Core-Point, Ridge-Frequency, and Ridge-Orientation Density Roles in Selecting Region of Interest of Fingerprint

Mukhaiyar, Riki

doi:10.21660/2017.30.tvet015

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…This approach is also called continuous classification [10], [11], [12]. Field orientation that is commonly used in building the vectors of numerical features containing local orientations [13], [14], [15], [16]. Besides that, the average range of the fingerprint is also used as an assistance feature in certain researches [11], [17].…”

Section: Proposed Methodsmentioning

confidence: 99%

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Mukhaiyar¹

2017

GEOMATE

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According to Galton-Henry, fingerprint classification varies into arch, tented arch, left loop, right loop, whorl, and twin loop schemes. The approach is depended on the existence of a core and delta of each fingerprint. If this method is implemented for all fingerprint benchmark databases, then less than 41% of the fingerprint is being classified, by 37.19% for FVC 2002 and 40.31% for FVC 2004. Therefore, in this research, three requirements are needed to improve the classification result of the fingerprint, i.e. core point and its number, ridge frequency and ridge direction, and tented arch as additional requirement. This approach improves the result which is only 5.94% and 1.56% that is unclassified for FVC 2002 and 2004 consecutively. Then, to evaluate the time taken in executing the algorithm, this research does the evaluation by offering two possible conditions of the input of the fingerprint. The first type is without the fingerprint classification, while the second type is with the classification step along the algorithm process. The latter type requires an additional step namely RoI selection process to select a desired area of the fingerprint.

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Section: Proposed Methodsmentioning

confidence: 99%

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Mukhaiyar¹

2017

GEOMATE

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“…According to the figure, gas sensor system (s 1 to s n ) receives flavor from one material (e.g. chemical material), then each sensor will provide an electrical signal (x 1 to x n ) [3].This signal will be processed by a pattern classification system. Then, the system produces the classes of the information about the flavor.…”

Section: Fig1 a System Of The Flavour's Sensoricmentioning

confidence: 99%

“…Materials can be identified through its unique flavor. There are people who are experts in classifying materials by smelling its flavor [1] [2] [3]. Though, some experts have a different opinion regarding to how accurate the rate of the classification process.…”

Section: Introductionmentioning

confidence: 99%

The Comparation of Back Propagation Method and Kohonen Method for Gas Identification

Mukhaiyar¹

2017

GEOMATE

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The identification process of gas flavor is conducted by using the output of gas sensor system to recognize a variety of gas flavor. The identification and analysis process of this system is processed by using an artificial neural network approaches those are back propagation and Kohonen method. According of the experiment's result, the best parameter for back propagation network is the momentum constant (α) = 0.7, the constant of the sigmoid function (β) = 4.5, constant learning (η) = 0.9, and the constant of convergence (ε) = 0001, convergence is achieved more or less in the 19 500 iterations (± 16 seconds). Meanwhile, the best classification for Kohonen network is for the output of 8 knots with an average of 80.7% uniformity (for a maximum of 500 times iteration, approximately ± 3 seconds). Thus, the best network to classify the signal pattern of gas flavor is back propagation network for the parameters (α) = 0.7, a constant sigmoid function (β) = 4.5, a constant learning (η) = 0.9, and a constant convergence (ε) = 0001.

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Analysis of pollutant load due to greywater from riverbanks settlement on Ciliwung River segment 2

Putri

Hadisoebroto

Hendrawan

2019

J. Phys.: Conf. Ser.

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Greywater from domestic activities on the riverbanks settlement in Indonesia usually channelled directly to the drainage or to the river. It will be decrease the river water quality. The aims of the research are to analyse the pollutant load that enters the drainage channel due to greywater from settlement in banks of Ciliwung River segment 2 and responses of society sanitation habits. The research was conducted at 6 villages are Babakan Pasar, Sukasari, Baranangsiang, Cibuluh, Kedung Badak, and Sempur. Water samples were taken from the drainage channel in the morning and evening, which are considered to represent the peak hours of greywater. Sampling was conducted in May, June and July 2018. Pollutant load based on BOD level in the drainage channel ranges from 4,903.19-132,804.33 kg/day. This condition shows the high tendency of pollution due to greywater domestic activities and absence of greywater treatment in residential areas. Almost 50% of the community understand the importance of the need for wastewater management. Community awarness for sanitation habits such as environment community service and maintenance and handling waste in drainage. The conclusion is that greywater treatment in riverbanks settlement is really needed to be applied.

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Core-Point, Ridge-Frequency, and Ridge-Orientation Density Roles in Selecting Region of Interest of Fingerprint

Cited by 5 publications

References 3 publications

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

Analysis of Galton-Henry Classification Method for Fingerprint Database FVC 2002 and 2004

The Comparation of Back Propagation Method and Kohonen Method for Gas Identification

Analysis of pollutant load due to greywater from riverbanks settlement on Ciliwung River segment 2

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