Device-free localization (DFL), which can detect and locate a person by measuring the changes in received signals, is one of the primary techniques in wireless sensor networks (WSNs). Recently, research on fingerprint-based localization in changing environments has been receiving increasing attention. However, when the environment changes due to furniture or other objects being moved, there is still much room for localization accuracy improvement in fingerprint-based DFL. In this paper, we propose a novel DFL algorithm for changing environments: this algorithm features an enhanced channel-selection method and adopts the logistic regression classifier to improve the localization accuracy. The proposed frequency channel-selection method selects two correlated channels with higher Pearson correlation coefficient both in the training and testing procedures, which would be more robust to the environmental change. Meanwhile, the logistic regression classifier could counteract the negative influence on the localization accuracy, without the need for rebuilding the database in fingerprint-based DFL. Experimental results demonstrate that the logistic regression classifier has the lowest error rate among three related methods (k-nearest neighbours classifier, linear discriminant analysis classifier, and random forests classifier). In addition, the localization accuracy has been further improved by the proposed DFL algorithm than by other state-of-theart fingerprint-based methods.

Device-free localization (DFL) based on wireless sensor networks (WSNs) is expected to detect and locate a person without the need for any wireless devices. Radio tomographic imaging (RTI) has attracted wide attention from researchers as an emerging important technology in WSNs. However, there is much room for improvement in localization estimation accuracy. In this paper, we propose a geometry-based elliptical model and adopt the orthogonal matching pursuit (OMP) algorithm. The new elliptical model uses not only line-of-sight information, but also non-line-of-sight information, which divides one ellipse into several areas with different weights. Meanwhile the OMP, which can eliminate extra bright spots in image reconstruction, is used to derive an image estimator. The experimental results demonstrate that the proposed algorithm could improve the accuracy of positioning by up to 23.8% for one person and 33.3% for two persons over some state-of-the-art RTI methods.

An increase in vegetation greenness can improve ecosystem productivity, but also reduce the water supply, creating the potential for conflicting water demands between ecosystems and humans. This problem has been well-assessed and is most evident in dry environments. However, in humid regions, the potential effects of vegetation greenness on water yields under drought conditions are not well understood. To address this gap, we focused on the Poyang Lake watershed in the humid region of southern China. Based on the Standardized Precipitation Evapotranspiration Index and a satellite-derived leaf area index dataset during the growing seasons of 1984 to 2013, several typical dry growing seasons were selected as the study conditions. An existing Water Supply Stress Index model was modified to investigate how the changes in vegetation greenness affected water yield and to explore potentially conflicting water demands between ecosystems and humans under drought conditions. Our results showed that an increase of 20–80% in vegetation greenness generally resulted in a reduction of 3–27% in water yield under drought conditions. Large reductions in water yield mainly were observed in forested areas due to large increases in forest greenness. Moreover, increased vegetation greenness caused a 2 to 3 times greater reduction in water yield during continuing and intensifying droughts than during a short moderate drought period. Thus, in this study, during continuing and intensifying droughts, increased vegetation greenness can cause or aggravate water conflicts in sub-watersheds with high forest cover and high human water demands. Therefore, given the increasing frequency of extreme climatic events, afforestation with a targeted approach should be implemented as it would provide the most benefits. In addition, selective harvesting in forested areas with high density could be an effective strategy to maintain water supply in humid regions.

An important aspect of querying whether a product is likely to be forged is to identify its anti-counterfeiting label. However, the use of image processing technology for label-specific texture analysis to quickly and effectively identify the anti-counterfeiting label has been widely studied. Aiming at the defects of the local binary pattern (LBP) and its variants in texture identification, this paper proposes a new texture model for anti-counterfeiting identification, that is, the circular local ternary pattern (CLTP). The highlight of our technology is that it extracts the effective local texture descriptors by using the random features of inkjet printing. This allows for the technology to not only resist the interference of noise and illumination in images of anti-counterfeiting patterns but also to encode and reorganize the fine linear shape structure. Specifically, this paper extracts the CLTP texture feature in the corresponding key areas and forms the final feature histogram vector for comparison through the one-to-one correspondence between the sample image and the inspected image of anti-counterfeiting pattern. Experiments prove that our method not only has high discrimination, stability and effectiveness but also provides a convenient and practical idea for anti-counterfeiting technology.