HYBRID AES-DES BLOCK CIPHER: IMPLEMENTATION USING XILINX ISE 9.1I
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS AND ELECTRICAL ENGINEERING
Author(s): ANURHEA DUTTA , PRERNA BHARTI , SUREKHA K S , SWATI AGRAWAL
Abstract: In this era of information, need for protection of data is more pronounced than ever. Secure communication is necessary to protect sensitive information in military and government institutions as well as private individuals. Current encryption standards are used to encrypt and protect data not only during transmission but storage as well. Data Encryption Standard was introduced in early 1970s as a standard cryptographic algorithm to protect data. However, due to its short 56-bit key length, simple brute force attacks cracked it in less than 10 hrs. Another disadvantage was also the possibility of weak and semi weak keys. In the year 2000, Rijndael Encryption algorithm or AES was chosen by National Institite of Standards and Technology(NIST) to be adopted by the U.S. Government as the new Encryption standard to replace the outdated and easily crackable DES. The major advantage lay in the non-linearity of the key-schedule which eliminated the possibility of weak and semi weak keys. This
- Publication Date: 23-Jun-2012
- DOI: 10.15224/978-981-07-2683-6-101
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CLASSIFICATION OF PAPER-BASED ELECTROCARDIOGRAM
Published In: INTERNATIONAL CONFERENCE ON ADVANCES IN ELECTRONICS AND ELECTRICAL ENGINEERING
Author(s): CHUSAK THANAWATTANO , DUSIT THANAPATAY , GARN WUNGKOBKIAT
Abstract: A method for the automatic classification of paper-based electrocardiogram (ECG) ispresented.An automated classification system of digital ECG has been developing for a few years. However, in reality, ECG signal usually recorded on the paper which cannot be directly analyzed by the computer. To extract the feature of signal, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Hybrid Discriminant Analysis (HDA) have been used to perform in this issue. ECG shape form scanned image was detected by many image processing techniques. Example data was obtained from MIT-BIH database. This investigation uses Support Vector Machine (SVM) to create the classifier model. This experiment resulted in anaccuracy of 98.73%.
- Publication Date: 23-Jun-2012
- DOI: 10.15224/978-981-07-2683-6-102
- Views: 0
- Downloads: 0