Dr. K Poulose Jacob
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/2075
2024-02-07T17:52:01ZJerim-320: A New 320-Bit Hash Function Compared To Hash Functions With Parallel Branches
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4021
Jerim-320: A New 320-Bit Hash Function Compared To Hash Functions With Parallel Branches
Poulose Jacob,K; Sheena, Mathew
This paper describes JERIM-320, a new 320-bit hash function used for ensuring message integrity
and details a comparison with popular hash functions of similar design. JERIM-320 and FORK -256
operate on four parallel lines of message processing while RIPEMD-320 operates on two parallel
lines. Popular hash functions like MD5 and SHA-1 use serial successive iteration for designing
compression functions and hence are less secure. The parallel branches help JERIM-320 to achieve
higher level of security using multiple iterations and processing on the message blocks. The focus of
this work is to prove the ability of JERIM 320 in ensuring the integrity of messages to a higher
degree to suit the fast growing internet applications
International Journal of Computer Science and Applications,
Vol. 5, No. 4, pp 11 - 25, 2008
2008-01-01T00:00:00ZA novel Sigma–Delta based parallel analogue-to-residue converter
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/4020
A novel Sigma–Delta based parallel analogue-to-residue converter
Poulose Jacob,K; Shahana, T K; Babita, Jose R; Sreela Sasi
Animportant step in the residue number system(RNS) based signal processing is the
conversion of signal into residue domain. Many implementations of this conversion
have been proposed for various goals, and one of the implementations is by a direct
conversion from an analogue input. A novel approach for analogue-to-residue
conversion is proposed in this research using the most popular Sigma–Delta
analogue-to-digital converter (SD-ADC). In this approach, the front end is the same
as in traditional SD-ADC that uses Sigma–Delta (SD) modulator with appropriate
dynamic range, but the filtering is doneby a filter implemented usingRNSarithmetic.
Hence, the natural output of the filter is an RNS representation of the input signal.
The resolution, conversion speed, hardware complexity and cost of implementation
of the proposed SD based analogue-to-residue converter are compared with the
existing analogue-to-residue converters based on Nyquist rate ADCs
International Journal of Electronics
Vol. 96, No. 6, June 2009, 571–583
2009-05-01T00:00:00ZFEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/3915
FEATURE SELECTION AND COMPARISON OF TWO NAÏVE BAYES CLASSIFICATION METHODS IN THE CONTEXT OF SPAM FILTERING
Poulose Jacob,K; Supriya, M H; Liny, Varghese
Treating e-mail filtering as a binary text classification problem, researchers have applied several statistical learning
algorithms to email corpora with promising results. This paper examines the performance of a Naive Bayes classifier
using different approaches to feature selection and tokenization on different email corpora
International Journal of Computer Science and Communication Vol. 3, No. 1, January-June 2012, pp. 81-84
2012-06-01T00:00:00ZPERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION
https://dyuthi.cusat.ac.in:443/xmlui/handle/purl/3914
PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION
Poulose Jacob,K; Sonia, Sunny; David, Peter S
Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.
IJRET | APR 2013 Volume: 2 Issue: 4,590 - 597
2013-04-01T00:00:00Z