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A Fuzzy C-Medoids-Based CLARA Algorithm
for Fast Image Segmentation
-- Y V Ramana Rao and S Abirami
This paper proposes a clustering algorithm, Fuzzy CLARA, which combines Fuzzy C-Medoids algorithm (FCMDD) with Clustering LARge Applications (CLARA) algorithm with an application of the proposed algorithm for fast image segmentation. CLARA finds wide applications in different areas of data mining and is known to reduce time complexity while dealing with large datasets. The performance of the fuzzy CLARA algorithm is compared with fuzzy c-medoids algorithm and its linearized low complexity version. The efficiency of the clustering algorithms is measured using the clustering validity index Xie-Beni. The findings of the study show that the fuzzy CLARA algorithm gives better results with respect to both time complexity and Xie-Beni index compared to Fuzzy c-medoids algorithm and its linearized low complexity version.
© 2011 IUP. All Rights Reserved.
A Comparative Analysis of Huffman Coding
with Uniform Coding
-- Pinaki Mitra
In this paper, we analyze the compression ratio achieved by Huffman coding with that of uniform coding. For this, we first study the skewness property of the Huffman coding tree. We demonstrate that this tree will be completely skewed when the sorted frequency distribution of characters satisfies certain prefix properties. We also establish that among all the frequency distributions f1, f2, …, fn of a set of n characters that satisfy the prefix property, the average code length is maximum when the frequency distribution is a Fibonacci sequence. Then we estimate the average code length of Huffman coding for this frequency distribution.
© 2011 IUP. All Rights Reserved.
Design of Knowledge-Based Efficient
Speed Optimization Algorithm in Unplanned Traffic
-- Prasun Ghosal, Arijit Chakraborty and Sabyasachee Banerjee
Speed Optimization in an Unplanned Traffic (SOUT) is a very promising research problem. Searching for an efficient optimization algorithm to increase the degree of speed optimization and thereby increasing the traffic flow in an unplanned zone is a widely concerning issue. However, very few research works have been carried out on the optimization of the lane usage and speed optimization simultaneously. This paper presents a novel SOUT technique to solve the problem optimally using the knowledge derived by analyzing the speed of vehicles, which, in turn will act as a guide in designing lanes optimally to provide better optimized traffic. Also, base model estimates for different features like dimensional factors for the geometric design as well as different traffic control features are being adjusted by the accident factors. Knowledgebased analysis technique is applied to the proposed design and speed optimization plan. The experimental results and observations are quite encouraging.
© 2011 IUP. All Rights Reserved.
Selecting Plaintext in Rabin Cryptosystems
Using Padding Generated
by Pseudo-Random Bit Generators
-- Bh. Padma and D Chandravathi
As with all asymmetric cryptosystems (Stalling, 2003), the Rabin system uses both a public and a private key. The public key is necessary for later encoding and can be published, while the private key must be known only to the sending and receiving entities A and B. Each entity creates a public key and a corresponding private key. For public-key encryption, receiver B generates two large random and distinct prime numbers p, q such that p = q = 3 (mod 4), each having roughly the same size. Then B computes n = pq. B’s public key is n, B’s private key is (p, q).
© 2011 IUP. All Rights Reserved.
The Application of the Inclusion-Exclusion Principle
in Learning Monotonic Boolean Functions
-- Christopher Gaffney and Thomas Quint
In this paper, we consider the inference problem for monotone Boolean structure functions (for example, Torvik and Triantaphyllou, 2002 and 2005; or Judson et al., 2005). We follow Judson’s algorithm (in Judson, 1999; or Judson et al., 2005), except with two possible changes. First, when choosing a vector to test, we consider simply evaluating the “value” of a given number of random vectors (instead of using Judson’s “neighbor” algorithm to find test vectors). Second, we consider a new way of calculating the value of a vector, which makes use of the inclusionexclusion principle from combinatorics. Via testing on some 10-component systems, we find that the “random” approach is better than the “neighbor” approach, and that the inclusionexclusion method is an improvement whenever the size of the boundary of the “unknown vector set” is small.
© 2011 IUP. All Rights Reserved.
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