scholarly journals Hierarchy Restructuring for Hierarchical LVS Comparison

VLSI Design ◽  
1999 ◽  
Vol 10 (1) ◽  
pp. 117-125 ◽  
Author(s):  
Wonjong Kim ◽  
Hyunchul Shin
Keyword(s):  
Pattern Matching ◽  
Experimental Results ◽  
Memory Usage ◽  
Comparison System ◽  
Bottom Up ◽  
Rule Based ◽  
Matching Algorithm ◽  
Cpu Time ◽  
Layout Verification ◽  
Comparison Technique

A new hierarchical layout vs. schematic (LVS) comparison system for layout verification has been developed. The schematic hierarchy is restructured to remove ambiguities for consistent hierarchical matching. Then the circuit hierarchy is reconstructed from the layout netlist by using a modified SubGemini algorithm recursively in bottom-up fashion. For efficiency, simple gates are found by using a fast rule-based pattern matching algorithm during preprocessing. Experimental results show that our hierarchical netlist comparison technique is effective and efficient in CPU time and in memory usage, especially when the circuit is large and hierarchically structured.

An Improved BM Pattern Matching Algorithm in Intrusion Detection System

2011 ◽  
Vol 148-149 ◽  
pp. 1145-1148
Author(s):  
Chao Yin
Keyword(s):  
Intrusion Detection ◽  
Pattern Matching ◽  
Intrusion Detection System ◽  
Detection System ◽  
Experimental Results ◽  
Matching Algorithm ◽  
Bm Algorithm ◽  
Pattern Matching Algorithm ◽  
Improved Algorithm

An improved BM-algorithm in intrusion detection system was presented which can increase displacements using the next character of the substring has been matched in the main string and pattern string. Experimental results obtained by capturing network packets with the number of matches, the number of pattern moves, the number of character comparisons as the main indicators. Experimental results show that the number of matches after and before improved is equal, and the number of pattern moves decreased by about13.3% after improved, and the number of character comparisons decreased by about 15.1% after improved. This indicates that the improved algorithm improved the efficiency of pattern matching.

Research on TCAM Match Algorithm in High-Speed DPI System

2011 ◽  
Vol 110-116 ◽  
pp. 5090-5094
Author(s):  
Zheng Hu Chen ◽  
Ju Long Lan
Keyword(s):  
Theoretical Analysis ◽  
Pattern Matching ◽  
High Speed ◽  
Network Throughput ◽  
Experimental Results ◽  
Identification Accuracy ◽  
Matching Algorithm ◽  
Backbone Network ◽  
Match Algorithm ◽  
Service Identification

The backbone network throughput has climbed up to 40Gbps, it demands a high-speed pattern matching algorithm. In this paper, we apply Half byte matching and SRAM-based Expansion into the traditional TCAM lookup algorithm, to meet accuracy requirements. Theoretical analysis and experimental results show that, the HBS-TCAM algorithms can significantly improve the service identification accuracy, thus enhancing the performance of TCAM lookup system.

Double Line Clustering based Colour Image Segmentation Technique for Plant Disease Detection

2019 ◽  
Vol 15 (8) ◽  
pp. 769-776
Author(s):  
Kalaivani Subramani ◽  
Shantharajah Periyasamy ◽  
Padma Theagarajan
Keyword(s):  
Pattern Matching ◽  
Error Rate ◽  
Median Filter ◽  
Identification System ◽  
Double Line ◽  
Cultivation System ◽  
Matching Algorithm ◽  
Non Local ◽  
Mining Methods ◽  
Blight Disease

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.

A UMDA-Based Discretization Method for Continuous Attributes

2011 ◽  
Vol 403-408 ◽  
pp. 1834-1838
Author(s):  
Jing Zhao ◽  
Chong Zhao Han ◽  
Bin Wei ◽  
De Qiang Han
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Evolutionary Algorithms ◽  
Marginal Distribution ◽  
Convergence Speed ◽  
Fast Convergence ◽  
Experimental Results ◽  
Discretization Method ◽  
Bottom Up ◽  
Global Dynamic

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

An algorithm for rule-based layout pattern matching

2020 ◽  
Author(s):  
Sheng-Hao Wang ◽  
Yen-Jong Chen ◽  
Ting-Chi Wang ◽  
Oscar Chen
Keyword(s):  
Pattern Matching ◽  
Rule Based
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