3 results about "Planar network" patented technology

A method of pipeline contributor selection, a method for pipeline contributor selection assistance, and related apparatuses are provided. The method of pipeline contributor selection includes sending a candidate data pipeline topology map to a computing resource agent, wherein the candidate data pipeline topology map indicates candidate data pipeline contributors to support data pipeline operation according to data plane network data service request; receiving from the computing resource agent one or more recommended data pipeline paths that are derived based on computing resource consumption and / or performance information associated with the candidate data pipeline contributors; and based on the one or more recommended data pipeline paths, determining a group of data pipeline contributors to be selected for the data pipeline operation. This can facilitate the DPAC to select suitable data pipeline contributors.

The invention relates to an HFSS rapid modeling method based on an AutoCAD plane feeder network. The HFSS rapid modeling method comprises the following steps: (1) designing a CAD plane feeder network graph; (2) importing the HFSS into a CAD plane network; (3) modeling according to the planar network; and (4) model processing and excitation adding. The invention constructs an automatic modeling method system which can be popularized and applied to radio frequency multilayer boards with different feeder line structures and different layer numbers. Through a generalized logic rule and a script automation process, a traditional manual drawing conversion process is changed into a standardized and modularized operation process, the research and development efficiency is greatly improved, the human error risk is reduced, and the method is particularly suitable for a small-batch rapid iteration development scene of a radio frequency communication product.

This invention discloses a recurrent neural network physical architecture based on artificial surface plasmon supercells, relating to the interdisciplinary fields of metamaterials, surface plasmons, and brain-like computing. The architecture consists of multiple identical planar network layers stacked sequentially in the vertical direction. Each layer includes a dielectric substrate (1), an SSPP supercell array (2), a bi-line divider (3), and a combiner (4). The SSPP supercell array is connected to inter-layer feedback links via vias. Its output is divided into an output path and a feedback path by a bi-line divider. The feedback path integrates an RF amplifier (5) for gain compensation and transmits the signal across layers to the combiner in the next layer for vector superposition with the new input signal. This architecture integrates temporal feature extraction, nonlinear modulation, and recursive computation, possessing ultra-high-speed parallel processing capabilities, and can effectively realize deep fusion and associative decision-making of multimodal information based on the nonlinear coherence effect of SSPP waves.