Comparative Analysis of Quantum based Receivers with Linear and Nonlinear Adaptive Receivers

In this paper,we propose a new linear multiuser receiver for synchronous code-division multiple-access (CDMA) systems,referred to as the orthogonal multiuser(OMU) receiver.Unlike the linear minimum mean-squared error(MMSE) receiver,the OMU receiver depends only on the signature vectors and does not require knowledge of the received amplitudes or the channel signal-to-noise ratio(SNR).Here we develop methods that construct an optimal set of vectors with a specified inner product structure,from a given set of vectors in a complex Hilbert space.The optimal vectors are chosen to minimize the sum of the squared norms of the errors between the constructed vectors and the given vectors.An algorithm has been developed using the principles of quantum parameters and some of its axioms and constraints.In place of the classical matched filter(MF) receiver we propose a modified receiver.This approach assumes that improving the accuracy will necessarily result in im- proved performance.The simulation results provided here suggests that in certain cases the OMU and POMU receivers can significantly increase the probability of correct detection with low error rate over the MF receiver.

Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques

This paper presents,a new approach of Medical Image Pixels Clustering(MIPC),aims to trace the dissimilar patterns over the Magnetic Resonance(MR)image through the process of automatically identify the appropriate number of distinct clusters based on different improved unsupervised clustering schemes for enrichment,pattern predication and deeper investigation.The proposed MIPC consists of two stages:clustering and validation.In the clustering stage,the MIPC automatically identifies the distinct number of dissimilar clusters over the gray scale MR image based on three different improved unsupervised clustering schemes likely improved Limited Agglomerative Clustering(iLIAC),Dynamic Automatic Agglomerative Clustering(DAAC)and Optimum N-Means(ONM).In the second stage,the performance of MIPC approach is estimated by measuring Intra intimacy and Intra contrast of each individual cluster in the result of MR image based on proposed validation method namely Shreekum Intra Cluster Measure(SICM).Experimental results showthat the MIPC approach is better suited for automatic identification of highly relative dissimilar clusters over the MR cancer images with higher Intra closeness and lower Intra contrast based on improved unsupervised clustering schemes.