Iris Recognition System Using Support Vector Machines

In recent years, with the increasing demands of security in our networked society, biometric systems for user verification are becoming more popular. Iris recognition system is a new technology for user verification. In this paper, the CASIA iris database is used for individual user’s verification by using support vector machines (SVMs) which based on the analysis of iris code as feature extraction is discussed. This feature is then used to recognize authentic users and to reject impostors. Support Vector Machines (SVMs) technique was used for the classification process. The proposed method is evaluated based upon False Rejection Rate (FRR) and False Acceptance Rate (FAR) and the experimental result show that this technique produces good performance

Design and Evaluation of a Pressure Based Typing Biometric Authentication System

The design and preliminary evaluation of a pressure sensor-based typing biometrics authentication system (PBAS) is discussed in this paper. This involves the integration of pressure sensors, signal processing circuit, and data acquisition devices to generate waveforms, which when concatenated, produce a pattern for the typed password. The system generates two templates for typed passwords. First template is for the force applied on each password key pressed. The second template is for latency of the password keys. These templates are analyzed using two classifiers. Autoregressive (AR) classifier is used to authenticate the pressure template. Latency classifier is used to authenticate the latency template. Authentication is complete by matching the results of these classifiers concurrently. The proposed system has been implemented by constructing users’ database patterns which are later matched to the biometric patterns entered by each user, thereby enabling the systemto accept or reject the user. Experiments have been conducted to test the performance of the overall PBAS system and results obtained showed that this proposed system is reliable with many potential applications for computer security

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

Evaluating the effect of voice activity detection in isolated Yoruba word recognition system

This paper discusses and evaluates the effect of voice Activity Detection (VAD) in an isolated Yoruba word recognition system (IYWRS). The word database used in this paper are collected from 22 speakers by repeating the numbers 1 to 9 three times each. A hybrid configuration of Mel-Frequency Cepstral coefficient (MFCC) and Linear Predictive Coding (LPC) have been used to extract the features of the speech samples. Artificial Neural Network algorithms are then used to classify these features. An overall accuracy of about 60% has been achieved from the two proposed feature extraction methods

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

The Rise of Internet of Things and Big Data on the Cloud: Challenges and Future Trends

Huge growth in the scale of data generated through cloud computing has been led to Internet of Things (IoT). Fog computing has been recently adopted to improve some features of cloud computing and makes cloud computing more attractive to users. Furthermore, it comes to improve some parameters such as latency, security and load network. The combination of cloud and fog computing is seen a new progress in distributed computing and the appropriate platform for the data. Fog computing is defined as a new paradigm that works at the edge of the network to improve the quality of the network. In this paper, the use of fog computing in cloud computing is reviewed in this work. The characteristics, architectures and discussions and the relationship is further elaborated. Additionality, recommendation for further research is discussed

Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System

This paper presents an intelligent control approach for air handling unit (AHU) which is an integral part of heat, ventilation, and air conditioning (HVAC) system. In the past years various control design for HVAC have been proposed as this system remarkably consumes very high energy. But most of the proposed designs were focused on the control flow of heat-transfer medium such as chilled or heated water while the importance of the efficient mixture of outdoor and indoor enthalpies is sometimes ignored. These enthalpies invariably determine the best strategy to overcome thermal load in a controlled environment to satisfy human comfort, hence a control design strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor enthalpies by a proper control of AHU dampers and fans. This approach requires sensors to measure temperature and relative humidity of both outdoor and indoor environments. However, unpredictable level of disturbances coming from many sources including heat generated by occupants, electrical items and air leaking and the continuous changes of outdoor enthalpy makes it difficult to model the process. Consequently, conventional controllers are not suitable, hence the use of fuzzy logic controller (FLC) is proposed in this paper. This proposed controller operates in a master and slave control loop so as to control the AHU dampers and fans with adjustable output membership function whilst at the same time a scaling-factor method is used to drive the master operation. To implement the proposed system, a small scale prototype has been designed and fabricated. This prototype is an AHU model which consists of ductwork, temperature and humidity sensors, dampers, air cooling and heating systems. A small box is used as a conditioning space in which a room temperature is measured. The control algorithm is programmed using National Instrument (NI) LabVIEW and executed using NI FieldPoint. Experimental results reveal that proper control of AHU dampers and fans is an effective and practical means to satisfy human comfort with minimum energy consumption. Keywords: Human comfort, Intelligent control, Air handling unit, Adaptive fuzzy logic contro

Transform Based Approach for ECG Period Normalization

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