Fast Convolutions of Packed Strings and Pattern Matching with Wildcards

2017 ◽  
Vol 28 (03) ◽  
pp. 289-307
Author(s):  
Meng Zhang

We give faster methods to compute discrete convolutions. We assume that all the inputs are packed, that is, strings are packed into words such that each word is packed with [Formula: see text] characters, where w is the length of a machine word and ∑ is the alphabet. The output of our methods is also packed, that is, each word of the output contains more than one element of the result. The approach is based on the word-level parallelism and the FFT. Given two strings with m and n ( n ≥ m ) characters that are packed into [Formula: see text] and [Formula: see text] words respectively, the convolution of them can be computed in [Formula: see text] time, where [Formula: see text] by the FFT. Experiments show that our method is three times faster than the convolution using the standard trick. We consider the problem of pattern matching with wildcards on packed strings. It finds all the occurrences of a pattern in a text, both of which may contain wildcards. By the convolution of packed strings, we present algorithms that are faster than the previous [Formula: see text]-time algorithm, where m is the length of the pattern and n the length of the text. The algorithm runs in [Formula: see text] time, where occ is the number of occurrences of the pattern in the input. Experiments show that the method is faster than the bit-parallel wildcard matching algorithm for long patterns.

Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan

Background: Agriculture is one of the most essential industry that fullfills people’s need and also plays an important role in economic evolution of the nation. However, there is a gap between the agriculture sector and the technological industry and the agriculture plants are mostly affected by diseases, such as the bacterial, fungus and viral diseases that lead to loss in crop yield. The affected parts of the plants need to be identified at the beginning stage to eliminate the huge loss in productivity. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Methods: In the present scenario, crop cultivation system depend on the farmers experience and the man power, but it consumes more time and increases error rate. To overcome this issue, the proposed system introduces the Double Line Clustering technique based disease identification system using the image processing and data mining methods. The introduced method analyze the Anthracnose, blight disease in grapes, tomato and cucumber. The leaf images are captured and the noise has been removed by non-local median filter and the segmentation is done by double line clustering method. The segmented part compared with diseased leaf using pattern matching algorithm. Conclusion: The result of the clustering algorithm achieved high accuracy, sensitivity, and specificity. The feature extraction is applied after the clustering process which produces minimum error rate.


Author(s):  
KUO-LIANG CHUNG

Given a pattern of length m and a text of length n, commonly m≪n, this paper presents a randomized parallel algorithm for pattern matching in O(n1/10) (=O(n1/10+(n−m)1/10)) time on a newly proposed n3/5×n2/5 modular meshconnected computers with multiple buses. Furthermore, the time bound of our parallel algorithm can be reduced to O(n1/11) if fewer processors are used.


2017 ◽  
Vol 80 ◽  
pp. 162-170 ◽  
Author(s):  
Muhammad Tahir ◽  
Muhammad Sardaraz ◽  
Ataul Aziz Ikram

2014 ◽  
Vol 571-572 ◽  
pp. 461-464
Author(s):  
Hai Yan Zhou

A fast and efficient matching algorithm is proposed to address the issue on multi-pattern matching of double-byte string, for example Chinese characters, which has major difference with single-byte string matching algorithm. The algorithm capitalizes on double cross link data list and two finite prefix automata to match a double-byte character, so as to solve the storage expansion problems in which the double-byte cross data link table results. The method requires less storage in comparison with double-byte cross data link table, and has the same order of magnitude in efficiency as a single-byte cross-link table approach.


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