Lunar Penetrating Radar (LPR) is one of the important scientific instruments onboard the Chang'e-3 spacecraft. Its scientific goals are the mapping of lunar regolith and detection of subsurface geologic structures. This paper describes the goals of the mission, as well as the basic principles, design, composition and achievements of the LPR. Finally, experiments on a glacier and the lunar surface are analyzed.
The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection of pavements, the thickness estimation of the thin top surface layer is still a difficult problem due to the limitations of GPR resolution and the similar permittivity of asphalt sub-layers. Besides, the detection of some road defects, including inadequate compaction and delamination at interfaces, require further practical study. In this paper, a newly-developed vehicle-mounted GPR detection system is introduced. We used a horizontal high-pass filter and a modified layer localization method to extract the underground layers. Besides, according to lab experiments and simulation analysis, we proposed theoretical methods for detecting the degree of compaction and delamination at the interface, respectively. Moreover, a field test was carried out and the estimated results showed a satisfactory accuracy of the system and methods.
A novel handheld pseudo random coded ultra-wideband (UWB) radar for human sensing applications is presented in this paper. In order to reduce the size of the radar and obtain good penetrability, the center frequency of the m-sequence is assigned to about 1.1 GHz. The peak voltage of the m-sequence is 4.5 Vpp, while the length of the m-sequence can be chosen to obtain different signal-to-noise ratio (SNR). The digital transmitter, the dual-channel receiver and the clock synchronization are discussed in detail. The experimental results show that the proposed handheld pseudo random UWB radar has good detection performance for human sensing applications in complex environment.
Free amino acids are an important indicator of the freshness of yellow tea. This study investigated a novel procedure for predicting the free amino acid (FAA) concentration of yellow tea. It was developed based on the combined spectral and textural features from hyperspectral images. For the purposes of exploration and comparison, hyperspectral images of yellow tea (150 samples) were captured and analyzed. The raw spectra were preprocessed with Savitzky-Golay (SG) smoothing. To reduce the dimension of spectral data, five feature wavelengths were extracted using the successive projections algorithm (SPA). Five textural features (angular second moment, entropy, contrast, correlation, and homogeneity) were extracted as textural variables from the characteristic grayscale images of the five characteristic wavelengths using the gray-level co-occurrence matrix (GLCM). The FAA content prediction model with different variables was established by a genetic algorithm-support vector regression (GA-SVR) algorithm. The results showed that better prediction results were obtained by combining the feature wavelengths and textural variables. Compared with other data, this prediction result was still very satisfactory in the GA-SVR model, indicating that data fusion was an effective way to enhance hyperspectral imaging ability for the determination of free amino acid values in yellow tea.
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