Informatics Publishing Limited

K. Malathy ¹, B. Justus Rabi ²

Affiliations
1 Karpagam University, Coimbatore - 641021, Tamil Nadu, IN
2 Shri Andal Alagar College of Engineering, Chennai - 600026, Tamil Nadu, IN

Abstract

The aim is to design a new architecture called "Novel Radix-2 Single path Delay Commutator (R2SDC)" for improving the architectural performances of Fast Fourier Transformation (FFT) technique. In proposed new architecture, only commutator structures and complex multipliers are used to convert the time domain signals into frequency domain signals. In order to match the frequency response, delay elements are used appropriately. Traditional pipelined architectures have multiple complex multiplication units for performing frequency transformation techniques. But, proposed R2SDC structure has single complex multiplication unit per stage. Data flow structures of R2SDC architecture has been designed through Very Large Scale Integration (VLSI) System design environment. Proposed novel architecture called R2SDC FFT offers 21.4% improvements in hardware slices, 26.76% improvements in Look Up Tables (LUTs), 23.49% improvements in maximum output required time after clock and 31.88% improvements in power consumptions than traditional pipelined Radix-2 Single path Delay Feedback (R2SDF) FFT architecture. Also proposed architecture offers 4.61% improvements in hardware slices, 6.52% improvements in LUTs and 10.10% improvements in power consumption than combined SDC-SDF FFT architecture. In future, proposed R2DSC FFT architecture will be useful in Orthogonal Frequency Division Multiplexing (OFDM) architecture for estimating the frequency response of digital signals. It will be useful in large distance communications based applications

Keywords

Analog to Digital Converter, Combined Single path Delay Feedback –Single path Delay Commutator, Orthogonal Frequency Division Multiplexing, Radix-2 Single path Delay Commutator, Very Large Scale Integration

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S. Allin Christe ¹, K. Malathy ¹, A. Kandaswamy ²

Affiliations
1 Department of Electronics and Communication Engineering, PSG College of Technology, Coimbatore, IN
2 Department of Bio-Medical Engineering, PSG College of Technology, Coimbatore, IN

Abstract

Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Relevant application in neuroradiology is the segmentation of MRI data sets of the human brain into the structure classes gray matter, white matter and cerebrospinal fluid (CSF) and tumor. In this paper, brain image segmentation algorithms such as Fuzzy C means (FCM) segmentation and Kohonen means(K means) segmentation were implemented. In addition to this, new hybrid segmentation technique, namely, Fuzzy Kohonen means of image segmentation based on statistical feature clustering is proposed and implemented along with standard pixel value clustering method. The clustered segmented tissue images are compared with the Ground truth and its performance metric is also found. It is found that the feature based hybrid segmentation gives improved performance metric and improved classification accuracy rather than pixel based segmentation.

Keywords

K-Means, Fuzzy C-Means, Fuzzy Kohonen Means Clustering, Distance of Clustering, Von Dongen Index.

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