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Sensors convert the physical parameter into equivalent measurable electrical quantity, which is given as input to controllers through an optional wireless communication device.
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Trajectories from manipulator control module are streamed to the controller through the supported communication protocol.
The measured output is directly transmitted to the event generator, whether the released signal can be sent out to the controller through the quantizer depends on the triggering condition.
Suppose that the system output is available and will be sampled before it is transmitted to the controller through network.
Furthermore, each subsystem may also be connected to a controller through the same network.
Users will also be able to connect up other controllers through the box's built-in Bluetooth.
The upstream controller generates instructions to the underlying device controllers through communication network.
Nevertheless, since NAND flash memory sensing latency is linearly proportional to the number of sensing quantization levels and the sensing results must be transferred to the memory controller through standard chip-to-chip links, a straightforward use of soft-decision ECC in NAND flash memory can result in significant memory read latency overhead.
Reinforcement learning and adaptive dynamic programming techniques are employed to design stochastic adaptive optimal controllers through online successive approximations of optimal solutions.
In this case, scenarios have been scaled down to a duration of 5 min, which is enough to drive the controller through its different operational stages and apply the robustness tests.
The local bus is responsible for connecting all FPGAs to the host controller through PCIex8.
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