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Spatial wave-function switched field effect transistor (SWSFET) switches the current flow between different channels inside the FET based on the applied voltage in its gate terminal. SWSFET can be used to implement multi-valued logic circuit with less number of circuit elements. Recently we presented unipolar inverter circuit using SWSFET. In this paper we develop a circuit model of SWSFET based on BSIM 3.2.0 and BSIM 3.2.4 and implement membership function using that circuit model of SWSFET.

The spatial wave-function switched field effect transistor (SWSFET) has two or three low band-gap quantum well channels inside the substrate of the semiconductor. Applied voltage at the gate region of the SWSFET, switches the charge carrier concentration in different channels from source to drain region. A circuit model of SWSFET is developed in BSIM 3.2.0. Membership function is implemented using the circuit model of the SWSFET. Membership function implementation using less number of SWSFET will reduce the device count in future analog-to-digital converter (ADC) and digital-to-analog converter (DAC) circuits.

To enhance the efficiency of malicious intrusion detection of network communication, a malicious intrusion detection model for the network communication in cloud data center is designed. Firstly, the data preprocessing includes three parts: normal sample data modeling, standard data membership calculation and standard data membership calculation. Then, the characteristic value collection stage is completed. Finally, the intrusion detection classification and trust value calculation are completed to conclude the malicious intrusion detection of the network communication in cloud data center. Exploratory findings show that the malicious intrusion detection model for the network communication in cloud data center improves the intrusion detection rate, and reduces the detection time and false alarm rate.

Content-based image retrieval (CBIR) has turned into an important research field with the advancement in multimedia and imaging technology. The term CBIR has been widely used to describe the process of retrieving desired images from a large collection on the basis of features such as color, texture and shape that can be automatically extracted from the images themselves. Considering the gap between low-level image features and the high-level semantic concepts in the CBIR, we proposed an image retrieval system for brain magnetic resonance images based on saliency map. First, the proposed approach exploits the ant colony optimization (ACO) technique to measure the image’s saliency through ants’ movements on the image. The textural features are then calculated from the saliency map of the images. The image retrieval of the proposed CBIR system is based on textural features and the fuzzy approach using Adaptive neuro-fuzzy inference system (ANFIS). Regarding the various categories of images in a database, we define some membership functions in the ANFIS output in order to determine the membership values of the images related to the existing categories. In online image retrieval, a query image is introduced to the system and the relevant images can be retrieved based on query membership values into different classes including normal or tumoral. The experimental results indicate that the proposed method is reliable and has high image retrieval efficiency compared with the previous works.