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LogicMindtech Nologies

IMAGE PROCESSING Projects for M. Tech, IMAGE PROCESSING Projects in Vijayanagar, IMAGE PROCESSING Projects in Bangalore, M. Tech Projects in Vijayanagar, M. Tech Projects in Bangalore, IMAGE PROCESSING IEEE projects in Bangalore, IEEE 2015 IMAGE PROCESSING Projects, MATLAB Image Processing Projects, MATLAB Image Processing Projects in Bangalore, MATLAB Image Processing Projects in Vijayangar

Engineering
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Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Title: Multi-scale two-directional two-dimensional principal component analysis and its application to high-dimensional biomedical signal classification Abstract— Goal: Time-frequency analysis incorporating the wavelet transform followed by principal component analysis(WT-PCA) has been a powerful approach for the analysis of biomedical signals such as electromyography (EMG),electroencephalography (EEG), electrocardiography (ECG), and Doppler ultrasound. Time-frequency coefficients at various scales were usually transformed into a one-dimensional array using only a single or a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT-PCA is not applicable to high-dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, wepresent a multi-scale two-directional two- dimensional principal component analysis (MS2D2PCA) method for the efficient and effective extraction of essential feature information from high-dimensional signals. Multi-scale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of sub-band signals among channels. In the second step, the two-directional two-dimensional principal component analysis operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movementsusing 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for high- dimensional biomedical signal analysis. .
Software & Hardware requirement: 1. Hardware requirement: 1. PC 2. RAM minimum 2GB 3. HDD minimum 100GB 2. Software requirement: 1. MATLAB 7.0 2. Signal processing toolbox 3. Image processing toolbox 4. Mathematical toolbox PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) [ For More Details please contact Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Mail: logicmindtech@gmail.com
Multi scale two-directional two-dimensional

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Multi scale two-directional two-dimensional

  • 1. Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Title: Multi-scale two-directional two-dimensional principal component analysis and its application to high-dimensional biomedical signal classification Abstract— Goal: Time-frequency analysis incorporating the wavelet transform followed by principal component analysis(WT-PCA) has been a powerful approach for the analysis of biomedical signals such as electromyography (EMG),electroencephalography (EEG), electrocardiography (ECG), and Doppler ultrasound. Time-frequency coefficients at various scales were usually transformed into a one-dimensional array using only a single or a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT-PCA is not applicable to high-dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, wepresent a multi-scale two-directional two- dimensional principal component analysis (MS2D2PCA) method for the efficient and effective extraction of essential feature information from high-dimensional signals. Multi-scale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of sub-band signals among channels. In the second step, the two-directional two-dimensional principal component analysis operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movementsusing 89-channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for high- dimensional biomedical signal analysis. .
  • 2. Software & Hardware requirement: 1. Hardware requirement: 1. PC 2. RAM minimum 2GB 3. HDD minimum 100GB 2. Software requirement: 1. MATLAB 7.0 2. Signal processing toolbox 3. Image processing toolbox 4. Mathematical toolbox PROJECT FLOW: First Review: Literature Survey Paper Explanation Design of Project Project Enhancement explanation Second Review: Implementing 40% of Base Paper Third Review Implementing Remaining 60% of Base Paper with Future Enhancement (Modification) [ For More Details please contact Logic Mind Technologies Vijayangar (Near Maruthi Medicals), Bangalore-40 Ph: 8123668124 // 8123668066 Mail: logicmindtech@gmail.com