This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are pr...This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are presented to guide VLSIcircuits to work under with fewer test vectors in order to reduce testing time and todevelop VLSI circuit designs. This framework utilizes two methods of partitioningPrimary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning todetermine number of test vectors in the circuit. The key role of partitioning is to identifyreconvergent fanout branch pairs and the optimal value of primary input node N andfanout F partitioning using I-PIFAN algorithm. The number of reconvergent fanout andits locations are critical for testing of VLSI circuits and design for testability. Hence, theirselection is crucial in order to optimize system performance and reliability. In the presentwork, the design constraints of the partitioned circuit considered for optimizationincludes critical path delay and test time. POCOFAN-POFRAME algorithm uses theparameters with optimal values of circuits maximum primary input cone size (N) andminimum fan-out value (F) to determine the number of test vectors, number of partitionsand its locations. The ISCAS’85 benchmark circuits have been successfully partitioned,the test results of C499 shows 45% reduction in the test vectors and the experimentalresults are compared with other partitioning methods, our algorithm makes fewer testvectors.展开更多
Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-req...Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant information.The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval outcomes.The prevailing procedures for IR consume considerable time,and they could not recover information proficiently.In this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated downsides.The proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user query.In the initial phase,a crop ontology is developed and evaluated by gathering crop information.In the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the database.In the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the database.The performance of the proposed system is measured and compared with that of existing techniques.Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.展开更多
文摘This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are presented to guide VLSIcircuits to work under with fewer test vectors in order to reduce testing time and todevelop VLSI circuit designs. This framework utilizes two methods of partitioningPrimary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning todetermine number of test vectors in the circuit. The key role of partitioning is to identifyreconvergent fanout branch pairs and the optimal value of primary input node N andfanout F partitioning using I-PIFAN algorithm. The number of reconvergent fanout andits locations are critical for testing of VLSI circuits and design for testability. Hence, theirselection is crucial in order to optimize system performance and reliability. In the presentwork, the design constraints of the partitioned circuit considered for optimizationincludes critical path delay and test time. POCOFAN-POFRAME algorithm uses theparameters with optimal values of circuits maximum primary input cone size (N) andminimum fan-out value (F) to determine the number of test vectors, number of partitionsand its locations. The ISCAS’85 benchmark circuits have been successfully partitioned,the test results of C499 shows 45% reduction in the test vectors and the experimentalresults are compared with other partitioning methods, our algorithm makes fewer testvectors.
文摘Ontology is the progression of interpreting the conceptions of the information domain for an assembly of handlers.Familiarizing ontology as information retrieval(IR)aids in augmenting the searching effects of user-required relevant information.The crux of conventional keyword matching-related IR utilizes advanced algorithms for recovering facts from the Internet,mapping the connection between keywords and information,and categorizing the retrieval outcomes.The prevailing procedures for IR consume considerable time,and they could not recover information proficiently.In this study,through applying a modified neuro-fuzzy algorithm(MNFA),the IR time is mitigated,and the retrieval accuracy is enhanced for trouncing the above-stated downsides.The proposed method encompasses three phases:i)development of a crop ontology,ii)implementation of the IR system,and iii)processing of user query.In the initial phase,a crop ontology is developed and evaluated by gathering crop information.In the next phase,a hash tree is constructed using closed frequent patterns(CFPs),and MNFA is used to train the database.In the last phase,for a specified user query,CFP is calculated,and similarity assessment results are retrieved using the database.The performance of the proposed system is measured and compared with that of existing techniques.Experimental results demonstrate that the proposed MNFA has an accuracy of 92.77% for simple queries and 91.45% for complex queries.
