Security Features in Fingerprint Biometric System

Nowadays, embedded systems run in every setting all around the globe. Recent advances in technology have created many sophisticated applications rich with functionality we have never seen. Nonetheless, security and privacy were a common issue for these systems, whether or not sensitive data can be protected from malicious attacks. These concerns are justified on the grounds that the past of security breaches and the resulting consequences narrate horrific stories concerning embedded systems. The attacks are now evolving, becoming more complex with technological advancements. Therefore, a new way of implementing security in embedded systems must be pursued. This paper attempts to demonstrate the incorporation of security features in fingerprint biometric system in the requirements analysis phase, ensuring the same throughout the system life cycle of embedded systems based on case study. The comparison of various biometric technologies such as face, fingerprint, iris, palm print, hand geometry gait, signature, and keystroke is presented. The aim of this paper includes analyzing, decomposing and transforming the threats and counter-measures identified during the requirements analysis using the abuse case into more specific safety requirements or functions. Furthermore, we have shown that the incorporation of security features into the biometric fingerprint system by analyzing the requirements of the system and providing the main steps for the protection of the biometric system in this paper

Review of Environmental Wireless Sensor Networks System and Design

The paper presents the review of wireless system networks development for environmental application. An environmental problem such as climate change requires urgent attention. The use of embedded system and wireless sensors make monitoring possible for areas such as remote and harsh environment. Environment parameters such CO2 and other greenhouse gasses are monitored using the sensor attached to a wireless remote node from a different location and transmitted to the central unit for processing. It is important to use the right type of wireless technology since the remote application requires low power management and resistance to noise. There are several types of modules such as WI-FI, GPRS, Bluetooth, ZigBee and other wireless technology. The related work in this field will be reviewed and factors such as topology, power requirement, and good system design approach will be taken into account for the system review

The important role of system engineering for yacht engineering

Systems engineering has been an important field that many research centers keep developing and enhancing. This importance comes from the role that Systems Engineering has during the system's life cycle. This paper will address the important role of Yacht Engineering during the systems life cycle. A Yacht to be engineered to meet modern standards and user needs. It shall involve various engineering and design disciplines for successful development. As it is going to be a complex system, it will require system engineering principles to guide and fully engineer

Detection of arrhythmia from the analysis of ECG signal using artificial neural networks

Arrhythmia is a heart rhythm problem that could indicate a symptom of heart disease that often contributes to the increase in hospitalization in many developed countries. The patient of heart disease requires continuous monitoring and close attention to their vital sign such as the heart rate. There are many attempts to automate the detection of Arrhythmia from the Electrocardiogram (ECG) readings of patient. Nevertheless, the accuracy of some of these methods isnot satisfactory and prone to biased result due to inter-patient variations of ECG dataset.The purpose of this research addresses the arrhythmia classification problem from the ECG signal using Artificial Neural Network (ANN). First, we perform feature extraction on the ECG data which are the four features from RR intervals. The features are then transformed into a feature vector. Then we modelled sixteen different models of ANN where four different algorithms were used such as Bayesian Regularization (BR), Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Resilient Backpropagation (RP). The sixteen models are built withadifferent number of neurons in the hidden layer. We used the dataset from Massachusetts Institutes of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database for evaluating our models which are simulated in MATLAB. The results of the simulation were analyzedand the best model was compared with the previous work. The analysis of our research indicates that the ANN usingBayesian regularization withtwenty number of neurons in the hidden layer is the optimal model compared to other models with an overall accuracy of 83.1%. The Normal class Sensitivity was 97.4%, Specificity of 66.7% and Positive Predictive Value of 77.1%. The SVEB Sensitivity was 60% with Specificity of 86.9% and Positive Predictive Value of 42.9%. The VEB Sensitivity was 66.7% with Specificity of 88.7% and Positive Predictive Value of 66.7%. The comparison with other worksindicatesthat our model outperforms the previous work in terms of sensitivity and overall accuracy