Design and development of intelligent fingerprint-based security system

Traditionally, user authentication is meant to provide an identification number or a password that is unique and well protected to assure the overall system security. This type of security system is very fragile in an area where a higher level of security system is required. Biometrics-based system offers a new and better approach to user authentication. Biometrics authentication is an automated method whereby an individual identity is confirmed by examining a unique physiological trait or behavioural characteristic, such as fingerprint, iris, or signature, since physiological traits have stable physical characteristics. The design and development of a fingerprint-based security system, comprising the scanner, interface system, Boltzmann machine neural network and access control system is discussed in this paper. The integration between the hardware and the software is completed by using Visual Basic 6 programming language. The results obtained both for the simulation studies and testing of the integrated system with real-life physical system have demonstrated the practicality of such system as well as its potential applications in many fields

Comparison between Binary Particles Swarm Optimization (BPSO) and Binary Artificial Bee Colony (BABC) for nonlinear autoregressive model structure selection of chaotic data

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Classification of visualization exudates fundus images results using support vector machine

This paper classifies the characteristics of normal and exudates fundus images by determine its accuracy for diagnostic purposes. Image normalization was performed on 149 fundus images (81 normal and 68 exudates) from MESSIDOR databases to standardize the colours in the fundus images. The OD removed fundus image and fundus image with the exudates areas removed. The SVM1 classifier was applied to 30 test fundus images to determine the best optimal parameter. The kernel function settings; linear, polynomial, quadratic and RBF have an effect on the classification results. For SVM1, the best parameter in classifying pixels is linear kernel function. The visualization results using CAC and radar chart are classified using ts accuracy. It has proven to discriminated exudates and non exudates pixels in fundus image using linear kernel function of SVM1 to diagnose DR.Keywords: Diabetic retinopathy (DR); Optic disc (OD); Support Vector Machine (SVM); AC); Radial Basis Function (RBF)

Detection of asphyxia in infants using deep learning Convolutional Neural Network (CNN) trained on Mel Frequency Cepstrum Coefficient (MFCC) features extracted from cry sounds

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Design an electronic mouse trap for agriculture area

This paper explains the issues on the increasing of mice in agriculture area. The electronic mousetrap is being improved as it can catch more than a mouse with maximum of five. The device has a LCD to display a message of “Awaiting Mouse” and “Mouse Trapped”. Then, the LED light will remain light up after the door has been closed. The LED were function as an alert to the owner to know either there is a mouse or not inside the trap. The LED will turn off once the mouse is release. In the project, the main part of the circuit consists of PIR sensors and LCD display. The coding is designed by using Arduino.Keywords: PIR sensor; Arduino; LCD; mousetrap; LE