Fingerprint Classification Based on Orientation Field

Fingerprint Classification Based on Orientation Field Zahraa Hadi Khazaal and Safaa S. Mahdi Al- Nahrain University, Baghdad, Iraq ABSTRACT

This paper introduces an effective method of fingerprint classification based on discriminative feature gathering from orientation field. A nonlinear support vector machines (SVMs) is adopted for the classification. The orientation field is estimated through a pixel-Wise gradient descent method and the percentage of directional block classes is estimated. These percentages are classified into four-dimensional vector considered as a good feature that can be combined with an accurate singular point to classify the fingerprint into one of five classes. This method shows high classification accuracy relative to other spatial domain classifiers.

KEYWORDS

Orientation Field, Singular point, SVMs Classifier, Feature Vector.

Original Source URL http://wireilla.com/papers/ijesa/8418ijesa03.pdf https://wireilla.com/ijesa/current.html

Performance Evaluation of Fuzzy Logic and Back Propagation Neural Network for Hand Written Character Recognition

Performance Evaluation of Fuzzy Logic and Back Propagation Neural Network for Hand Written Character Recognition Heba M. Abduallah and Safaa S. Mahdi Al- Nahrain University, Baghdad, Iraq ABSTRACT

Fuzzy c-mean is one of the efficient tools used in character recognition. Back propagation neural network is another powerful that may be used in such field. A comparison between fuzzy c-mean and BP neural network classifiers are presented in this research. The comparison was based on recognition efficiency; this efficiency was evaluated as the ratio of the number of assigned characters with unknown one to the number of character set related to that character. The fuzzy Cmean and BP neural network algorithms were tested on a set of hand written and machine printed dataset named The Chars74K dataset.

KEYWORDS

Fuzzy c-mean, character recognition, Back propagation neural network, recognition efficiency & Chars74K dataset

Original Source URL http://wireilla.com/papers/ijesa/8418ijesa02.pdf https://wireilla.com/ijesa/current.html

Design and Implementation of IOT Based Smart Power Monitoring and Management System Using WSNS

Design and Implementation of IOT Based Smart Power Monitoring and Management System Using WSNS Iman Mohammed Nayyef and Anas Ali Husein Al-Nahrain University, Iraq ABSTRACT

We will design a system based on WSNs and IoT technologies to manage real-time power at buildings. This system comprises of: a wireless sensor network (sensing node and base station) and a smart home gateway. A sensing node is utilized wireless sensors to measure voltage and current; to calculate power consumption of connected appliances, transmitted wirelessly to a base station via Zigbee node. A base station is designed to receive all data transmitted from the sensing node and display it through GUI available at the personal computer, with the possibility of controlling ON and OFF appliances according to consumer requirements; All of these readings will be stored at database for analysis. In addition, a smart home gateway will connect the system with internet to allow consumers to continuous monitoring and remote control the appliances via a smartphone application. The benefit of this system, that the appliances control mechanism can be done in different ways (manually, automatically, and remotely). Various household appliances were tested to verify the accuracy of the electrical parameters that measured at system and compare them with practical measurement, found the average error ratio between them (0.3%) was in voltage, (1.5%) in current, and (1.8%) in power.

KEYWORDS

IoT, WSN, Zigbee, Power Management, Smartphone app.

Original Source URL http://wireilla.com/papers/ijesa/8418ijesa01.pdf https://wireilla.com/ijesa/current.html