This work was supported by the BK-21 four program through the National Research Foundation of Korea (NRF) under the Ministry of Education. We would also like to present bundle of thanks to Nvidia Corporation for providing a support by donating us a Telsa K-40 GPU. Zubair khan and Farman Ali contributed equally and co-first authors ABSTRACT Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR's different stages effectively. This paper focuses on classifying the DR's different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer's virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.

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Underwater dam crack detection and classification based on sonar images is a challenging task because underwater environments are complex and because cracks are quite random and diverse in nature. Furthermore, obtainable sonar images are of low resolution. To address these problems, a novel underwater dam crack detection and classification approach based on sonar imagery is proposed. First, the sonar images are divided into image blocks. Second, a clustering analysis of a 3-D feature space is used to obtain the crack fragments. Third, the crack fragments are connected using an improved tensor voting method. Fourth, a minimum spanning tree is used to obtain the crack curve. Finally, an improved evidence theory combined with fuzzy rule reasoning is proposed to classify the cracks. Experimental results show that the proposed approach is able to detect underwater dam cracks and classify them accurately and effectively under complex underwater environments.

Abstract.Underwater localization are an importantpart of underwater sensor networks (USNs). USNs attracted significant attention, they are widely used for many applications, such as tsunami before the reaching inhabited areas, pollution monitoring, civilian and military applicationsOcean resource exploration, USNs which are mounted on the ocean bottom can detect earth quakes and Ocean monitoring. The variable speed of sound and the non-negligible node mobility due to water currents create a unique set of challenges for localization in UWSNs. This present a comprehensive survey of different techniques which are employed in USNs. This survey paper mainly focus on USNs, Localization techniques and its algorithms.