<span lang="EN-US">The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second is responsible for executing the trained TinyML network. Two MCUs communicate to each other using the universal asynchronous receiver-transmitter (UART) protocol. The multi-tasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform.</span>
Ah.wocf-Presents are now results on designing a robust adaptive direct eontroller for a class of non-linear f i s t order systems, bssed on the use of dead zone in parsmeters' updale law. It i s rbown that the size of the dead zone does not depend on the upper bounds of lhe disturbances. However, in Ihe ideal case, when erogenous signal functions and the function represeats unmodeled dynamics of the system3 equal to zero, the proposed coneoller does not mean the convergence to zero of the tracking error. Computer simulation results show the effectiveness of the Keywords-AdaptiveConBol, Dead zone, Robustness, controller in dealing with the stated problems. Nonlinear System1.
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