The file contains two arrays
inputs: contains the values of the mechanical parameters of the suspension ordered as kwh ,ksus, kseat, csus, cseat, V
output: contains the corresponding value of the vibration transmitted by the vehicle suspension system
This dataset includes all the datum of our numerial simulations, which are generated from networks with 5-25 end-to-end paths.
In order to discriminate and mark audio signal segments which include normal human speech and discriminate segments which do not include speech (like silence, music and noise), Speech/Music Discrimination (SMD) systems are used. Using this definition, SMD systems can be considered as a specific or accurate type of speech activity detection system.
The metaheuristic optimization algorithms are relatively the new kinds of optimization algorithms which are widely used for difficult optimization problems in which the classic methods cannot be applied and are considered as known and very broad methods for crucial optimization problems. Here, a new metaheuristic optimization algorithm is presented for which the main idea is extracted from a kind of motion in physics and is expected to have better results compared to other optimization algorithms in this field to present a novel method for achieving a more desirable point.
This dataset is associated with the paper, Jackson & Hall 2016, which is open source, and can be found here: http://ieeexplore.ieee.org/document/7742994/
The DataPort Repository contains the data used primarily for generating Figure 1.
The class of registration methods proposed in the framework of Stokes Large Deformation
Diffeomorphic Metric Mapping is a particularly interesting family of physically
meaningful diffeomorphic registration methods.
Stokes-LDDMM methods are formulated as a conditioned variational problem,
where the different physical models are imposed using the associated partial differential equations
as hard constraints.
The most significant limitation of Stokes-LDDMM framework is its huge computational complexity.
This is the data competion hosted by the IEEE Machine Learning for Signal Processing (MLSP) Technical Committee as part of the 27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2017), Tokyo, Japan. This year the competion is based on a dataset kindly provided Petroleum Geo-Systems (PGS), on source separation for seismic data acquistion.
