scholarly journals Perancangan Perangkat Lunak Kompresi Citra Menggunakan Transformasi Wavelet dan PCA

2019 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Andrian Andrian
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Digital Image ◽  
Wavelet Transformation ◽  
Principal Component ◽  
Transformation Method ◽  
File Size ◽  
Compression Technique ◽  
The People ◽  
Good Compression

<p>Now days, The most needed of digital images is influenced by the people will that want to take a part of moment life into digital image. The good digital image has the big filesize, so it will need more space memory to saving more images. There is technique in image processing to decrease file size that is compression. By combine wavelet transformation method and Principal Component Analysis in developing application can produce the good compression technique.</p>

scholarly journals A Generic Block-Level Error Confinement Technique for Memory Based on Principal Component Analysis

2019 ◽  
Vol 9 (22) ◽  
pp. 4733
Author(s):  
Cuiping Shao ◽  
Huiyun Li ◽  
Zheng Wang ◽  
Jiayan Fang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Error Resilience ◽  
Component Analysis ◽  
Cmos Technology ◽  
Error Correcting Codes ◽  
Memory Storage ◽  
Protection Scheme ◽  
Bit Flip

Nanoscale CMOS technology has encountered severe reliability issues especially in on-chip memory. Conventional word-level error resilience techniques such as Error Correcting Codes (ECC) suffer from high physical overhead and inability to correct increasingly reported multiple bit flip errors. On the other hands, state-of-the-art applications such as image processing and machine learning loosen the requirement on the levels of data protection, which result in dedicated techniques of approximated fault tolerance. In this work, we introduce a novel error protection scheme for memory, based on feature extraction through Principal Component Analysis and the modular-wise technique to segment the data before PCA. The extracted features can be protected by replacing the fault vector with the averaged confinement vectors. This approach confines the errors with either single or multi-bit flips for generic data blocks, whilst achieving significant savings on execution time and memory usage compared to traditional ECC techniques. Experimental results of image processing demonstrate that the proposed technique results in a reconstructed image with PSNR over 30 dB, while robust against both single bit and multiple bit flip errors, with reduced memory storage to just 22.4% compared to the conventional ECC-based technique.

scholarly journals POTRET KESEJAHTERAAN RAKYAT DI PROVINSI BALI MENGGUNAKAN METODE CHERNOFF FACES

2013 ◽  
Vol 2 (3) ◽  
pp. 1
Author(s):  
I WAYAN WIDHI DIRGANTARA ◽  
KOMANG GDE SUKARSA ◽  
KOMANG DHARMAWAN
Keyword(s):  
Principal Component Analysis ◽  
Graphical Method ◽  
Principal Component ◽  
Component Analysis ◽  
Facial Features ◽  
The People ◽  
Visualization Techniques

Chernoff Faces method is a graphical method of visualization techniques to present data with many variables in the form of a cartoon face which can be determined by 20 parameters or less. In this research it was shown how the Chernoff Faces method was used to see welfare of the people in the province of Bali and Bali's nine regencies. To pair the variables and Chernoff’s facial features, then we used  Principal Component Analysis and survey to make the faces look more human. The result from 18 indicators of welfare of the people in the province of Bali, only 8 indicators were not really well. It was obtained too that Tabanan was the most prosperous regency and Karangasem was the lest prosperous regency.

Fast Face Recognition on GPUs

2014 ◽  
Vol 905 ◽  
pp. 543-547
Author(s):  
Yi Lei ◽  
Xiao Ya Fan ◽  
Meng Zhang
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Face Recognition ◽  
Processing Speed ◽  
Principal Component ◽  
Great Accuracy ◽  
Analysis Algorithm ◽  
Training Images ◽  
Principal Component Analysis Algorithm

Face recognition is popular in the field of pattern recognition and image processing. However, traditional recognition technologies spend too long there are a lot of images to be recognized or trained for great accuracy in the recognition. Parallel computing is an effective way to improve the processing speed. With the improvement of GPU performance, its widely applied in computing-concentrated data operations. This paper presents a study of performance speedup achieved by applying GPU for face recognition based on PCA (Principal Component Analysis) algorithm. We successfully accelerated the testing phase by 6868-folds compared to a sequential C implementation when it has 100 test images and 2400 training images.

scholarly journals Kernel Principal Component Analysis of Coil Compression in Parallel Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yuchou Chang ◽  
Haifeng Wang
Keyword(s):  
Principal Component Analysis ◽  
Parallel Imaging ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Computational Time ◽  
Parallel Mri ◽  
Compression Technique ◽  
Virtual Channels ◽  
Pca Method

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

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