No abstract

For the study of anatomical structure and image processing of MRI medical images techniques of noise removal have become an important practice in medical imaging application. In medical image processing, precise images need to be obtained to get accurate observations for the given application. The goal of any de-noising technique is to remove noise from an image which is the first step in any image processing. The noise removal method should be applied watchful manner otherwise artefacts can be introduced which may blur the image. In this paper, performance evaluation of the of MRI image de-noising techniques is provided. The techniques used are namely the median and Gaussian filter, Max filter [11], Min filter [11], and Arithmetic Mean filter [8]. All the above filters are applied on MRI brain and spinal cord images and the results are noted. A new method is proposed which modifies the existing median filter by adding features. The experimental result of the proposed method is then analyzed with the other three image filtering algorithms. The output image efficiency is measured by the statistical parameters like root mean square error (RMSE), signal-tonoise ratio (SNR), peak signal-to-noise ratio (PSNR).

WSN (Wireless Sensor Network) has energy constraints. Main energy consumption is at the (Tx) transmitter/ receiver (Rx) side, which is proportional to data/command packets /frames. Failure at the receiving end asks for re-transmission leading to more power consumption. WSN follows structured layers like medium access control (MAC), physical layer (PHY), link layer etc. PHY deals with RF transmission including antenna. In this paper the focus is given on smart antenna as, WSN deals with systems which need to be adaptive, especially in unknown time varying scenarios. With data transmission; location positioning (localization) and efficient routing are the factors to be considered. Localization of node is done using range measurements which include received signal strength, time of arrival, time difference of arrival (TDOA) and angle-of-arrival (AoA) measurement. By improvising antenna performance i.e. small beam width, we can achieve less erroneous data reception, leading to less energy consumption. In this work, we propose a method based on Non-dominated Sorting Genetic Algorithm (NSGA-II) for localization of nodes using smart antennas. Comparative analysis of its performance is done with different algorithms like LMS (Least Mean Square), LMS and PSO (Particle Swarm Algorithm) for varying number of elements and spacing between elements. We demonstrated that the proposed method achieves very good accuracy and precision in angle measurements as compared with existing approaches.