ALBERT

Description: A ratiometric wavelength measurement based on a Silicon-on-Insulator (SOI) integrated device is proposed and designed, which consists of directional couplers acting as two edge filters with opposite spectral responses. The optimal separation distance between two parallel silicon waveguides and the interaction length of the directional coupler are designed to meet the desired spectral response by using local supermodes. The wavelength discrimination ability of the designed ratiometric structure is demonstrated by a beam propagation method numerically and then is verified experimentally. The experimental results have shown a general agreement with the theoretical models. The ratiometric wavelength system demonstrates a resolution of better than 50 pm at a wavelength around 1550 nm with ease of assembly and calibration.

Description: This paper examines the impact of the Chinese recycling subsidy policy (CRS-policy) on the recycling and reuse industry across the whole product life cycle. We propose a closed-loop dynamic system to illustrate the production flow and interactions among various industry and market factors. A simulation analysis is conducted using data on gas auto engines in China to evaluate the effectiveness of the CRS-policy in encouraging product recycling and reuse. Specifically, we analyze the preventative and regenerative effects of the CRS-policy, and its impact on environmental pollution and social welfare. We further investigate how market factors, including the manufacturer’s innovation environment, consumer environmental awareness and sensitivity to the subsidy, and recycling and reuse industry profit, affect the effectiveness of the CRS-policy. The study provides strong evidence of the important role of the subsidy policy in the recycling and reuse industry and offers insightful recommendations for enhancing the effectiveness of the subsidy policy.

Description: Photocatalytic removal of organic pollution such as waste colored dyes was a promising technique for environment technique. However, effective photocatalysts were needed to enhance the photocatalytic efficiency. Ag/AgCl was regarded as high performance catalyst for photocatalytic degradation. Ag/AgCl nanoparticles were biochemically prepared with metabolin of living fungi which was used as reductant and characterized by X-Ray diffraction (XRD), UV-visible spectroscopy and transmission electron microscopy (TEM). The Ag/AgCl nanoparticle composites showed spherical aggregation shape with an average size of about 3–5 nm which is well inside the quantum regime. The UV-visible study showed that Ag/AgCl nanoparticles had strong visible light absorption and exhibited excellent visible-light-driven photocatalytic performance. Photocatalytic results indicated that the obtained Ag/AgCl nanoparticles were suitable for photocatalytic removal of RhB dye under visible light irradiation. The excellent photocatalytic activities could be attributed to the quantum size nanoparticles and the Plasmon resonance of Ag/AgCl composites.

Description: Sensors, Vol. 18, Pages 1912: Microwave Metamaterial Absorber for Non-Destructive Sensing Applications of Grain Sensors doi: 10.3390/s18061912 Authors: Yin Zhang Junming Zhao Jie Cao Bo Mao In this work, we propose a metamaterial absorber at microwave frequencies with significant sensitivity and non-destructive sensing capability for grain samples. This absorber is composed of cross-resonators periodically arranged on an ultrathin substrate, a sensing layer filled with grain samples, and a metal ground. The cross-resonator array is fabricated using the printed circuit board process on an FR-4 board. The performance of the proposed metamaterial is demonstrated with both full-wave simulation and measurement results, and the working mechanism is revealed through multi-reflection interference theory. It can serve as a non-contact sensor for food quality control such as adulteration, variety, etc. by detecting shifts in the resonant frequencies. As a direct application, it is shown that the resonant frequency displays a significant blue shift from 7.11 GHz to 7.52 GHz when the mass fraction of stale rice in the mixture of fresh and stale rice is changed from 0% to 100%. In addition, the absorber shows a distinct difference in the resonant absorption frequency for different varieties of grain, which also makes it a candidate for a grain classification sensor. The presented scheme could open up opportunities for microwave metamaterial absorbers to be applied as efficient sensors in the non-destructive evaluation of agricultural and food product quality.

Description: Remote Sensing, Vol. 9, Pages 846: A Novel Method of Change Detection in Bi-Temporal PolSAR Data Using a Joint-Classification Classifier Based on a Similarity Measure Remote Sensing doi: 10.3390/rs9080846 Authors: Jinqi Zhao Jie Yang Zhong Lu Pingxiang Li Wensong Liu Le Yang Accurate and timely change detection of the Earth’s surface features is extremely important for understanding the relationships and interactions between people and natural phenomena. Owing to the all-weather response capability, polarimetric synthetic aperture radar (PolSAR) has become a key tool for change detection. Change detection includes both unsupervised and supervised methods. Unsupervised change detection is simple and effective, but cannot detect the type of land cover change. Supervised change detection can detect the type of land cover change, but is easily affected and depended by the human interventions. To solve these problems, a novel method of change detection using a joint-classification classifier (JCC) based on a similarity measure is introduced. The similarity measure is obtained by a test statistic and the Kittler and Illingworth (TSKI) minimum-error thresholding algorithm, which is used to automatically control the JCC. The efficiency of the proposed method is demonstrated by the use of bi-temporal PolSAR images acquired by RADARSAT-2 over Wuhan, China. The experimental results show that the proposed method can identify the different types of land cover change and can reduce both the false detection rate and false alarm rate in the change detection.

Description: Sustainability, Vol. 10, Pages 1285: Land Cover Based Landscape Pattern Dynamics of Anhui Province Using GlobCover and MCD12Q1 Global Land Cover Products Sustainability doi: 10.3390/su10041285 Authors: Jinling Zhao Jie Wang Yu Jin Lingling Fan Chao Xu Dong Liang Linsheng Huang The development and free distribution of global land cover (GLC) products have greatly assisted in the evolution and analysis of relationships between land cover and landscape pattern. In this study, GlobCover and MCD12Q1 GLC datasets of 2005 and 2009 were comparatively used to analyze the variation of land cover in Anhui Province, China at both the class and landscape scale. The land cover classification schemes of both datasets were firstly reclassified to six types of forestland, grassland, wetland, cropland, artificial area, and others, and then FRAGSTATS was used to calculate the landscape indices. The results showed that from 2005 to 2009, the area density of ‘cropland’ landscape decreased, and it increased for ‘wetland’ and ‘artificial area’. The landscape fragmentation of ‘forestland’ and ‘grassland’ were larger. Moreover, over the same period, the class edge (CE) of ‘cropland’ was diminished; while the CE of ‘wetland’ was enhanced and the aggregation became larger. Conversely, the aggregation and shape complexity of ‘artificial area’ remained the same. The clumpiness index (CLUMPY) of ‘cropland’ varied from 0.8995 to 0.9050, indicating a higher aggregation and more concentrated distribution. The heterogeneity index (HT) value of MCD12Q1 and GlobCover datasets varied, respectively, from 0.9642 to 0.9053 and from 0.8867 to 0.8751, demonstrating that the landscape heterogeneity of Anhui Province was reduced from 2005 to 2009. Driving force analysis (DFA) was just performed for ‘artificial area’, ‘cropland’, and ‘wetland’ according to the 2005–2009 statistical yearbook data, because they were apt to be affected by human activities over a relatively short period of time.

Description: Sustainability, Vol. 10, Pages 1529: The Influence of Soil Physico-Chemical Properties and Enzyme Activities on Soil Quality of Saline-Alkali Agroecosystems in Western Jilin Province, China Sustainability doi: 10.3390/su10051529 Authors: Qing Zhao Jie Tang Zhaoyang Li Wei Yang Yucong Duan Soil organic carbon (SOC) plays a critical role in carbon cycling and soil quality of agroecosystems. Understanding the factors influencing SOC and the main indicators for soil quality can help in better soil management and sustainable agriculture. In this study, we selected three upland fields (U1, U2 and U3) and three paddy fields (P1, P2 and P3) of saline-alkali agroecosystems to study the impacts of soil physico-chemical properties (soil pH, exchangeable sodium percentage, electrical conductivity and bulk density) and enzyme activities (soil amylase, invertase, catalase and polyphenol oxidase) on SOC dynamics. The soil pH and exchangeable sodium percentage (ESP) had profoundly negative effect on SOC. Soil amylase and invertase activities were significantly positively correlated with SOC in both upland and paddy fields. Catalase promoted the accumulation of paddy SOC and polyphenol oxidase led to the acceleration of decomposition of upland SOC. Additionally, we combined SOC contents, soil physico-chemical properties and soil enzyme activities together to obtain the main indicators of soil quality. The results suggested that, in upland sites, the main factors affecting the soil quality were soil pH, ESP and SOC. As for paddy sites, the main indicators of soil quality were soil pH, amylase and invertase. By comparing the soil quality indicators between upland and paddy fields, it was observed that the inhibiting effect of ESP on paddy soil quality was not as significant as on upland soil quality due to the irrigation practice of rice planting, which could reduce the degree of soil alkalization. Therefore, paddy development has been widely used to improve the saline-alkali land in western Jilin Province of China.

Description: Sustainability, Vol. 10, Pages 2585: Evaluating Sampling Designs for Demersal Fish Communities Sustainability doi: 10.3390/su10082585 Authors: Jing Zhao Jie Cao Siquan Tian Yong Chen Shouyu Zhang Fish communities play an important role in determining the dynamics of marine ecosystems, while the evaluation and formulation of protective measures for these fish communities depends on the quality and quantity of data collected from well-designed sampling programs. The ecological model was used first to predict the distribution of the demersal fish community as the “true” population for the sampling design. Four sampling designs, including simple random sampling, systematic sampling, and stratified sampling with two sampling effort allocations (proportional allocation and Neyman allocation), were compared to evaluate their performance in estimating the richness and biodiversity indices of the demersal fish community. The impacts of two different temperature change scenarios, uniform temperature and non-uniform temperature increase on the performance of the sampling designs, were also evaluated. The proportional allocation yielded the best estimates of fish community richness and biodiversity relative to a synthetic baseline. However, its performance was not always robust relative to the simulated temperature change. When the water temperature changed unevenly, systematic sampling tended to perform the best. Thus, it is important to adjust the strata for a stratified sampling when the habitat experiences large changes. This suggests that we need to carefully evaluate the appropriateness of stratification when temperature change-induced habitat changes are large enough to result in substantial changes in the fish community.

Description: Sensors, Vol. 18, Pages 3000: Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN Sensors doi: 10.3390/s18093000 Authors: Yanchao Zhao Jie Wu Wenzhong Li Sanglu Lu The emerging edge computing paradigm has given rise to a new promising mobile network architecture, which can address a number of challenges that the operators are facing while trying to support growing end user’s needs by shifting the computation from the base station to the edge cloud computing facilities. With such powerfully computational power, traditional unpractical resource allocation algorithms could be feasible. However, even with near optimal algorithms, the allocation result could still be far from optimal due to the inaccurate modeling of interference among sensor nodes. Such a dilemma calls for a measurement data-driven resource allocation to improve the total capacity. Meanwhile, the measurement process of inter-nodes’ interference could be tedious, time-consuming and have low accuracy, which further compromise the benefits brought by the edge computing paradigm. To this end, we propose a measurement-based estimation solution to obtain the interference efficiently and intelligently by dynamically controlling the measurement and estimation through an accuracy-driven model. Basically, the measurement cost is reduced through the link similarity model and the channel derivation model. Compared to the exhausting measurement method, it can significantly reduce the time cost to the linear order of the network size with guaranteed accuracy through measurement scheduling and the accuracy control process, which could also balance the tradeoff between accuracy and measurement overhead. Extensive experiments based on real data traces are conducted to show the efficiency of the proposed solutions.

Description: Remote Sensing, Vol. 10, Pages 361: Directional ℓ0 Sparse Modeling for Image Stripe Noise Removal Remote Sensing doi: 10.3390/rs10030361 Authors: Hong-Xia Dou Ting-Zhu Huang Liang-Jian Deng Xi-Le Zhao Jie Huang Remote sensing images are often polluted by stripe noise, which leads to negative impact on visual performance. Thus, it is necessary to remove stripe noise for the subsequent applications, e.g., classification and target recognition. This paper commits to remove the stripe noise to enhance the visual quality of images, while preserving image details of stripe-free regions. Instead of solving the underlying image by variety of algorithms, we first estimate the stripe noise from the degraded images, then compute the final destriping image by the difference of the known stripe image and the estimated stripe noise. In this paper, we propose a non-convex ℓ 0 sparse model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise, and the locally continuous property of the underlying image as well. Moreover, the proposed non-convex model is solved by a proximal alternating direction method of multipliers (PADMM) based algorithm. In addition, we also give the corresponding theoretical analysis of the proposed algorithm. Extensive experimental results on simulated and real data demonstrate that the proposed method outperforms recent competitive destriping methods, both visually and quantitatively.