction of 3D tree models for aboveground biomass estimation

Complexity of forest structure is an important factor contributing to uncertainty in aboveground biomass estimates. In this study, we present a new method for reducing uncertainty in forest aboveground biomass (AGB) estimation based on plot-level terrestrial laser scanner (TLS) point clouds reconstruction. The method estimates the total AGB...

Learning to Reconstruct Compact Building Models from Point Clouds

Three-dimensional building models play a pivotal role in shaping the digital twin of our world. With the advance of sensing technologies, unprecedented data acquisition capabilities on capturing the built environment have surfaced, with photogrammetry and light detection and ranging being the two important sources, both of which can acquire...

Weyl Points In Superconducting Nanostructures

Topological band theory has contributed to some of the most astonishing developments in solid-state physics. The unique attributes that arise from topological effects are at the focus of modern experimental and theoretical research. Weyl point, a topological defect at the Fermi surface, enables topological transitions and transport phenomena....

A comparative study of point clouds semantic segmentation using three different neural networks on the railway station dataset

Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the...

SCIPoC: Semantic Classification of Indoor Point Cloud: A study into the possibilities of classifying indoor point cloud using a Deep Learning approach

With the constantly evolving range of applications for technology the quality and amount of data constantly increases as well. In this growing data environment, there is a constant search to provide more value to all data that is available for as little effort as possible. Our research tries to add such additional value by diving into the...

Dynamics of a Tidal Current System in a Marginal Sea: A Case Study of the Yellow Sea, China

Tidal currents belong to the main driving forces shaping the bathymetry of marginal seas. A globally unique radial sand ridge field exists in the South Yellow Sea off the central Jiangsu coast, China. Its formation is related to the distinctive “radial tidal current” pattern at that location. A generally accepted hypothesis is that the ...

PLADE: A Plane-Based Descriptor for Point Cloud Registration with Small Overlap

Traditional point cloud registration methods require large overlap between scans, which imposes strict constraints on data acquisition. To facilitate registration, users have to carefully position scanners to ensure sufficient overlap. In this article, we propose to use high-level structural information (i.e., plane/line features and their...

Edge-aware simplification of roof and facade point clouds into a uniformly dense point cloud

Point clouds can be obtained by airborne or terrestrial Light Detection and Ranging (LiDAR) scanning or directly from street-view panoramic imagery. Point clouds obtained from airborne scanners cover most roof information but lack facade information. Similar but orthogonal to airborne scanning, street-view point clouds cover well of the facade...