The detection and segmentation of tumor region in brain image is a critical task due to the similarity between abnormal and normal region. In this article, a computer‐aided automatic detection and segmentation of brain tumor is proposed. The proposed system consists of enhancement, transformation, feature extraction, and classification. The shift‐invariant shearlet transform (SIST) is used to enhance the brain image. Further, nonsubsampled contourlet transform (NSCT) is used as multiresolution transform which transforms the spatial domain enhanced image into multiresolution image. The texture features from grey level co‐occurrence matrix (GLCM), Gabor, and discrete wavelet transform (DWT) are extracted with the approximate subband of the NSCT transformed image. These extracted features are trained and classified into either normal or glioblastoma brain image using feed forward back propagation neural networks. Further, K‐means clustering algorithm is used to segment the tumor region in classified glioblastoma brain image. The proposed method achieves 89.7% of sensitivity, 99.9% of specificity, and 99.8% of accuracy.

Summary Electricity generation, transmission, distribution, and consumption are efficiently monitored and controlled by the next‐generation power grid called a smart grid systems (SGs) with the help of information and communication technology (ICT). Due to this ICT intervention, the SGs are facing a lot of security threats. The main security threats are tampering of smart meters and meter data because they are fixed in the customer places without any hardware security. This may result in inaccurate billing and wrong decisions related to grid management. To overcome these issues, an energy‐efficient privacy‐preserving and physically secure mutual authentication scheme is proposed for providing secure communication in SGs by using the one‐way hash functions, bitwise exclusive‐OR operations, physically unclonable functions (PUFs), and reverse fuzzy extractor. The security and performance analysis section ensures that the proposed scheme provides essential security features with less computation and communication overhead compared to that of other existing schemes. Therefore, it is better suitable for resource‐limited SGs.

In recent years, mortality rate with high-grade tumor has been increased significantly especially with glioblastoma (GBM) brain tumor while compared to other malignant brain tumor. Here, the amount of dead cells accommodated with the tumor tissue in GBM brain tumor play a vital task and necessitate an earlier diagnosis of malignancy with the GBM tumor. It inspires to implement new automatic diagnosis system which detects the dead cells and tumor tissue with the GBM brain tumor, such that the survival rate of the diseased can easily be prognosis by the Radiologist and Neurosurgeon. The main objective of this article is to detect the amount of dead cells with respect to tumor tissue associated with the GBM brain tumor which desires the impact factor of the brain tumor. In this framework, initially, the new contrast enhancement modality is incorporated to enhance the gray information over the dead cells and the tumor tissue with the T1-weighted MRI GBM brain tumor. In this enhancement, the edges of the tumor cells and its dead cells are magnified efficiently. As the noises and outliers with MR image is unpredictable and it experiences the variable amount of noises over the local window, the contextual information over each pixel of the image is adaptively modified with respect to the amount of noise over local window using adaptive contextual clustering. The performance evaluation of the framework is investigated which exhibits the overwhelming result compared to conventional detection techniques.

This paper presents a design of typical multilayer on-chip inductor to determine the layout parameters of the desired inductance value of electromagnetic modeling. The inductance and quality factor of multilayer on-chip spiral inductors are determined by its layout parameters and technological parameters. These layout parameters must be optimized to obtain the maximum quality factor at the desired frequency of operation. An electromagnetic model with fewer assumptions than empirical equations and higher efficiency than full-field solvers would be welcome. So would facile comparisons of different inductor structures. This paper describes recent works on the electromagnetic modeling of on-chip inductor structures applied to the comparison of inductor geometries, including the traditional spiral inductor and a novel multilayer inductor. The electromagnetic modeling of the investigative model is presented. The modeling and simulation are implemented using the method of moments. To simulate the proposed algorithm, the EM Simulator software is used.