Linear Vector Quantization Algorithm for Pattern Recognition on Paper Currency's Feature Using UV Light

2015 ◽  
Vol 21 (10) ◽  
pp. 3151-3155
Author(s):  
Dewanto Harjunowibowo ◽  
Anif Jamaluddin ◽  
Sri Hartati ◽  
Rosihan Ari Yuana ◽  
Aris Budianto ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Vector Quantization ◽  
Uv Light ◽  
Linear Vector

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

The Design of a Universal Infrared Remote Repeater

2012 ◽  
Vol 466-467 ◽  
pp. 1100-1103
Author(s):  
Hong Shan Nie ◽  
Qiang Liu ◽  
Miao Li ◽  
Qing Jiang Li ◽  
Hai Jun Liu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Remote Control ◽  
Vector Quantization ◽  
Control Signal ◽  
Good Prospect ◽  
Clock Signal ◽  
Storage Efficiency ◽  
Pattern Clustering ◽  
Decoding Efficiency ◽  
Repetitive Pattern

In this paper, a common infrared remote control transmitter is designed, using which, a variety of infrared remote control signal can be decoded, stored and transmitted when users need. And with this design, the problem that remote controllers can not be universally operated is solved . In the design, the algorithm of pattern clustering is used to reduce the errors caused by the instability of clock signal. Moreover, the method of vector Quantization coding and repetitive pattern recognition is used to compress he remote control signal data which achieve a good decoding efficiency and a storage efficiency. Experiments show that the design is feasible and has a good prospect in application.

scholarly journals Osteoarthritis severity using linear vector quantization based first order feature

2019 ◽  
Vol 1211 ◽  
pp. 012045
Author(s):  
L Anifah ◽  
M H Purnomo ◽  
T L R Mengko ◽  
I KE Purnama
Keyword(s):  
Vector Quantization ◽  
Linear Vector ◽  
First Order

Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks

1993 ◽  
Vol 207 (2) ◽  
pp. 113-118 ◽  
Author(s):  
D T Pham ◽  
E Oztemel
Keyword(s):  
Pattern Recognition ◽  
Control Chart ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Composite Pattern ◽  
Pattern Recognition Systems ◽  
Network Modules ◽  
Recognition Systems ◽  
Control Chart Pattern ◽  
Chart Patterns

Pattern recognition systems made up of independent multi-layer perceptrons and learning-vector-quantization neural network modules have been developed for classifying control chart patterns. These composite pattern recognition systems have better classification capabilities than their individual modules. The paper describes the structures of these pattern recognition systems and the results obtained on using them.

Lung Cancer Nodule Location Diagnoses Using Linear Vector Quantization Based on Morphology Process

2017 ◽  
Vol 23 (12) ◽  
pp. 12359-12361
Author(s):  
Haryanto ◽  
Achmad Fiqhi Ibadillah ◽  
Kunto Aji ◽  
Puput Wanarti Rusimamto ◽  
Lilik Anifah
Keyword(s):  
Lung Cancer ◽  
Vector Quantization ◽  
Linear Vector
Sign in / Sign up

Export Citation Format

Share Document