Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine (SVM). Whilst SVM is optimized for direct classifications between two classes, the KNN is best for multi-class classifications. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The differences in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, a higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. The best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset. It shows that different input dataset may behave differently even by using the same machine learning approach in its biometric recognition process

A review on diseases manifestation by ocular diseases using computer aided diagnosis (CAD)

The use of eye for diagnosis for detecting the disease has been used long time ago. However, for conventional medical practitioners this procedure are used to detect diseases that cause vision problems. This method is widely used by practitioners of alternative medicine that uses the eyes to detect the presence of disease, such as iridology practitioners. In this paper we study the method adopted by the researchers based on conventional and alternative medical practitioners to detect the presence of disease using a computer-aided diagnosis (CAD) or automatically

Social Networking as Support Tool for Online Teaching and Learning Factors and Contributions

Creating a good and stylish online learning for education is always bringing a good response to student especially for long-distance learning. The outcome is clear; which is to expand the capability of online education for all people around the globe by giving them a chance to study wherever they live and work as long they have good Internet access. Immersive learning as we all know consisting of the virtual reality aspect that bring educator and learner closer without having them to leave their place in order to gain knowledge and further their study. This paper studied the requirement to perform immersive learning while characterizing each challenge that we must face to perform a good online learning. We also numbered few technologies that could benefits the online learning either through website or online video conferencing

Machine vision based height measuring system

This paper presents a machine vision based height measuring system. The system will measure the height of a product based on the input from a webcam. The input image from the webcam will then be processed using image processing and then calculated to give the height of the product. In the market, there are many products that need to be measured whether the length or the height. These products also have different size and shape. There are several problems when using manual method for measuring such as man power will be needed at the station, longer time needed for measuring the product and the measurement may not be so accurate. This system aims to reduce these problems by developing a measuring system based on vision system

Design of Protection Relay Coordination Settings and Connecting to PLC Siemens S7-1500 For Load Shadding and HMI Display at PT. Semen Padang

There are some vital loads and must not extinguished on the production activities of PT. Semen Padang cause the operational costs to rerun are very large. To solve this problem, it is necessary to evaluate the coordination settings of protection relays and automatic control systems to supporting the production process of PT. Semen Padang so production efficiency can be maintained. The relay settings to be evaluated are inverse time and instantaneous overcurrent relays by coordinating the main relay, backup relay 1 and backup relay 2 from downstream to upstream load feeder. The designed control system is an automatic load shedding and HMI display that can visualizing the system in realtime using the PLC Siemens S7-1500 with simulating on the PLCSimulator of the TIA Portal 15.1 software. Based on the calculations carried out, there are significant differences in the relay settings from the calculation results with the existing state. the relays installed in the system do not coordinate properly in the existing state, while after the calculation of the coordination relays the relays coordinate well. From the design of the control system, an automatic load shedding program and the display of the HMI system in real time were produced

Image Histogram: Preliminary Findings of Anti-Spoofing Mechanism for Hand Biometrics Acquisition

Biometrics data are prone to spoofing activities especially on its sensor levels where fake biometrics data can be generated to imitate genuine biometrics data. Fake biometrics are false biometrics data that resemble genuine biometrics characteristics. If fake biometrics is accepted by a biometrics system, the possibility of personal information and data to be stolen is high. The consequences lie in the unwanted access, and the public may become insecure to use biometrics as an authentication tool. Biometrics acquisition process with an added detection mechanism can help distinguish between genuine and fake biometrics data. It is possible by the use of near-infrared (NIR) light during acquisition process because the interaction between NIR light with human skin and fake biometrics are different; due to the living trait property possessed by a human. This paper shares preliminary findings of image histogram for both genuine and fake biometrics images acquired by NIR illumination. Observation on the image histogram reveals that there are differences to the image properties that can be used to distinguish the genuine and fake biometrics data. The approach can be extended to its usage as a detection mechanism for other biometrics data as well. The main principle lies in the difference of image response between genuine and fake biometrics data acquired by the NIR illumination

TCP Performance And Throughput Fairness Optimization In A Multi-Hop Pipeline Network

Node starvation wireless sensor network (WSN) is a critical factor that affects the overall performance in a typical multi-hop linear network especially in an extensive scale network. The unfairness of sharing network resources with all source nodes in a multi-hop linear network amplifies the node starvation that often results in passive nodes in a network. This factor becomes critical with the increasing network density, aggressive data transfer, single destination node and inadequate data scheduling. This paper highlights the Delayed acknowledgement timeout for flat one-tier throughput critical application model (DAT-FTCAM) a mathematical fairness model that ensure maximum throughput fairness for pipeline network scenario. The DAT-FTCAM enables the users to calculate the maximum delayed acknowledgement timeout for transmission control protocol (TCP) proportional to the travel time or difference between a source and a destination node. The implementation of DAT-FTCAM technique with modified TCP parameters on NS2 has revealed a network fairness index of above 0.99 with optimum network performance in a scalable pipeline network. The DAT-FTCAM decreases data packet collision and eliminates passive nodes in a pipeline network with optimum throughput fairnes

Classification of eye abnormality using statistical parameters in texture features of corneal arcus image

The corneal arcus (CA), is the white-gray sediments, exist within the iris-limbus like a circle ring, caused by the occurrence of lipid disorder, in the bloodstream. This sign shows, the indication to diseases such as the coronary heart disease, diabetes, and hypertension. This paper demonstrates the classification of the CA as an indicator of hyperlipidemia. The experiment, uses two sets of sample data, consisting of the normal and abnormal eyes (i.e., CA), for classifies each group. The step for this classification, begin with the normalization of the eye images (as part of pre-processing), to achieve the region of interest (ROI). The next process is to extract the image texture using the grey level co-occurrence matrix (GLCM) technique, and calculate the extraction of the image texture using the statistical method. These features, then will be fed into the classifier, as the input for several processes, namely as the data training, data testing and validation data. In these experiments, we have obtained the excellent result using the proposed framework. This proves that, by using a Bayesian regularization (BR) classifier, the results of this classification given by the sensitivity (94%), specificity (100%), and accuracy (97.78%). Applications/Improvements: Based on the results obtained, the proposed system is successfully to classify the images with the CA signs. This show that, this proposed method can be applied to identify the presence of the hypercholesterolemia in a non-invasive test, to classify and detect the image of the CA

Palm vein pattern visual interpretation using laplacian and frangi-based filter

Detection of palm vein pattern through image processing techniques is an open problem as performance of each technique is closely related to the sample image gathered for the processing. The detected palm vein pattern is useful for further analysis in biometrics application and medical purpose. This paper aims to investigate the application of Laplacian filter and Frangi-based filter in detecting vein pattern contained in a near infrared illuminated palm image. Both filtering techniques are applied independently to two palm image databases to compare their performance in translating vein pattern in the image visually. Through empirical study, it is observed that Laplacian filter can translate the vein pattern in the image effectively. But pre-processings involved before the application of Laplacian filter need to be performed to accurately translate the vein pattern. The implementation of Frangi-based filter, while simplifying the detection process without the need of extra pre-processing, resulted in only certain vein pattern detected. Using pixel-by-pixel objective assessment, the rate for Laplacian filter in detecting vein pattern are generally more than 85% compared to Frangi-based filter; where it ranges from 60% to 100%