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Among the existing MIMO detectors, the FSD [9] is an attractive scheme; it is highly suitable for hardware implementation due to its fixed complexity and parallel-computing-friendly features.
Such a controller design is very easy for hardware implementation due to its simple structure.
The hybrid algorithm is well suited for real-time implementation due to its block-by-block FFT methodology.
This makes Chu sequences a good choice for practical implementation due to its simple construction and flexibility in sequence lengths.
The first is a slotted ring architecture that is suitable for NoC implementation due to its simplicity and low cost.
Firstly, SAD-based methods are the most appropriate to real-time implementation due to its low complexity (only summations and absolute values calculations) [18].
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Moreover, it is also very computationally efficient and suited to digital implementations due to its use of the FFT.
F-zkNN performs worse than the rest of the implementations due to its resource-hungry propagation of the transformed dataset during the first stage.
The charge-pump (CP) mismatch current is a dominant source of static phase error and reference spur in the nano-meter CMOS PLL implementations due to its worsened channel length modulation effect.
This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy.
However it is hampered by incoherence and very poor implementation due to weak governance capacities and lack of financial resources.
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