All human biological traits are unique as biometrics, such as fingerprint, palm, iris, palm veins, finger veins and other biometrics. Using these biometrics has always been challenging. One of the challenges in biometrics is physical injuries. Finger vein biometrics is one of the characteristics that is most resistant to physical injuries. Numerous algorithms for authentication have been proposed with the help of this biometrics, which have weaknesses such as high computational complexity and low identification accuracy. In this paper, a new method in identification based on maximum curvature algorithm and morphological operators is proposed. The maximum curvature algorithm extracts image properties using a set of operations based on image returns. This process has been enhanced in the proposed method with morphological operators. What distinguishes the proposed method from other methods is that this algorithm is very accurate in distinguishing images which are similar but belonging to different classes. The proposed method, in addition to having a reasonable computational complexity, has been able to record very good identification accuracy in the challenge of low image quality. The identification accuracy of the proposed method is 97.5%, which compared to other methods has been able to improve more than 3%. Also, the identification speed of the proposed method is 0.84 seconds, which is very fast in its kind. Manuscript profile

Precise segmentation of tumors in the breast is one of the most significant steps for MRIs and diagnosis tools using computers. Segmentation of the breast tumor is a demanding task due to some factors including partial volume effect, the similarity of the brightness of tumor texture with other surrounding non-tumor textures, variety in shape size and location of the tumor in different patients. Due to its vitality, the process of segmentation is carried out manually by specialists and its disadvantages are long computation time, and high cost. To overcome these issues, algorithms are required to segment images with high accuracy and no need for user intervention. This study presents a new method based on fuzzy connectedness algorithm and super pixels for tumor segmentation in magnetic resonance imaging (MRI). The proposed method is applied to a dataset built by the respected researchers on Matlab. The suggested method has been compared using two commonly used methods of clustering and morphological operators in tumor segmentation in magnetic resonance imaging (MRI). Mean average precision of 98.33 and the Dice similarity coefficient of 98.06 signifies the prominence of the suggested method in comparison with other methods compared using clustering algorithm 90.33 and morphological algorithm 91.83. Manuscript profile

The main task of a network-connected SOP is to improve power quality in an active distribution network. In the references, two control modes are created for SOP operation using back-to-back voltage source converters. Using these two modes depends on the distribution network conditions in which the SOP is located. The independent control of real and reactive power provides power control at first mode. The second mode is source restoration which with a voltage controller makes it possible to feed isolated loads due to network faults. In this research, an optimal method is presented based on the several SOPs’ performance for active distribution networks and with the help of it, it is presented using self-healing capability based on energy optimization that is presented. Compared to conventional switches, the coordination of multiple SOPs is capable of providing voltage support and effectively raising the source restoration range so as to enhance the load restoration level. Surveys and simulations in the standard IEEE 33-bus network in various scenarios show that if network reconfiguration are used with SOPs, loads in the power outage area are completely restored, which effectively improves the flexibility of distribution networks. Manuscript profile