Trusted Cluster-Based Communication for Wireless Sensor Network Using Meta-Heuristic Algorithms

The mobile transient and sensor network’s routing algorithm detects available multi-hop paths between source and destination nodes.However,some methods are not as reliable or trustworthy as expected.Therefore,finding a reliable method is an important factor in improving communication security.For further enhancement of protected communication,we suggest a trust cluster based secure routing(TCSR)framework for wireless sensor network(WSN)using optimization algorithms.First,we introduce an efficient cluster formation using a modified tug of war optimization(MTWO)algorithm,which provides loadbalanced clusters for energy-efficient data transmission.Second,we illustrate the optimal head selection using multiple design constraints received signal strength,congestion rate,data loss rate,and throughput of the node.Those parameters are optimized by a butterfly optimal deep neural network(BO-DNN),which provides first-level security towards the selection of the best head node.Third,we utilize the lightweight signcryption to encrypt the data between two nodes during data transmission,which provides second-level security.The model provides an estimation of the trust level of each route to help a source node to select the most secure one.The nodes of the network improve reliability and security by maintaining the reliability component.Simulation results showed that the proposed scheme achieved 45.6%of delivery ratio.

Lossless compression of digital mammography using base switching method

Mammography is a specific type of imaging that uses low-dose x-ray system to examine breasts. This is an efficient means of early detection of breast cancer. Archiving and retaining these data for at least three years is expensive, diffi-cult and requires sophisticated data compres-sion techniques. We propose a lossless com-pression method that makes use of the smoothness property of the images. In the first step, de-correlation of the given image is done using two efficient predictors. The two residue images are partitioned into non overlapping sub-images of size 4x4. At every instant one of the sub-images is selected and sent for coding. The sub-images with all zero pixels are identi-fied using one bit code. The remaining sub- images are coded by using base switching method. Special techniques are used to save the overhead information. Experimental results indicate an average compression ratio of 6.44 for the selected database.

Simulation model for electron irradiated IGZO thin film transistors

An efficient drain current simulation model for the electron irradiation effect on the electrical parameters of amorphous In–Ga–Zn–O（IGZO） thin-film transistors is developed. The model is developed based on the specifications such as gate capacitance, channel length, channel width, flat band voltage etc. Electrical parameters of un-irradiated IGZO samples were simulated and compared with the experimental parameters and 1 kGy electron irradiated parameters. The effect of electron irradiation on the IGZO sample was analysed by developing a mathematical model.

Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method

Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.