Background: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although many deep learning-based methods have been proposed to segment the disc and cup for fundus image, achieving highly accurate segmentation performance is still a great challenge due to the heavy overlap between the optic disc and cup. Methods: In this paper, we propose a two-stage method where the optic disc is firstly located and then the optic disc and cup are segmented jointly according to the interesting areas. Also, we consider the joint optic disc and cup segmentation task as a multi-category semantic segmentation task for which a deep learning-based model named DDSC-Net (densely connected depthwise separable convolution network) is proposed. Specifically, we employ depthwise separable convolutional layer and image pyramid input to form a deeper and wider network to improve segmentation performance. Finally, we evaluate our method on two publicly available datasets, Drishti-GS and REFUGE dataset. Results: The experiment results show that the proposed method outperforms state-of-the-art methods, such as pOSAL, GL-Net, M-Net and Stack-U-Net in terms of disc coefficients, with the scores of 0.9780 (optic disc) and 0.9123 (optic cup) on the DRISHTI-GS dataset, and the scores of 0.9601 (optic disc) and 0.8903 (optic cup) on the REFUGE dataset. Particularly, in the more challenging optic cup segmentation task, our method outperforms GL-Net by 0.7 $$\%$$% in terms of disc coefficients on the Drishti-GS dataset and outperforms pOSAL by 0.79 $$\%$$% on the REFUGE dataset, respectively. Conclusions: The promising segmentation performances reveal that our method has the potential in assisting the screening and diagnosis of glaucoma.

Opportunistic networks aim to provide reliable communications in an intermittently connected environment. The research in cache management of opportunistic networks has been done a lot in those aspects, such as queue strategy, cache replace, redundancy delete, etc. But most of the existing studies only focus on a subdivision of the buffer management. To deal with such case, this article proposes a comprehensive integration buffer management strategy, called comprehensive-integrated buffer management (CIM), which takes all information relevant to message delivery and network resources into consideration. The simulation experiments show that the CIM strategy improved the performance in terms of delivery ratio, overhead ratio, and average delivery delay.

In this paper, we develop a novel active video content caching scheme (RCC) based on recommendation system and consistent hash for device-to-device(D2D) communication. The RCC scheme is constructed by a cache placement scheme, a consistent hash algorithm, an optimal video segmentation scheme and an optimal video library segmentation scheme. To begin with, a well-designed cache placement scheme based on a mobile model with helper notes and video segmentation is proposed to reduce the redundancy of the videos and save users' cache. In order to solve the interruption problem caused by segmentation, a consistent hash algorithm is introduced to improve the success probability of D2D communication. According to the recommendation system, all users' predictive score for all videos can be calculated, which results that users' most interesting video files rather than popular video files as previous work can be obtained and cached in advance to improve the hitting probability. Furthermore, an optimal video segmentation scheme and an optimal video library segmentation scheme are developed to minimize the transmission delay and maximize the hitting probability respectively. Simulation results show that compared with other traditional caching schemes, the proposed RCC scheme can reach about 70% reduction in outage probability, 40% reduction in system latency, and 10% improvement in hitting probability, all of which can achieve the best performance.