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Wireless sensor networks constitute the platform of a broad range of applications related to national security, surveillance, military, health care, and environmental monitoring. The coverage of WSN has answered the questions about quality of service (surveillance) which can be provided by WSN. Therefore, maximizing coverage using the resource constrained nodes is a non-trivial problem. The coverage problem for wireless sensor network (WSN) has been studied extensively in recent years, especially when combined with connectivity and energy efficiency. In this paper we present a survey of coverage problem. And besides some basic design considerations in coverage of WSN we describe two challenges, namely, maximizing network lifetime and network connectivity. We also provide a brief summary and comparison of existing coverage schemes. 

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment.

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TechnologyAs the energy consumption of multi-core systems becomes increasingly prominent, it's a challenge to design an energy-efficient real-time scheduling algorithm in multi-core systems for reducing the system energy consumption while guaranteeing the feasibility of real-time tasks. In this paper, we focus on multi-core processors, with the global Dynamic Voltage Frequency Scaling (DVFS) and Dynamic Power Management (DPM) technologies. In this setting, we propose an energy-efficient real-time scheduling algorithm, the Time Local remaining execution plane based Dynamic Voltage Frequency Scaling (TL-DVFS). TL-DVFS utilizes the concept of Time Local remaining execution (TL) plane to dynamically scale the voltage and frequency of a processor at the initial time of each TL plane as well as at the release time of a sporadic task in each TL plane. Consequently, TL-DVFS can obtain a reasonable tradeoff between the real-time constraint and the energy-saving while realizing the optimal feasibility of sporadic tasks. Mathematical analysis and extensive simulations demonstrate that TL-DVFS always saves more energy than existing algorithms, especially in the case of high workloads, and guarantees the optimal feasibility of sporadic tasks at the same time.

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