• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.5
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• pp.35-42
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• 2008

In this paper, we propose a fast coeff_token decoding method based on the re-constructed VLCT. Since the conventional decoding method is still based on large memory accesses, it is not suitable for the multimedia services such as PMP, PMB, DVH-H where fast decoding and low power consumption are required. Based on the analysis for the codeword structure, new structure of the codeword and the corresponding memory architecture are developed in this paper. The simulation results show that the proposed algorithm achieves memory access saving from 10% to 57%, compared to the conventional decoding method. This meant that the issues of tow power consumption and high speed decoding can be resolved without video-quality and coding efficiency degradation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.