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A chain of participants.
A chain of transmission.
The proposed nonlinear observer possesses a state-dependent gain which is computed from the solution of a system of first-order singular partial differential equations, and in particular, consists of a chain of state observation algorithms that reconstruct the unmeasurable state vector at different delayed time-instants within the time-delay window introduced by the available output measurements.
It implements a chain of mathematical and statistical algorithms, provides graphical interfaces to help users analyze experimental data and visualize the results, and provides a third party independent experimental data management system.
The proposed observer is composed of a chain of observers with similar algorithm.
It requires users to implement iterative algorithm as a chain of jobs where the output from each job is used as input to the next job.
Our algorithm employs a chain of common substrings to narrow down the search.
In such a situation, our alignment approach will correctly align to and to since, for pairwise alignment, our algorithm returns a chain of fragments with maximum total score.
The posterior probability validating the final tree is estimated using a Monte Carlo Markov Chain algorithm establishing a chain of possible dendrograms, which randomly wanders the tree space by sampling dendrograms until an equilibrium distribution is reached.
Moreover, we analyze the algorithm efficiency for a chain of quantum dots, where the mean processing time exhibits a linear dependence with the number of quantum dots in the array.
The Laboratory's Pulse-to-Pulse Phase Diversity technique utilizes a specialized radar transmit waveform and a tailored chain of signal processing algorithms to filter out interference and eliminate range ambiguity.
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