Abstract:Monitoring open water bodies accurately is an important and basic application in remote sensing. Various water body mapping approaches have been developed to extract water bodies from multispectral images. The method based on the spectral water index, especially the Modified Normalized Difference Water Index (MDNWI) calculated from the green and Shortwave-Infrared (SWIR) bands, is one of the most popular methods. The recently launched Sentinel-2 satellite can provide fine spatial resolution multispectral images. This new dataset is potentially of important significance for regional water bodies' mapping, due to its free access and frequent revisit capabilities. It is noted that the green and SWIR bands of Sentinel-2 have different spatial resolutions of 10 m and 20 m, respectively. Straightforwardly, MNDWI can be produced from Sentinel-2 at the spatial resolution of 20 m, by upscaling the 10-m green band to 20 m correspondingly. This scheme, however, wastes the detailed information available at the 10-m resolution. In this paper, to take full advantage of the 10-m information provided by Sentinel-2 images, a novel 10-m spatial resolution MNDWI is produced from Sentinel-2 images by downscaling the 20-m resolution SWIR band to 10 m based on pan-sharpening. Four popular pan-sharpening algorithms, including Principle Component Analysis (PCA), Intensity Hue Saturation (IHS), High Pass Filter (HPF) and À Trous Wavelet Transform (ATWT), were applied in this study. The performance of the proposed method was assessed experimentally using a Sentinel-2 image located at the Venice coastland. In the experiment, six water indexes, including 10-m NDWI, 20-m MNDWI and 10-m MNDWI, produced by four pan-sharpening algorithms, were compared. Three levels of results, including the sharpened images, the produced MNDWI images and the finally mapped water bodies, were analysed quantitatively. The results showed that MNDWI can enhance water bodies and suppressbuilt-up features more efficiently than NDWI. Moreover, 10-m MNDWIs produced by all four pan-sharpening algorithms can represent more detailed spatial information of water bodies than 20-m MNDWI produced by the original image. Thus, MNDWIs at the 10-m resolution can extract more accurate water body maps than 10-m NDWI and 20-m MNDWI. In addition, although HPF can produce more accurate sharpened images and MNDWI images than the other three benchmark pan-sharpening algorithms, the ATWT algorithm leads to the best 10-m water bodies mapping results. This is no necessary positive connection between the accuracy of the sharpened MNDWI image and the map-level accuracy of the resultant water body maps.

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing (MEC) platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified. ! 1Compared with cloud and on-device learning, edge learning has its unique strengths. First, it has the most balanced resource support (see Fig. 1), which helps achieving the best tradeoff between the AI-model complexity and the model-training speed. Second, given its proximity to data sources, edge learning overcomes the drawback of cloud learning that fails to process real-time data due to excessive propagation delay and also network congestion caused by uploading data to the cloud. Furthermore, the proximity gives an additional advantage of location-and-context awareness. Last, compared with on-device learning, edge learning achieves much higher learning accuracy by supporting more complex models and more importantly aggregating distributed data from many devices. Due to the all-rounded capabilities, edge learning can support a wide spectrum of AI models to power a broad range of mission-critical applications, such as autodriving, rescue-operation robots, disaster avoidance and fast industrial control. Nevertheless, edge learning is at its nascent stage and thus remains a largely uncharted area with many open challenges. Fig. 1. Layered in-network machine learning architecture.The main design objective in edge learning is the fast intelligence acquisition from the rich but highly distributed data at subscribed edge devices. This critically depends on data processing at edge servers, as well as efficient communication between edge servers and edge devices. Compared with increasingly high processing speeds at edge servers, communication suffers from hostility of wireless channels (e.g., pathloss, shadowing, and fading), and consequently forms the bottleneck for ultra...

Treatment of wounds in special areas is challenging due to inevitable movements and difficult fixation. Common cotton gauze suffers from incomplete joint surface coverage, confinement of joint movement, lack of antibacterial function, and frequent replacements. Hydrogels have been considered as good candidates for wound dressing because of their good flexibility and biocompatibility. Nevertheless, the adhesive, mechanical, and antibacterial properties of conventional hydrogels are not satisfactory. Herein, cationic polyelectrolyte brushes grafted from bacterial cellulose (BC) nanofibers are introduced into polydopamine/polyacrylamide hydrogels. The 1D polymer brushes have rigid BC backbones to enhance mechanical property of hydrogels, realizing high tensile strength (21–51 kPa), large tensile strain (899–1047%), and ideal compressive property. Positively charged quaternary ammonium groups of tethered polymer brushes provide long‐lasting antibacterial property to hydrogels and promote crawling and proliferation of negatively charged epidermis cells. Moreover, the hydrogels are rich in catechol groups and capable of adhering to various surfaces, meeting adhesive demand of large movement for special areas. With the above merits, the hydrogels demonstrate less inflammatory response and faster healing speed for in vivo wound healing on rats. Therefore, the multifunctional hydrogels show stable covering, little displacement, long‐lasting antibacteria, and fast wound healing, demonstrating promise in wound dressing.

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