Real-time digital image stabilization based on regional field image gray projection

Digital image stabilization technique plays important roles in video surveillance and object acquisition.Many useful electronic image stabilization algorithms have been studied.A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions,respectively.For the odd field image channel,motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves.Then motion compensations can be obtained after using the Kalman filter method.Finally,the odd field image is adjusted according to the compensations.In the mean time,motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame.By minimizing absolute difference between odd and even field image gray projection curves of the current frame,the inter-field motion parameters can be estimated.Therefore,the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations.Finally,the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images.Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance.In addition,image blurring can be improved.

Inverse full state hybrid projective synchronization for chaotic maps with different dimensions

A new synchronization scheme for chaotic（hyperchaotic） maps with different dimensions is presented.Specifically,given a drive system map with dimension n and a response system with dimension m,the proposed approach enables each drive system state to be synchronized with a linear response combination of the response system states.The method,based on the Lyapunov stability theory and the pole placement technique,presents some useful features：（i） it enables synchronization to be achieved for both cases of n 〈 m and n 〉 m;（ii） it is rigorous,being based on theorems;（iii） it can be readily applied to any chaotic（hyperchaotic） maps defined to date.Finally,the capability of the approach is illustrated by synchronization examples between the two-dimensional H′enon map（as the drive system） and the three-dimensional hyperchaotic Wang map（as the response system）,and the three-dimensional H′enon-like map（as the drive system） and the two-dimensional Lorenz discrete-time system（as the response system）.

High-Throughput Chemotyping of Cannabis and Hemp Extracts Using an Ultraviolet Microplate Reader and Multivariate Classifiers

As the use of Cannabis products as natural medicines burgeons,it is also appearing as a food ingredient.It is important to screen Cannabis samples as ingredients by profiling their chemical compositions,which is referred to as chemotyping.Two sets of botanical extracts were studied.The first set is referred to as Cannabis contained plant materials from 15 samples of the sativa,indica,and hybrids of the two species.The second set contained 20 extracts from the variety of Cannabis sativa with low tetrahydrocannabinol(THC)concentrations,i.e.,below 0.3%,and,henceforth,will be referred to as hemp.An ultraviolet(UV)microplate reader provides a cost-effective and high-throughput method for identifying chemotypes of plant extracts by their spectra.The microplate reader affords rapid measurements of small volumes,e.g.,50μL,which demonstrates a potential to significantly reduce the analysis time and cost for Cannabis and hemp chemotyping or chemi-cal profiling.Replicate samples were measured on different days to demonstrate the robustness of the method.Projected difference resolution(PDR)maps were used to visualize the separations among the classes.Five multivariate classifiers,fuzzy rule-building expert system(FuRES),super partial least squares-discriminant analysis(sPLS-DA),support vector machine(SVM),and two tree-based support vector machines(SVMtreeG and SVMtreeH)were evaluated.The classifiers were validated with ten bootstrapped Latin partitions(BLPs).For the Cannabis extracts,the SVMtreeG yielded the best performance and the classification accuracy was 99.1±0.4%for spectra collected in the nonlinear absorbance range.For the hemp extracts,the SVM classifier performed the best with a 97.4±0.6%classification accuracy.These results demonstrate that the UV microplate reader coupled with multivariate classifiers can be used as a high-throughput and cost-effective approach for chemotyping Cannabis.