To investigate the adsorptive properties of a local laterite deposited in Chenzhou, Hunan province, China, the adsorptive properties of the natural laterite were investigated by batch technique in this study. The effects of contact time, pH, ionic strength, temperature, and the concentration on adsorption properties were also analyzed. The obtained experimental results show that the main mineral composition of laterite is kaolinite and montmorillonite. The adsorption process achieved equilibrium within 60 minutes and 90 minutes for Sr(II) and Cr(VI), respectively. The adsorption capacities for Cr(VI) and Sr(II) by the laterite were about 7.25 mg·g^-1 and 8.35 mg·g^-1 under the given experimental conditions, respectively. The equilibrium adsorption data were fitted to the second-order kinetic equation. The adsorption capacity for Sr(II) onto the laterite increased with increasing pH from 3–11 but decreased with increasing ionic strength from 0.001 to 1.0 M NaCl. The Sr(II) adsorption reaction on laterite was endothermic and the process of adsorption was favored at high temperature. Similarly, the adsorption capacity for Cr(VI) onto the laterite increased with increasing pH from 3–11, however, the ionic strength and temperature had an insignificant effect on Cr(VI) adsorption. The adsorption of Cr(VI) and Sr(II) was dominated by ion exchange and surface complexation in this work. Furthermore, the Langmuir and Freundlich adsorption isotherm model was used for the description of the adsorption process. The results suggest that the studied laterite samples can be effectively used for the treatment of contaminated wastewaters.

Effective recognition of tags in the dynamic measurement system would significantly improve the reading performance of the tag group, but the blurred outline and appearance of tag images captured in motion seriously limit the effectiveness of the existing tag group recognition. Thus, this paper proposes passive tag group recognition in the dynamic environment based on motion blur estimation and improved YOLOv2. Firstly, blur angles are estimated with a Gabor filter, and blur lengths are estimated through nonlinear modelling of a Generalized Regression Neural Network (GRNN). Secondly, tag recognition based on YOLOv2 improved by a Gaussian algorithm is proposed. The features of the tag group are analyzed by the Gaussian algorithm, the region of interest of the dynamic tag is effectively framed, and the tag foreground is extracted; Secondly, the data set of tag groups are trained by the end-to-end YOLOv2 algorithm for secondary screening and recognition, and finally the specific locations of tags are framed to meet the effective identification of tag groups in different scenes. A considerable number of experiments illustrate that the fusion algorithm can significantly improve recognition accuracy. Combined with the reading distance, the research presented in this paper can more accurately optimize the three-dimensional structure of the tag group, improve the reading performance of the tag group, and avoid the interference and collision of tags in the communication channel. Compared with the previous template matching algorithm, the tag group recognition ability put forward in this paper is improved by at least 13.9%, and its reading performance is improved by at least 6.2% as shown in many experiments.