Uncertainty Theory Based Partitioning for Cyber-Physical Systems with Uncertain Reliability Analysis

Reasonable partitioning is a critical issue for cyber-physical system (CPS) design. Traditional CPS partitioning methods run in a determined context and depend on the parameter pre-estimations, but they ignore the uncertainty of parameters and hardly consider reliability. The state-of-the-art work proposed an uncertainty theory based CPS partitioning method, which includes parameter uncertainty and reliability analysis, but it only considers linear uncertainty distributions for variables and ignores the uncertainty of reliability. In this paper, we propose an uncertainty theory based CPS partitioning method with uncertain reliability analysis. We convert the uncertain objective and constraint into determined forms; such conversion methods can be applied to all forms of uncertain variables, not just for linear. By applying uncertain reliability analysis in the uncertainty model, we for the first time include the uncertainty of reliability into the CPS partitioning, where the reliability enhancement algorithm is proposed. We study the performance of the reliability obtained through uncertain reliability analysis, and experimental results show that the system reliability with uncertainty does not change significantly with the growth of task module numbers.

Adaptive Enhancement Algorithm of High-Resolution Satellite Image Based on Feature Fusion

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.

Estimating Carbon, Nitrogen, and Phosphorus Contents of West–East Grassland Transect in Inner Mongolia Based on Sentinel-2 and Meteorological Data

Estimating the carbon (C), nitrogen (N), and phosphorus (P) contents of a large-span grassland transect is essential for evaluating ecosystem functioning and monitoring biogeochemical cycles. However, the field measurements are scattered, such that they cannot indicate the continuous gradient change in the grassland transect. Although remote sensing methods have been applied for the estimation of nutrient elements at the local scale in recent years, few studies have considered the effective estimation of C, N, and P contents over large-span grassland transects with complex environment including a variety of grassland types (i.e., meadow, typical grassland, and desert grassland). In this paper, an information enhancement algorithm (involving spectral enhancement, regional enhancement, and feature enhancement) is used to extract the weak information related to C, N, and P. First, the spectral simulation algorithm is used to enhance the spectral information of Sentinel-2 imagery. Then, the enhanced spectra and meteorological data are fused to express regional characteristics and the fractional differential (FD) algorithm is used to extract sensitive spectral features related to C, N, and P, in order to construct a partial least-squares regression (PLSR) model. Finally, the C, N, and P contents are estimated over a West–East grassland transect in Inner Mongolia, China. The results demonstrate that: (i) the contents of C, N, and P in large-span transects can be effectively estimated through use of the information enhancement method involving spectral enhancement, regional feature enhancement, and information enhancement, for which the estimation accuracies (R2) were 0.88, 0.78, and 0.85, respectively. Compared with the estimation results of raw Sentinel-2 imagery, the RMSE was reduced by 3.42 g/m2, 0.14 g/m2, and 13.73 mg/m2, respectively; and (ii) the continuous change trend and spatial distribution characteristics of C, N, and P contents in the west–east transect of the Inner Mongolia Plateau were obtained, which showed decreasing trends in C, N, and P contents from east to west and the characteristics of meadow > typical grassland > desert grassland. Thus, the information enhancement algorithm can help to improve estimates of C, N, and P contents when considering large-span grassland transects.

Strawberry Maturity Recognition Algorithm Combining Dark Channel Enhancement and YOLOv5

Aiming at the problems of low accuracy of strawberry fruit picking and large rate of mispicking or missed picking, YOLOv5 combined with dark channel enhancement is proposed. In “Fengxiang” strawberry, the criterion of “bad fruit” is added to the conventional three criteria of ripeness, near-ripeness, and immaturity, because some of the bad fruits are close to the color of ripe fruits, but the fruits are small and dry. The training accuracy of the four kinds of strawberries with different ripeness is above 85%, and the testing accuracy is above 90%. Then, to meet the demand of all-day picking and address the problem of low illumination of images collected at night, an enhancement algorithm is proposed to enhance the images, which are recognized. We compare the actual detection results of the five enhancement algorithms, i.e., histogram equalization, Laplace transform, gamma transform, logarithmic variation, and dark channel enhancement processing under the different numbers of fruits, periods, and video tests. The results show that combined with dark channel enhancement, YOLOv5 has the highest recognition rate. Finally, the experimental results demonstrate that YOLOv5 is better than SSD, DSSD, and EfficientDet in terms of recognition accuracy, and the correct rate can reach more than 90%. Meanwhile, the method has good robustness in complex environments such as partial occlusion and multiple fruits.

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640 × 480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.