Results 221 to 230 of about 357,721 (279)
QSP-Copilot: An AI-Augmented Platform for Accelerating Quantitative Systems Pharmacology Model Development. [PDF]
CPT Pharmacometrics Syst PharmacolSaini A, Farnoud A.
europepmc +1 more source
Probabilistic Isolation of Crystalline Inorganic Phases. [PDF]
J Chem Inf ModelRitchie D +5 more
europepmc +1 more source
HDA-YOLO: a hierarchical and densely-fused attention network for rice pest detection in complex agricultural environments. [PDF]
Front Plant SciYuan S, Duan Y, Su H, Zhou X, Hao Y.
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Fibonacci decision diagrams and spectral Fibonacci decision diagrams
Proceedings 30th IEEE International Symposium on Multiple-Valued Logic (ISMVL 2000), 2002The authors define the Fibonacci decision diagrams (FibDDs) permitting representation of functions defined in a number of points different from N=2/sup n/ by decision diagrams consisting of nodes with two outgoing edges. We show the relationships between the FibDDs and the contracted Fibonacci codes. Then, we define the Spectral Fibonacci DDs (FibSTDDs)
R.S. Stankovic +3 more
openaire +1 more source
Student Conference on Research and Development, 2003
TDD graph-based representation is actually a natural extension of the binary decision diagram (BDD) to the three-valued case. This paper describes a method of defining, analyzing, and implementing the Boolean function using a ternary decision diagram (TDD). This diagram representation enables us to evaluate a Boolean function.
null Sim Poh Ching, M.K. Suaidi
openaire +1 more source
TDD graph-based representation is actually a natural extension of the binary decision diagram (BDD) to the three-valued case. This paper describes a method of defining, analyzing, and implementing the Boolean function using a ternary decision diagram (TDD). This diagram representation enables us to evaluate a Boolean function.
null Sim Poh Ching, M.K. Suaidi
openaire +1 more source
IEEE Transactions on Computers, 1978
This paper describes a method for defining, analyzing, testing, and implementing large digital functions by means of a binary decision diagram. This diagram provides a complete, concise, "implementation-free" description of the digital functions involved.
openaire +2 more sources
This paper describes a method for defining, analyzing, testing, and implementing large digital functions by means of a binary decision diagram. This diagram provides a complete, concise, "implementation-free" description of the digital functions involved.
openaire +2 more sources
2014
Symbolic analysis traditionally suffers circuit size problems as the number of symbolic terms generated can grow exponentially with the circuit size. This problem has been partially mitigated by a graph-based approach, called determinant decision diagram (DDDs), where the symbolic terms are implicitly represented in a graph, which inspired by the ...
Guoyong Shi +2 more
openaire +1 more source
Symbolic analysis traditionally suffers circuit size problems as the number of symbolic terms generated can grow exponentially with the circuit size. This problem has been partially mitigated by a graph-based approach, called determinant decision diagram (DDDs), where the symbolic terms are implicitly represented in a graph, which inspired by the ...
Guoyong Shi +2 more
openaire +1 more source
Multi-Terminal Binary Decision Diagrams and Hybrid Decision Diagrams
1996Functions that map vectors with binary values into the integers are important for the design and verification of arithmetic circuits. We demonstrate how multi-terminal binary decision diagrams (MTBDDs) can be used to represent such functions concisely.
Edmund M. Clarke +2 more
openaire +1 more source
2016
Bounds on the optimal value are often indispensable for the practical solution of discrete optimization problems, as for example in branch-and-bound procedures. This chapter explores an alternative strategy of obtaining bounds through relaxed decision diagrams, which overapproximate both the feasible set and the objective function of the problem.
David Bergman +3 more
openaire +1 more source
Bounds on the optimal value are often indispensable for the practical solution of discrete optimization problems, as for example in branch-and-bound procedures. This chapter explores an alternative strategy of obtaining bounds through relaxed decision diagrams, which overapproximate both the feasible set and the objective function of the problem.
David Bergman +3 more
openaire +1 more source