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This manuscript investigates the effect of inner gate engineering (IGE) on the linearity and harmonic distortion performance of junctionless (JL) silicon-nanotube (Si-NT) FETs for wireless applications. The three configurations of JL-Si-NT-FET namely DS-JL-Si-NT-FET (inner gate covers drain & source regions both in addition to channel region), D--JL-Si-NT-FET (inner gate covers only drain region and channel region), and S--JL-Si-NT-FET (inner gate covers only source region and channel region) are compared to find the best configuration for wireless applications. The simulation results divulge that S--JL-Si-NT-FET configurations shows the improved linearity and harmonic distortion performance in terms of the performance metrics HD2, HD3, VIP3, IIP3, 1-db compression point, and higher order coefficients of transconductance (gm2 & gm3). Consequently, the S-JL-Si-NT-FET is the best configuration to be used for RFIC design and wireless applications.

Classification and Detection of vessels from cluttered images is an actively researched field of computer vision. The complexity of the coastal area makes it more complex in distinguishing different vessels. Environment conditions and visual aspect of the vessels make classification problem more difficult. The visual aspect ratio is very small and similar to most type of vessels. Since the traditional CNNs methods are slow and not much accurate, we present the robust method for the classification of ships derived from Support Vector Machine, bag of features and Convolutional Neural Networks (CNNs). First of all Support Vector Machine which is a supervised transfer leaning technique is introduced to remove the false candidates. As the ships have wide range of variability, bag of features are applied to handle the diversified features of different categories of vessels. Finally CNNs framework is applied for the deep feature extraction to successfully classify the ships from database. The proposed algorithm is trained with 16 different categories of ships with a data set of more than 2700 images. The algorithm trains and validates the results in terms of confusion matrix and provides better accuracy to classify the ships. The proposed Convolutional Neural Networks (CNNs) model outperforms the other previous methods and gives an accuracy of 91.04 percentages.