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- 저자 : Asadullah Shah (검색결과 4 건)
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Risk Management Policy of Telecommunication and Engineering Laboratory
Abdul Salam Shah,Muhammad Fayaz,Asadullah Shah,Shahnawaz Shah 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.4
The Telecommunication laboratory plays an important role in carrying out research in the different fields like Telecommunication, Information Technology, Wireless Sensor Networks, Mobile Networks and many other fields. Every Engineering University has a setup of laboratories for students particularly for Ph.D. scholars to work on the performance analysis of different Telecommunication Networks including WLANs, 3G/4G, and Long Term Evolution (LTE). The laboratories help students to have hand on practice on the theoretical concepts they have learned during the teachings at the university. The technical subjects have a practical part also which boosts the knowledge of students and learning of new ideas. The Telecommunication and Engineering laboratories are equipped with different electronic equipment’s like digital trainers, simulators etc. and some additional supportive devices like computers, air conditioners, projectors, and large screens, with power backup facility that creates the perfect environment for experimentation. The setup of Telecommunication and Engineering laboratories cost huge amount, required to purchase equipment, and maintain the equipment. In any working environment risk factor is involved. To handle and avoid risks there must be risk management policy to tackle with accidents and other damages during working in the laboratory, may it be human or equipment at risk. In this paper, we have proposed a risk management policy for the Telecommunication and Engineering laboratories, which can be generalized for similar type of laboratories in engineering fields of studies.
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Testing Desktop Application : Police Station Information Management System
Abdul Salam Shah,Muhammad Fayaz,Asadullah Shah,Shahnawaz Shah 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.7
The police stations have adequate importance in the society to control the law and order situations of the country. In Pakistan, police stations manage criminal records and information manually. We have previously developed and improved a desktop application for the record keeping of the different registers of the police stations. The data of police stations is sensitive and that need to be handled within secured and fully functional software to avoid any unauthorized access. For the proper utilization of the newly developed software, it is necessary to test and analyze the system before deployment into the real environment. In this paper, we have performed the testing of an application. For this purpose, we have used Ranorex, automated testing tool for the functional and performance testing, and reported the results of test cases as pass or fail.
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An Offline Signature Verification Technique Using Pixels Intensity Levels
Abdul Salam Shah,M.N.A. Khan,Fazli Subhan,Muhammad Fayaz,Asadullah Shah 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.8
Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naïve Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naïve Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naïve Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naïve Bayes Tree and 88.12% (with forgeries).
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Muhammad Fayaz,Abdul Salam Shah,Fazli Wahid,Asadullah Shah 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.10
The analysis of MRI images is a manual process carried by experts which need to be automated to accurately classify the normal and abnormal images. We have proposed a reduced, three staged model having pre-processing, feature extraction and classification steps. In preprocessing the noise has been removed from grayscale images using a median filter, and then grayscale images have been converted to color (RGB) images. In feature extraction, red, green and blue channels from each channel of the RGB has been extracted because they are so much informative and easier to process. The first three color moments mean, variance, and skewness are calculated for each red, green and blue channel of images. The features extracted in the feature extraction stage are classified into normal and abnormal with K-Nearest Neighbors (k-NN). This method is applied to 100 images (70 normal, 30 abnormal). The proposed method gives 98.00% training and 95.00% test accuracy with datasets of normal images and 100% training and 90.00% test accuracy with abnormal images. The average computation time for each image was .06s.
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