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karim Faez
Amirkabir University of Technology
Computer Science & IT Electrical & Electronics Engineering

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Human Identification Based on Electrocardiogram and Palmprint Sara Zokaee; Karim Faez
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 2: April 2012
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (104.073 KB)

Abstract

In this paper, a new approach in human identification is investigated. For this purpose, we fused ECG and Palm print biometrics to achieve a multimodal biometric system. In the proposed system for fusing biometrics, we used MFCC approach in order to extract features of ECG biometric and PCA to extract features of Palm print. The features undergo a KNN classification. The performance of the algorithm is evaluated against the standard MIT-BIH and POLYU databases. Moreover, in order to achieve more realistic and reliable results, we gathered Holter ECG recordings acquired from 50 male and female subjects in age between 18 and 54. The numerical results indicated that the algorithm achieved 94.7% of the detection rate.DOI:http://dx.doi.org/10.11591/ijece.v2i2.292
A novel method for extracting and recognizing logos Arash Asefnezhad; Karim Faez
International Journal of Electrical and Computer Engineering (IJECE) Vol 2, No 5: October 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

Nowadays, the high volume of archival documents has made it exigent to store documents in electronic databases. A text logo represents the ownership of the text, and different texts can be categorized by it; for this reason, different methods have been presented for extracting and recognizing logos. The methods presented earlier, suffer problems such as, error of logo detection and recognition and slow speed. The proposed method of this study is composed of three sections: In the first section, the exact position of the logo can be identified by the pyramidal tree structure and horizontal and vertical analysis, and in the second section, the logo can be extracted through the algorithm of the boundary extension of feature rectangles. In the third section, after normalizing the size of the logo and eliminating the skew angle, for feature extraction, we first blocked the region encompassing the logo, and then we extract a particular feature by the parameter of the center of gravity of connected component each block. Finally, we use the KNN classification for the recognition of the logo.DOI:http://dx.doi.org/10.11591/ijece.v2i5.1292
A Flow-based Distributed Intrusion Detection System Using Mobile Agents Zahra Hakimi; Karim Faez; Morteza Barati
International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 6: December 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In recent decade, computer networks have grown in popularity. So, network security measures become highly critical to protect networks against different kind of cyber attacks. One of the security measures is using intrusion detection system (IDS). An IDS aims to detect behaviors that compromise network integrity, availability and confidentiality, by continuously capturing and analyzing events occurring in the network. A challenging problem for current IDSs is that their performance decreases in today’s high speed and large scale networks. A centralize IDS cannot process such high volume of data and there is a high possibility that it discards some attacks. In this paper we propose a flow-based distributed IDS using mobile agents (MA), which performs both data capturing and data analyzing in a distributed fashion. Our distributed IDS provides a framework for deployment of a scalable and high performance IDS, which by using a grouping mechanism and help of mobile agents, effective collaboration can be established between all network members. We simulated our method in NS2. Then we compared our proposed system with a general network-based IDS and a distributed IDS. Experimental results showed its superiority using several metrics of network load, detection rate and flow loss rate.DOI:http://dx.doi.org/10.11591/ijece.v3i6.3936
Co-Authors Ali Bazmi Arash Asefnezhad Babak Namazi Davod Aghlmandi Davod Aghlmandi Hassan Khotanlou Milad Shirmohammadi Minoo Hamissi Mojtaba Bahmanzadegan Jahromi Morteza Barati Razieh Akhavan Razieh Akhavan Reihane Saniei Sara Zokaee Zahra Hakimi