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Random test generation was implemented on the base of these fault models.
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Two types of spectrometer have been implemented on the base instrument: One has a 4.096 GS/s sampling speed with 2048 frequency channels and the other one has a 2.048 GS/s sampling speed with 32,768 frequency channels.
Specifically, as shown in Fig. 1, the first-stage ensemble learning is implemented on the base ELM classifiers of the multi-channel bilinear features generated from B-MC-ELM-AE for each sound frame, and the second-stage ensemble learning is conducted on all the decisions of all frames to provide final SEC.
The proposed algorithm is implemented on the GPU based on the Nvidia CUDA libraries and subsequently, this paper will provide details of the automatic baseline selection process.
It has been implemented on the sample platform based on ARMv7 Cortex-M4 microcontroller.
Results are provided when the processor is implemented on an FPGA over the base field F2103.
The mRMR feature selection was implemented based on the code downloaded from http://penglab.janelia.org/proj/mRMR/, and the top 20 characteristic features were selected.
A per-frame rate control technique was implemented based on the channel condition and the decoder buffer occupancy.
The whole project was implemented based on the interfaces of robot operation system (ROS).
The practical experiment was implemented based on the localization system for indoor environment proposed by BluePass [16].
The numerical model was implemented based on the Coupled Eulerian-Lagrangian Model (CEL) built-in Abaqus/Explicit finite element software.
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