Validating method classifying digitally modulated signals

Posted by / 07-Nov-2020 06:31

Validating method classifying digitally modulated signals

Examples include large networks described by coupled line-discretized diffusion-reaction equations, biological systems, large-scale gas and plasma dynamics simulations, oil and gas reservoirs models, to mention a few. Images with high resolution (HR) are desired in most imaging applications.

EMAPS (Event-driven Multiscale Asynchronous Parallel Simulation) offers a different approach to time integration, derived from Discrete-Event Simulation. For example, HR medical images are critical in making a correct diagnosis; HR satellite imagery offer unprecedented detail in object detection and registration, and HR computer vision boosts the performance of pattern recognition.

As a result, the numerical model progresses in simulation time via asynchronous computation of discrete events (local updates) generated by the underlying physics in a “game of life” fashion. Hence, an increase in spatial resolution is not guaranteed simply by reducing the pixel size in the design of the image sensor.Our findings raise the intriguing possibility that alternating deep brain stimulation patterns may be superior to conventional constant stimulation for supporting gait control.In the second part, I will talk about gamma oscillations and how they are linked to movement inhibition and execution.Our work suggests that the temporal structure of firing activity in the cortico-basal ganglia network is critical for flexible motor control.Studying cortico-basal ganglia information routing principles with multi-site recordings could provide important insights for improving treatments of basal ganglia disorders.

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