Fast compression of computer-generated holographic images based on a GPU-accelerated skip-dimension vector quantization method

2013 ◽  
Vol 11 (5) ◽  
pp. 050901-50905 ◽  
Author(s):  
Y. K. Lam Y. K. Lam ◽  
W. C. Situ W. C. Situ ◽  
P. W. M. Tsang P. W. M. Tsang
Keyword(s):  
Vector Quantization ◽  
Quantization Method ◽  
Dimension Vector

scholarly journals USING LEARNING VECTOR QUANTIZATION METHOD FOR AUTOMATED IDENTIFICATION OF MYCOBACTERIUM TUBERCULOSIS

2015 ◽  
Vol 3 (1) ◽  
pp. 26 ◽  
Author(s):  
Endah Purwanti ◽  
Prihartini Widiyanti
Keyword(s):  
Image Processing ◽  
Mycobacterium Tuberculosis ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Quantization Method ◽  
Automated Identification ◽  
Clinical Specimens ◽  
Automated Method

In this paper, we are developing an automated method for the detection of tubercle bacilli in clinical specimens, principally the sputum. This investigation is the first attempt to automatically identify TB bacilli in sputum using image processing and learning vector quantization (LVQ) techniques. The evaluation of the learning vector quantization (LVQ) was carried out on Tuberculosis dataset show that average of accuracy is 91,33%.

scholarly journals Automatic Speaker Identification by Voice Based on Vector Quantization Method

2019 ◽  
Vol 8 (10) ◽  
pp. 2443-2445
Keyword(s):  
Feature Extraction ◽  
Vector Quantization ◽  
Speaker Identification ◽  
Voice Recognition ◽  
Recognition System ◽  
Automatic Identification ◽  
Quantization Method ◽  
Feature Vectors ◽  
Cepstral Features ◽  
Code Book

In this paper, the systems of speaker identification of a text-dependent and independent nature were considered. Feature extraction was performed using chalk-frequency cepstral coefficients (MFCC). The vector quantization method for the automatic identification of a person by voice has been investigated. Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Speakers were modeled using vector quantization (VQ). Using the extracted features, the code book from each speaker was built by clustering the feature vectors. Codebooks of all announcers were collected in the database. From the results, it can be said that vector quantization using cepstral features produces good results for creating a voice recognition system.

scholarly journals Application of Learning Vector Quantization Method in Kinect Device AS a Base of the Development of Behaviour Detection System

2017 ◽  
Vol 1 ◽  
pp. 171-174
Author(s):  
Eko Arianto ◽  
Laifa Rahmawati
Keyword(s):  
Mental Disorder ◽  
Vector Quantization ◽  
Student Behavior ◽  
Detection System ◽  
Learning Vector Quantization ◽  
Learning Rate ◽  
Quantization Method ◽  
Special Schools ◽  
Natural Motion ◽  
Detection Technology

One of the lessons for mental disorder students in Special Schools is practicum lessons in the form of vocational education. This lesson uses equipment that requires prudence. Mental disorder students have characteristics that are low memory and move based on intuition. Teachers should pay extra attention especially to detect student behavior during the learning. This detection is needed for learning to take place smoothly and students are safe from the dangers around the practicum place. Teacher's feedback on the detection obtained in the form of a warning from the teacher. This study is expected to be useful for providing a special detection pattern for students to assist teachers by providing feedback in the form of warnings using natural motion detection technology. This research was conducted using Kinect as data input and data was processed using artificial neural network and Learning Vector Quantization method. The dangerous attitude used in the test is the attitude of standing at the time of drilling position. The data used by training is 126 data and do training using LVQ. At the LVQ training stage, the training was conducted with parameter of Learning Rate 0,05, maximum Iteration 44, reduction of learning rate 0.01, and Learning rate minimum 0,02.

Sign in / Sign up

Export Citation Format

Share Document