Gas Leakage Detection with Hyperspectral Imagery-Based Vegetation Stress Indices

The objective of this research is to provide a basis for gas leakage detection with hyperspectral cameras. The gas leakage attacks vegetations, which yields some spectral signature changes. As the vegetation under different stressors may induce similar effect. The spectral signature changes can be similar. A lab test was arranged to test three kinds of plants under four common natural stressors with a reference. All plants are routinely scanned with hyperspectral cameras every three days to obtain every developmental stage. Linear discriminant analysis (LDR) was applied in the data analysis to discriminate the plant with gas leakage treatment from the other stressors. The result shows that there is a more than 80 percent possibility that gas leakage can be distinguished from the other stressors

NasHD: Efficient ViT Architecture Performance Ranking using Hyperdimensional Computing

Neural Architecture Search (NAS) is an automated architecture engineering method for deep learning design automation, which serves as an alternative to the manual and error-prone process of model development, selection, evaluation and performance estimation. However, one major obstacle of NAS is the extremely demanding computation resource requirements and time-consuming iterations particularly when the dataset scales. In this paper, targeting at the emerging vision transformer (ViT), we present NasHD, a hyperdimensional computing based supervised learning model to rank the performance given the architectures and configurations. Different from other learning based methods, NasHD is faster thanks to the high parallel processing of HDC architecture. We also evaluated two HDC encoding schemes: Gram-based and Record-based of NasHD on their performance and efficiency. On the VIMER-UFO benchmark dataset of 8 applications from a diverse range of domains, NasHD Record can rank the performance of nearly 100K vision transformer models with about 1 minute while still achieving comparable results with sophisticated models

Hyperspectral Reflectance for Determination of Steel Rebar Corrosion and Cl− Concentration

In This Study, a New Method is Proposed to Determine Chloride Ion (Cl−) Concentration and Steel Rebar Corrosion from Hyperspectral Spectroscopy. Three Groups of Mortar Cubes with Water-To-Cement (W/c) Ratios of 0.5, 0.6, and 0.7 Were Subjected to Rapid Corrosion Testing in 3.5 Wt% NaCl Solution to Accelerate the Transport of Chloride Ions. Embedded Along the Centerline of Each Mortar Cube Was a Steel Rebar that Corroded When the Cl− Accumulation Around It Exceeded a Critical/threshold Concentration. Open Circuit Potential Was Measured to Characterize the Corrosion Possibility of Steel Rebar. Mortar Surfaces Were Scanned with a Hyperspectral Camera in the Infrared Range (1000 Nm − 2400 Nm), and the Reflectance Intensity at 2258 Nm Wavelength Was Extracted to Characterize Friedel\u27s Salt. the Possibility of Steel Corrosion Was Experimentally Shown to Increase with the Characteristic Reflectance Intensity that in Turn Decreases Linearly with the Diffusion Depth at a Given Corrosion State. for Each Type of Mortar Cubes with a Constant W/c Ratio, the Characteristic Reflectance Intensity Linearly Increases with the Cl− Content Up to 0.8 Wt%. Therefore, the Corrosion Status of Steel Rebar and Cl− Concentration Can Be Predicted based on the Combined Information from the Reflectance Intensity on the Mortar Surface and the Relation between Reflectance and Total Chloride Content