ALBERT

Description: New design and optimization of charge pump rectifiers using diode-connected MOS transistors is presented in this paper. An analysis of the output voltage and Power Conversion Efficiency (PCE) is given to guide and evaluate the new design. A novel diode-connected MOS transistor for UHF rectifiers is presented and optimized, and a high efficiency N-stage charge pump rectifier based on this new diode-connected MOS transistor is designed and fabricated in a SMIC 0.18-μm 2P3M CMOS embedded EEPROM process. The new diode achieves 315 mV turn-on voltage and 415 nA reverse saturation leakage current. Compared with the traditional rectifier, the one based on the proposed diode-connected MOS has higher PCE, higher output voltage and smaller ripple coefficient. When the RF input is a 900-MHz sinusoid signal with the power ranging from −15 dBm to −4 dBm, PCEs of the charge pump rectifier with only 3-stage are more than 30%, and the maximum output voltage is 5.5 V, and its ripple coefficients are less than 1%. Therefore, the rectifier is especially suitableto passive UHF RFID tag IC and implantable devices.

Description: Hyperspectral remote sensing is used in precision agriculture to remotely and quickly acquire crop phenotype information. This paper describes the generation of a digital orthophoto map (DOM) and radiometric calibration for images taken by a miniaturized snapshot hyperspectral camera mounted on a lightweight unmanned aerial vehicle (UAV). The snapshot camera is a relatively new type of hyperspectral sensor that can acquire an image cube with one spectral and two spatial dimensions at one exposure. The images acquired by the hyperspectral snapshot camera need to be mosaicked together to produce a DOM and radiometrically calibrated before analysis. However, the spatial resolution of hyperspectral cubes is too low to mosaic the images together. Furthermore, there are no systematic radiometric calibration methods or procedures for snapshot hyperspectral images acquired from low-altitude carrier platforms. In this study, we obtained hyperspectral imagery using a snapshot hyperspectral sensor mounted on a UAV. We quantitatively evaluated the radiometric response linearity (RRL) and radiometric response variation (RRV) and proposed a method to correct the RRV effect. We then introduced a method to interpolate position and orientation system (POS) information and generate a DOM with low spatial resolution and a digital elevation model (DEM) using a 3D mesh model built from panchromatic images with high spatial resolution. The relative horizontal geometric precision of the DOM was validated by comparison with a DOM generated from a digital RGB camera. A surface crop model (CSM) was produced from the DEM, and crop height for 48 sampling plots was extracted and compared with the corresponding field-measured crop height to verify the relative precision of the DEM. Finally, we applied two absolute radiometric calibration methods to the generated DOM and verified their accuracy via comparison with spectra measured with an ASD Field Spec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). The DOM had high relative horizontal accuracy, and compared with the digital camera-derived DOM, spatial differences were below 0.05 m (RMSE = 0.035). The determination coefficient for a regression between DEM-derived and field-measured crop height was 0.680. The radiometric precision was 5% for bands between 500 and 945 nm, and the reflectance curve in the infrared spectral region did not decrease as in previous research. The pixel and data sizes for the DOM corresponding to a field area of approximately 85 m × 34 m were small (0.67 m and approximately 13.1 megabytes, respectively), which is convenient for data transmission, preprocessing and analysis. The proposed method for radiometric calibration and DOM generation from hyperspectral cubes can be used to yield hyperspectral imagery products for various applications, particularly precision agriculture.

Description: Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R670 only: R2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management.

Description: IJERPH, Vol. 14, Pages 1321: Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016 International Journal of Environmental Research and Public Health doi: 10.3390/ijerph14111321 Authors: Luwei Feng Bo Ye Huan Feng Fu Ren Shichun Huang Xiaotong Zhang Yunquan Zhang Qingyun Du Lu Ma In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM2.5) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM2.5. A kriging model was used to simulate the distribution of PM2.5 in 2015 and 2016. Relative risk (RR) at a specified PM2.5 exposure concentration was estimated with an integrated exposure–response (IER) model for different causes of mortality: lung cancer (LC), ischaemic heart disease (IHD), cerebrovascular disease (stroke) and chronic obstructive pulmonary disease (COPD). The population attributable fraction (PAF) was adopted to estimate deaths attributed to PM2.5. 72.02% of cities experienced decreases in PM2.5 from 2015 to 2016. Due to the overall decrease in the PM2.5 concentration, the total number of deaths decreased by approximately 10,658 per million in 336 cities, including a decrease of 1400, 1836, 6312 and 1110 caused by LC, IHD, stroke and COPD, respectively. Our results suggest that the overall PM2.5 concentration and PM2.5-related deaths exhibited decreasing trends in China, although air quality in local areas has deteriorated. To improve air pollution control strategies, regional PM2.5 concentrations and trends should be fully considered.

Description: Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R2, SDR2, V-RMSE, and SDRMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

Description: This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents’ regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.