Display Data Structure Generation with Momentum and Autoregressive Filtering
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Solution Overview
Problem
Nonstationary processes are difficult to analyze due to their unstable statistical properties, making it challenging to draw meaningful conclusions and display relevant information effectively.
Innovation Solution
An apparatus and method that utilize a processor to apply momentum and autoregressive signal processing modules to generate a display data structure by classifying filtered momentum signals using time-series sequence classification models, mapping them to dynamic vectors, and transmitting this data to a remote device for display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nonstationary processes are processed directly, then the analysis can be performed quickly, but the statistical properties are unstable making conclusions unreliable
Solution Approach 1:
The processing system is divided into distinct modules: momentum processing module, autoregressive signal processing module, and classification models. Each module handles a specific aspect of the nonstationary process analysis, allowing the system to manage complexity through functional decomposition while improving reliability through specialized processing stages.
Solution Approach 2:
The system applies momentum processing and autoregressive filtering before classification to pre-process the nonstationary signal. This preliminary action stabilizes the statistical properties and extracts meaningful features, making subsequent analysis more reliable without requiring the entire system to be overly complex.
2Stability of the object's composition
If multiple processing modules are applied to stabilize the signal, then the statistical properties become stable for analysis, but the processing time increases
Solution Approach 1:
The system applies a limited number of processing stages (momentum processing followed by autoregressive filtering) rather than exhaustive processing. This partial action is sufficient to stabilize the statistical properties for meaningful analysis while avoiding unnecessary processing time that would result from applying more extensive transformations.
Solution Approach 2:
The processing modules transform the signal by changing its parameters - the momentum processing module changes the temporal derivatives, and the autoregressive module changes the statistical characteristics. These parameter changes stabilize the signal properties efficiently without requiring excessive processing time.
3Speed
If the processed data is displayed immediately, then the information is available in real-time, but the data may not be sufficiently filtered or classified
Solution Approach 1:
The processing pipeline operates continuously, with the momentum processing module and autoregressive filtering applied in sequence without interruption. This continuous processing ensures that data is stabilized and classified in real-time, maintaining both speed of availability and measurement precision through uninterrupted transformation.
Solution Approach 2:
The classification models act as intermediaries between the raw nonstationary data and the final displayed results. These intermediaries process the data through momentum and autoregressive transformations, ensuring precision is achieved before the data is made available for display, thus reconciling speed and precision requirements.
Data Source
AI summary
An apparatus and method for an apparatus for generating a display data structure from an input signal. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive an input signal, apply a momentum processing module to the input signal, receive at least one directional momentum signal for the at least a time series from the momentum processing module, apply an autoregressive signal processing module to the at least one directional momentum signal to determine at least one filtered momentum, generate a display data structure using the at least one filtered momentum signal and a plurality of threshold values, and transmit the display data structure to a remote device, wherein the display data structure is configured to cause the remote device to display the dynamic vector.