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In designing the CEPAR algorithm, the main themes were efficiently using the redundancy of data in the input database to reduce the amount of work and eliminating load imbalance to achieve high scalability.
Since 'strong' rather than 'weak' observability of the system ensures better performance of Kalman based estimators to develop a reliable soil moisture estimation algorithm, the main objective of this study is to discuss observability analysis of this nonlinear agro-hydrological system.
In this algorithm, the main object is the reduction of the absolute inner products that are above.
Although good quality images can be obtained using this algorithm, the main shortcoming is its computational inefficiency.
We are now in the position to give the convergence result of the algorithm, the main result of this section.
In our algorithm, the main complexity lies in the interference-plus-noise covariance matrix reconstruction problem and the nonconvex fractional QCQP problem.
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In the "Methods" section, we consider properties of l-mers in a metagenomic dataset and make several observations which form the intuition behind the algorithm, present the main algorithm, and extend the algorithm to handle arbitrary abundance ratios.
With PO-based algorithms, the main limitation is that the current assumption enounced above is valid only for objects larger than a few (e.g. three) wavelengths [2].
In Pair-wise algorithms, the main goal is to find the similar or closely related parts (motifs) of two sequences; whereas in MSA, the main goal is to find the consensus parts of more than two sequences.
In this section, we will design a sub-algorithm and the main algorithm, respectively.
We use classic PID algorithm as the main course control algorithm, and back-propagation neural network (BPNN) is also utilized to achieve more effective self-adaptive PID control.
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CEO of Professional Science Editing for Scientists @ prosciediting.com