Signal Identification via Vector Field Particle Flow
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data processing techniques struggle to identify meaningful patterns or signals in large, complex numerical sequences, especially when these signals are sparsely distributed or have a low signal-to-noise ratio, making it difficult to classify, categorize, or analyze data effectively.
Innovation Solution
A method involving conversion of numerical sequences into vector representations, generating a vector field, simulating particle flow through this field, and performing feature detection on static particle representations to identify signals, which can be used for classification, clustering, and indexing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data processing techniques are used to analyze numerical sequences, then data processing operations can be performed, but meaningful patterns or signals cannot be identified when signals are sparsely distributed or have low signal-to-noise ratio
Solution Approach 1:
The patent transforms a one-dimensional numerical sequence into a two-dimensional vector field representation. Each numerical value is converted to a vector with magnitude and direction, creating a multi-dimensional space where signals become more distinguishable from noise through geometric relationships and spatial patterns.
Solution Approach 2:
The patent introduces particles as intermediary elements that flow through the vector field. These particles interact with the vector structures to amplify and reveal underlying signals, acting as mediators between the raw numerical data and the detection process, making sparse or noisy signals more detectable.
2Loss of information
If traditional data analysis approaches are used, then processing can be performed, but meaningful patterns remain unidentified in complex numerical sequences
Solution Approach 1:
By converting numerical sequences into vector fields with multi-dimensional characteristics (magnitude, direction, spatial position), the patent preserves more information about the original data while creating a richer representation that highlights patterns invisible in one-dimensional analysis.
Solution Approach 2:
The patent creates a visual copy or representation of the numerical sequence in the form of a vector field with flowing particles. This visual copy transforms abstract numerical relationships into geometric and spatial patterns that can be more easily analyzed and interpreted, preserving the essential information in a more revealing form.
3Measurement precision
If vector field and particle flow simulation methods are used to identify signals, then pattern recognition is enhanced, but computational complexity increases
Solution Approach 1:
The patent divides the complex task of signal identification into distinct sequential steps: numerical sequence conversion to vector field, particle generation and flow simulation through the field, static representation capture at specific moments, and feature detection. This segmentation makes the complex process more manageable and implementable.
Solution Approach 2:
The patent performs preliminary transformations of the data into vector field representation before applying particle flow simulation and feature detection. This preliminary action prepares the data in a form that makes subsequent signal identification more effective, separating the complex transformation tasks from the detection tasks.
Data Source
AI summary
A computer implemented method for identifying a signal in a sequence of numerical values, the method including: converting the sequence of numerical values into a vector sequence by converting each value in the sequence to a normalized vector representation; generating a vector field comprising the vector sequence as a multi-dimensional data structure such that vectors in the vector sequence are plotted in sequence in the vector field; modeling a simulated flow of particles through the vector field such that a flow of each particle is influenced by vectors in the vector field; generating one or more static representations of the particles, each representation being generated at a particular point in time; and performing feature detection on each of the one or more static representations to identify features in the representation corresponding to the signal.