Multi-band terahertz metamaterial absorbers offer new perspectives to achieve perfect absorption and multipoint information matching, which enable an ever-growing number of applications. In this study, a dual-band terahertz metamaterial absorber based on the metal split ring is designed. The absorber has perfect absorption peaks at 1.15 THz and 2.47 THz, and the absorption rate is more than 99%. The absorber produces a harp peak with a bandwidth of 0.008 at 2.47 THz, which has an extremely high quality factor of 308. The distribution of the electric field and surface current at two resonance points is analyzed using the finite element integration method. Through full wave simulation calculation, the maximum sensitivity of the analyte refractive index of the absorber is 400 GHz/RIU, and the maximum sensitivity of the thickness is 35 GHz/μm. The results show that the absorber can achieve highly sensitive detection of trace substances.
The traditional moldy wheat identification and detection method require complex processing steps, which take a long time and have less feature extraction ability, resulting in poor moldy wheat identification and detection. In this paper, a F-C-BLS terahertz spectral image recognition method for moldy wheat is proposed based on broad learning system. The F-C-BLS moldy wheat classification and recognition model is constructed to enhance the image quality and improve the network feature extraction. Experimental results show that the classification accuracy of our F-C-BLS network is 5.11%, 5.27%, 3.89 and 4.06% higher than that of BLS, RF, CNN and RNN, respectively. Therefore, our algorithm can effectively provide a new and effective method for the early identification of wheat mold.
The freshness of rice reflects the time that has elapsed since it was harvested and the extent of deterioration in the quality of the rice that has occurred during storage. Therefore, it is crucial to detect the freshness of rice samples; here, we undertake that task using terahertz images and a modified VGG network. Terahertz imaging is non-destructive, permits molecular fingerprinting, and is low in energy consumption. Terahertz imaging technology uses terahertz rays to irradiate the sample and obtains a terahertz image of the sample by processing and analyzing the transmission and reflection spectra of the sample. Terahertz imaging technology has been widely used in applications related to material identification, medical diagnoses, quality detection of agricultural products, and safety inspections. In this paper, terahertz images of rice stored for various lengths of time were analyzed using a terahertz imaging system. Due to a large amount of data and inconspicuous features of the terahertz image, the traditional 1D-VGG network is relatively insufficient in computing power. Thus, it is not well suited to the extraction of features from within the images. To resolve this issue, the Inception-ResNet-A asymmetric convolution module in the Inception-ResNet-V2 network has great computing power,which is introduced into the VGG19 network structure. This proposed network is found to increase identification accuracy up to 99.8%. This work indicates that terahertz images combined with the modified 1D-VGG network represent an efficient and practical method for identifying rice freshness; this work thus has great potential for use as a tool for ensuring food quality and safety.
The problem of insignificant image features and poor image quality in the acquisition of terahertz spectral images of unsound wheat. Therefore, this study proposed the FFDNet-VGG terahertz image enhancement algorithm based on the FFDNet denoising algorithm and VGG feature extraction algorithm. The experimental results showed that this algorithm could effectively improve the image quality compared with the traditional algorithms such as BM3D and WNNM, and the PSNR and SSIM of the enhanced images were 39.10 dB and 0.93, respectively. The wheat unsound grain images processed by FFDNet, VGG and FFDNet-VGG algorithms were verified by CNN classification network with the classification accuracy of 93.1% and 93.7%, respectively. FFDNet-VGG effectively enhances the wheat unsound grain terahertz spectral images.
The spectral characteristics of potassium sorbate in milk powder in the range of 0.2~2.0 THz have been measured with THz time-domain spectroscopy(THz-TDS). Its absorption and refraction spectra are obtained at room temperature in the nitrogen atmosphere. The results showed that potassium sorbate at 0.98 THz obvious characteristic absorption peak. The simple linear regression(SLR) model was taken to analyze the content of potassium sorbate in milk powder. The results showed that the absorption coefficient increases as the mixture potassium sorbate increases. The research is important to food quality and safety testing.
Grain logistics is the important component of the social logistics, which can be attributed to frequent circulation and the
great quantity. At present time, there is no modern grain logistics distribution management system, and the logistics cost
is the high. Geographic Information Systems (GIS) have been widely used for spatial data manipulation and model
operations and provide effective decision support through its spatial database management capabilities and cartographic
visualization. In the present paper, a spatial decision support system (SDSS) is proposed to support policy makers and to
reduce the cost of grain logistics. The system is composed of two major components: grain logistics goods tracking
model and vehicle routing problem optimization model and also allows incorporation of data coming from external
sources. The proposed system is an effective tool to manage grain logistics in order to increase the speed of grain
logistics and reduce the grain circulation cost.
In recent year, with the rapid development of GIS technology, more and more programming problems depend on the GIS
technology and professional model system. The solution of auxiliary programming problem by using GIS technology,
which has become very popular. GIS is an important tool and technology, that captures, stores, analyzes, manages, and
presents data that are linked to location. A grain logistics distribution system based on GIS is established, which provides
a visualization scheme during the process of grain circulation and supports users making decision and analyzing for grain
logistics enterprise.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.