International Journal of Digital Signals and Smart Systems (4 papers in press)
Cluster based Non-Local Filters for Colour Image denoising
by FARHA FATINA WAHID, SUGANDHI K, RAJU G
Abstract: Non-Local filters have evolved as an alternative to local filters for image denoising. Even though, these filters relay on the similarity of pixel neighbourhoods within a search window, there are chances that similar pixel neighbourhoods may not be present in the search window. This problem is resolved in Cluster based NLM (CNLM) a recent modification to the basic NLM. CNLM suggests an alternative method for the formation of search window. The pixels in a given image are divided into clusters. Clustering is carried out based on the similarity of neighbourhoods of pixels. A given pixel P belonging to a cluster C is modified as the weighted sum of all other pixels in C. In this paper, we have carried out an extensive study of the CNLM algorithm using colour images. Further, we experimented the cluster approach with Non-Local Euclidean Median Algorithm (NLEM) and Improved Non-Local Euclidean Median Algorithm (INLEM). The study reveals that the performance of cluster based approach is better than the respective basic algorithms for low noise densities in terms of performance evaluation measures.
Keywords: Image Filtering; NLM; CNLM; NLEM; INLEM; Clusters.
Logarithmic Cost Based Improved Constant Modulus (LCICM) Type Blind Channel Equalization Algorithm For M-QAM/M-PSK Signal
by Prakhar Priyadarshi, C.S. Rai
Abstract: In this paper, authors have proposed the blind channel equalization algorithm based on logarithmic cost function for QAM/PSK signal which is widely used in the digital communication system. CMA (Constant Modulus Algorithm) and MCMA (Modified Constant Modulus Algorithm) ,the two widely referenced algorithms for blind equalization of a QAM/PSK signal, exhibit very slow convergence rates and large steady-state mean square error. Proposed algorithm attains faster convergence speed and low steady state mean square error by incorporating the improved logarithmic cost based error function in the resilient back propagation based framework. This makes the weight adaption of equalizer simple. Simulation results show that the proposed algorithm has better convergence rate and lower steady state error in comparison to CMA and MCMA algorithms.
Keywords: Equalization; Logarithmic cost function; RPROP; CMA ;MCMA.
A Case Study of Advanced Metering Infrastructure
by Hyeong-Jin Choi, Sisam Park, Wonsuk Ko, Essam A. Al-Ammar
Abstract: In this study, AMI (Advanced Metering Infrastructure) system is developed for the energy management system. To prove an efficiency of renewable systems such as wind, PV, and thermal systems, demonstration site that has the monitoring system of AMI system is selected. Next, the energy demand profile of the selected site is investigated to analyze effects of the use of renewable sources. To show the saving cost, RTP (Real Time Pricing) tariff is applied to the load shifting operating scenario. The result illustrates that electricity cost can be decreased about 11,600 KRW/day and it is equivalent to 9.65 USD/day, 3.8% saving of electricity cost.
Keywords: Advanced Metering Infrastructure; Energy Operation Scenario; Load Shifting Simulation; Real Time Pricing; Renewable Energy.
Classification of 3D Magnetic Resonance Brain Images
Using Texture Measures From Orthogonal Planes
by Yahia Samah, Ben Salem Yassine, Abdelkrim Mohamed Naceur
Abstract: In this paper, the performance of two new promising operators for
the analysis of 3D textures based on feature extraction is validated. The first
operator is the Descriptor Patterns from three orthogonal planes (DDP-TOP),
a new feature descriptor that considers the co-occurrences on three orthogonal
planes. The second operator is the Grey Level Co-occurence Matrix from
Three Orthogonal Planes (GLCM-TOP) which is an extension of the 2D Grey
Level Co-occurence Matrix method. To evaluate the proposed approaches, we
realized various experimentations using the 3D Brainweb database with various
Brain Magnetic Resonance Imaging (MRI) parameters. Six levels of noise are
considered to simulate real imaging conditions and different T1, T2 and PDweighted acquisition sequences. This way, the robustness of the used methods
against image artifacts is evaluated. In order to classify the MR images of brain
into healthy and diseased, several tests are performed in the same conditions
of work using the classifier multiclass Support Vector Machines (SVM). The
Local Binary Patterns (LBP), a best known method of texture analysis, is used
for comparison. Using the DDP-TOP operator, excellent experimental results are
obtained that prove the robustness of our approach with respect to the noise level
and to different image contrasts. Advantages of the DDP-TOP include speed of
treatment and simple computation.
Keywords: 3D texture; Brain MR images; DDP-TOP; GLCM-TOP; LBP-TOP;