Discrete Cosine Transform-based Microscope Focal Adjustment with RBC Classification Using Support Vector Machine

2021 ◽  
Author(s):  
Lester N. Manuel ◽  
Analyn N. Yumang ◽  
Joseph Keenu L. Maravilla ◽  
Joseph Bryan G. Ibarra ◽  
Ericson D. Dimaunahan
Keyword(s):  
Support Vector Machine ◽  
Discrete Cosine Transform ◽  
Support Vector ◽  
Cosine Transform

scholarly journals DCT based feature extraction and support vector machine classification for musical instruments tone recognition

2021 ◽  
Vol 10 (5) ◽  
pp. 2796-2803
Author(s):  
Linggo Sumarno ◽  
Rifai Chai
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Discrete Cosine Transform ◽  
Recognition System ◽  
Musical Instruments ◽  
Combination Method ◽  
Support Vector ◽  
Transform Domain ◽  
Cosine Transform ◽  
Tone Recognition

The conducted research proposes a feature extraction and classification combination method that is used in a tone recognition system for musical instruments. It is expected that by implementing this combination, the tone recognition system will require fewer feature extraction coefficients than those previously investigated. The proposed combination comprises of feature extraction using discrete cosine transform (DCT) and classification using support vector machine (SVM). Bellyra, clarinet, and pianica tones were used in the experiment, with each indicating a tone with one, several, or many major local peaks in the transform domain. Based on the results of the tests, the proposed combination is efficient enough to be used in a tone recognition system for musical instruments. This is indicated in recognizing a tone, it only needs at least eight feature extraction coefficients.

A Novel Classification Method of Fish Based on Multi-Feature Fusion

2015 ◽  
Vol 713-715 ◽  
pp. 1513-1519 ◽  
Author(s):  
Wei Dong Du ◽  
Bao Wei Chen ◽  
Hai Sen Li ◽  
Chao Xu
Keyword(s):  
Discrete Cosine Transform ◽  
Feature Fusion ◽  
Acoustic Scattering ◽  
Scattering Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Correct Identification ◽  
Classification Problems ◽  
Cosine Transform ◽  
Identification Rate

In order to solve fish classification problems based on acoustic scattering data, temporal centroid (TC) features and discrete cosine transform (DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted. The extracted features of fish are reduced in dimension and fused, and support vector machine (SVM) classifier is used to classify and identify the fishes. Three kinds of different fishes are selected as research objects in this paper, the correct identification rates are given based on temporal centroid features and discrete cosine transform coefficients features and fused features. The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.

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