Momentum-Filtered Display Data Structures for Nonstationary Signals
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Solution Overview
Problem
Nonstationary processes are notoriously difficult to analyze due to their unstable statistical properties, necessitating a method to process and display meaningful conclusions.
Innovation Solution
An apparatus and method involving a processor and memory that apply a momentum processing module, autoregressive signal processing module, and comparator module to generate a display data structure from an input signal, utilizing user configuration data to filter and compare signals, creating a dynamic vector for analysis and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If nonstationary processes are analyzed directly, then the analysis can be performed quickly, but the statistical properties are unstable and conclusions are unreliable
Solution Approach 1:
The patent segments the signal processing into distinct modules: a momentum processing module that computes directional momentum signals, an autoregressive signal processing module that applies temporal filtering, and a comparator module that evaluates conditions. This segmentation allows each module to handle specific aspects of nonstationary process analysis, improving reliability while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediary processing steps between the raw input signal and the final display data structure. The momentum processing module acts as an intermediary by transforming the input signal into directional momentum signals, and the autoregressive signal processing module further processes these signals. These intermediaries stabilize the statistical properties and enable reliable conclusions about nonstationary processes.
2Reliability
If multiple signal processing modules are applied to stabilize nonstationary processes, then the statistical properties become stable and conclusions become reliable, but the processing time increases
Solution Approach 1:
The patent applies preliminary processing actions in a structured sequence: first computing directional momentum signals through the momentum processing module, then applying temporal filtering through the autoregressive signal processing module. By performing these stabilizing actions in advance and systematically, the patent prepares the data for reliable analysis while optimizing the sequence to minimize total processing time.
Solution Approach 2:
The patent employs dynamic processing where the autoregressive signal processing module adapts its filtering based on the characteristics of the input signal. The system dynamically adjusts the processing parameters to stabilize nonstationary processes, allowing the processing time to be optimized based on the specific characteristics of each signal while maintaining reliable statistical properties.
3Speed
If the display data structure is generated in real-time from dynamic vectors, then the system responds to changes immediately, but the computational load increases
Solution Approach 1:
The patent implements feedback mechanisms where the comparator module evaluates conditions based on the processed signals and feeds this information back to generate appropriate display data structures. The system continuously monitors the dynamic vectors and adjusts the display generation in response to changes, enabling real-time response while managing computational load through efficient feedback-driven processing.
Solution Approach 2:
The patent changes processing parameters dynamically based on the input signal characteristics and the state of the display data structure. By adjusting parameters such as filtering strength, momentum calculation methods, and comparator thresholds, the system optimizes its computational load while maintaining real-time response capability to changing conditions.
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.