A string-matching engine capable of inspecting multiple characters in parallel can multiply the throughput. However, the space required for implementing a matching engine that can process multiple characters in parallel generally grows exponentially with respect to the characters to be processed in parallel. Based on the Aho-Corasick algorithm (AC-algorithm), this work presents a novel multicharacter transition Nondeterministic Finite Automaton (NFA) approach, called multicharacter AC-NFA , to allow for the inspection of multiple characters in parallel. This approach first converts an AC-trie to an AC-NFA by allowing for the simultaneous activation of multiple states and then converts the AC-NFA to a k -character AC-NFA by an algorithm with concatenation operations and assistant transitions. Additionally, the alignment problem, which occurs while multiple characters are being inspected in parallel, is solved using assistant transitions. Moreover, a corresponding output is provided for each inspected character by introducing priority multiplexers to determine the final matching outputs during implementation of the multicharacter AC-NFA. Consequently, the number of derived k -character transitions grows linearly with respect to the number k . Furthermore, the derived multicharacter AC-NFA is implemented on FPGAs for evaluation. The resulting throughput grows approximately 14 times and the hardware cost grows about 18 times for 16-character AC-NFA implementation, as compared with that for 1-character AC-NFA implementation. The achievable throughput is 21.4Gbps for the 16-character AC-NFA implementation operating at a 167.36MHz clock.

Salient region detection is useful for several image-processing applications, such as adaptive compression, object recognition, image retrieval, filter design, and image retargeting. A novel method to determine the salient regions of images is proposed in this paper. The L₀ smoothing filter and principle component analysis (PCA) play important roles in our framework. The L₀ filter is extremely helpful in characterizing fundamental image constituents, i.e., salient edges, and can simultaneously diminish insignificant details, thus producing more accurate boundary information for background merging and boundary scoring. PCA can reduce computational complexity as well as attenuate noise and translation errors. A local-global contrast is then used to calculate the distinction. Finally, image segmentation is used to achieve full-resolution saliency maps. The proposed method is compared with other state-of-the-art saliency detection methods and shown to yield higher precision-recall rates and F-measures.

BACKGROUND: The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. RESULTS: There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. CONCLUSION: The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.