Complementary Metamaterial Sensor for Nondestructive Evaluation of Dielectric Substrates

In this paper, complementary metamaterial sensor is designed for nondestructive evaluation of dielectric substrates. The design concept is based on electromagnetic stored energy in the complementary circular spiral resonator (CCSR), which is concentrated in small volume near the host substrate at resonance. This energy can be employed to detect various electromagnetic properties of materials under test (MUT). Effective electric permittivity and magnetic permeability of the proposed sensor is extracted from scattering parameters. Sensitivity analysis is performed by varying the permittivity of MUT. After sensitivity analysis, a sensor is fabricated using standard PCB fabrication technique, and resonance frequency of the sensor due to interaction with different MUT is measured using vector network analyzer (AV3672series). The transcendental equation is derived for the fabricated sensor to calculate relative permittivity for unknown MUTs. This method is very simple and requires calculating only the resonant frequency, which reduces the cost and computation time

High-Sensitivity Microwave Sensor for Liquid Characterization Using a Complementary Circular Spiral Resonator

This paper describes a low-cost, small size, and high-sensitivity microwave sensor using a Complementary Circular Spiral Resonator (CCSR), which operates at around 2.4 GHz, for identifying liquid samples and determining their dielectric constants. The proposed sensor was fabricated and tested to effectively identify different liquids commonly used in daily life and determine the concentrations of various ethanol–water mixtures at by measuring the resonant frequency of the CCSR. Using acrylic paint, a square channel was drawn at the most sensitive position of the microwave sensor to ensure accuracy of the experiment. To estimate the dielectric constants of the liquids under test, an approximate model was established using a High-Frequency Simulator Structure (HFSS). The results obtained agree very well with the existing data. Two parabolic equations were calculated and fitted to identify unknown liquids and determine the concentrations of ethanol–water mixtures. Thus, our microwave sensor provides a method with high sensitivity and low consumption of material for liquid monitoring and determination, which proves the feasibility and broad prospect of this low-cost system in industrial application

A Novel Formaldehyde-Free Wood Adhesive Synthesized by Straw Soda Lignin and Polyethyleneimine

To eliminate toxic formaldehyde from wood-based panels, a new formaldehyde-free wood adhesive (named OL/PEI adhesive) was synthesized by a reaction of oxidized lignin (OL) and polyethylenimine (PEI) reaction in the presence of sodium periodate. The curing mechanism of the OL/PEI adhesive was clarified by Fourier transform infrared spectroscopy (FTIR) and solid-state cross-polarization magic angle spinning carbon-13 nuclear magnetic resonance (CP/MAS13C-NMR) spectroscopy. The results showed that the sodium periodate could selectively oxidize wheat straw lignin to produce the ortho-quinone, and then the ortho-quinone in OL could further react with amino groups in PEI to form the OL/PEI adhesive. The as-prepared poplar particleboard was investigated with regard to hot-pressing temperature, the hot-pressing time, the OL/PEI weight ratio, and the dosage of OL/PEI adhesive. Under the optimum conditions, e.g., hot pressing temperature of 180 °C, hot pressing time of 13 min, the OL/PEI weight ratio of 1:1, and the dosage of 10%, OL/PEI adhesive was found to disperse evenly into the voids among the shavings of poplar particleboard, followed by the curing of OL/PEI adhesive using hot-pressing to form tightly bonds between the shavings. The resulting particleboard reached the requirement of mechanical properties (GB/T 4897.3-2003), higher water resistance properties, and better heating resistivity. This study demonstrated a new way to produce a formaldehyde-free wood adhesive with unique properties. This material could replace formaldehyde wood adhesive in wood bonding

Tea Cultivation Suitability Evaluation and Driving Force Analysis Based on AHP and Geodetector Results: A Case Study of Yingde in Guangdong, China

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures

Tea Cultivation Suitability Evaluation and Driving Force Analysis Based on AHP and Geodetector Results: A Case Study of Yingde in Guangdong, China

Tea is an economically important crop. Evaluating the suitability of tea can better optimize the regional layout of the tea industry and provide a scientific basis for tea planting plans, which is also conducive to the sustainable development of the tea industry in the long run. Driving force analysis can be carried out to better understand the main influencing factors of tea growth. The main purpose of this study was to evaluate the suitability of tea planting in the study area, determine the prioritization of tea industry development in this area, and provide support for the government’s planning and decision making. This study used Sentinel image data to obtain the current land use data of the study area. The results show that the accuracy of tea plantation classification based on Sentinel images reached 86%, and the total accuracy reached 92%. Then, we selected 14 factors, including climate, soil, terrain, and human-related factors, using the analytic hierarchy process and spatial analysis technology to evaluate the suitability of tea cultivation in the study area and obtain a comprehensive potential distribution map of tea cultivation. The results show that the moderately suitable area (36.81%) accounted for the largest proportion of the tea plantation suitability evaluation, followed by the generally suitable area (31.40%), the highly suitable area (16.91%), and the unsuitable area (16.23%). Among these areas, the highly suitable area is in line with the distribution of tea cultivation at the Yingde municipal level. Finally, to better analyze the contribution of each factor to the suitability of tea, the factors were quantitatively evaluated by the Geodetector model. The most important factors affecting the tea cultivation suitability evaluation were temperature (0.492), precipitation (0.367), slope (0.302), and elevation (0.255). Natural factors influence the evaluation of the suitability of tea cultivation, and the influence of human factors is relatively minor. This study provides an important scientific basis for tea yield policy formulation, tea plantation site selection, and adaptation measures

Application and Evaluation of Wavelet-Based Denoising Method in Hyperspectral Imagery Data

Part 1: GIS, GPS, RS and Precision FarmingInternational audienceThe imaging hyper-spectrometer is highly susceptible to the presence of noise and its noise removal is regularly necessary before any derivative analysis. A wavelet-based(WT) method is developed to remove noise of hyperspectral imagery data, and commonly used denoising methods such as Savitzky-Golay method(SG), moving average method(MA), and median filter method(MF) are compared with it. Smoothing index(SI) and comprehensive evaluation indicator(η) are designed to evaluate the performance of the four denoising methods quantitatively. The study is based on hyperspectral data of wheat leaves, collected by Pushbroom Imaging Spectrometer (PIS) and ASD Fieldspec-FR2500 (ASD) in the key growth periods. According to SI andη, the denoising performance of the four methods shows that WT>SG=MA>MF and WT>MA>MF>SG, respectively. The comparison results reveal that WT works much better than the others with the SI value 0.28 and η value 5.74E-05. So the wavelet-based method proposed in this paper is an optimal choice to filter the noise, in terms of balancing the contradiction between the smoothing and feature reservation ability

Detection of diseased pine trees in unmanned aerial vehicle images by using deep convolutional neural networks

This study presents a method that uses high-resolution remote sensing images collected by an unmanned aerial vehicle (UAV) and combines MobileNet and Faster R-CNN for detecting diseased pine trees. MobileNet is used to remove backgrounds to reduce the interference of background information. Faster R-CNN is adopted to distinguish between diseased and healthy pine trees. The number of training samples is expanded due to the insufficient number of available UAV images. Experimental results show that the proposed method is better than traditional machine learning approaches, such as support vector machine and AdaBoost, and methods of DCNN, such as Alexnet, Inception and Faster R-CNN. Through sample expansion and background removal, the proposed method achieves effective detection of diseased pine trees in UAV images by using deep learning technology