In this paper, we have developed a new method of accurate detection of retinal blood vessels based on a deep convolutional neural network (CNN) model. This method plays an important role in the observation of many eye diseases. Retinal Images have many issues that make the process of vessels segmentation very hard. We treat each issue of the retina image with the greatest observation to obtain a well-segmented image. The first step is to apply a pre-processing method based on fuzzy logic and image processing tactics. In a second step, in order to generate the segmented images, we propose a strided encoderdecoder CNN model. This network is trained and optimized using the Dice Loss function that supports the class imbalance problem that is in the database. The proposed model has a U-Net shape, but it is deeper and the pooling layers are replaced with strided convolutional layers in the encoder. This modification allows for a more precise segmentation of vessels and accelerates the training process. The last step is post-processing for removing the noisy pixels as well as the shadow of the optic disc. The performance of the proposed method was evaluated on DRIVE and STARE databases. The proposed method gives a sensitivity of 0.802 and 0.801 respectively on DRIVE and STARE, with an accuracy of 0.959 and 0.961 respectively. We focused on sensitivity and accuracy measurements that represent the accuracy of the model, especially tiny vessels. According to the results, the model outperforms many other proposed methods, especially in the abovementioned measures.

The harsh testing environments of underwater scenarios make it extremely hard to plan a reasonable routing protocol for Underwater Sensor Networks (UWSNs). The main challenge in UWSNs is energy confinement. It is needed to plan an energy effective scheme which increases the life span of the network and also reduces the energy usage in data transfer from supplier to sink. In this research, we present the design of a routing protocol known as Energy Harvesting in UWSN (EH-UWSN). EH-UWSN is a compact, energy efficient, and high throughput routing protocol, in which we present utilization of energy gaining with coordinating transfer of data packets through relay nodes. Through Energy Harvesting, the nodes are capable to recharge their batteries from the outside surrounding with the ultimate objective to improve the time span of network and proceed data through cooperation, along with restricting energy usage. At the sink node, the mixing plan applied is centered on Signal-to-Noise Ratio Combination (SNRC). Outcomes of EH-UWSN procedure reveal good results in terms of usage of energy, throughput, and network life span in comparing with our previous Cooperative Routing Scheme for UWSNs (Co-UWSN). Simulation results show that EH-UWSN has consumed considerably lesser energy when compared with Co-UWSN along with extending network lifetime and higher throughput at the destination.

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Composites being the key ingredients of the manufacturing in the aerospace, aircraft, civil and related industries, it is quite important to check its quality and health during its manufacture or in service. The most commonly found problem in the CFRPs is debonding. As debonds are subsurface defects, the general methods are not quite effective and require destructive tests. The Optical Pulse Thermography (OPT) is a quite promising technology that is being used for detecting the debonds. However, the thermographic time sequences from the OPT system have a lot of noise and normally the defects information is not clear. For solving this problem, an improved tensor nuclear norm (I-TNN) decomposition is proposed in the concatenated feature space with multilayer tensor decomposition. The proposed algorithm utilizes the frontal slice of the tensor to define the TNN and the core singular matrix is further decomposed to utilize the information in the third mode of the tensor. The concatenation helps embed the low-rank and sparse data jointly for weak defect extraction. To show the efficacy and robustness of the algorithm experiments are conducted and comparisons are presented with other algorithms.