CAN Message Sub-field Detection via Bit-field Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Structured time-series data with unknown sub-field positions hinders analysis due to varying arrangements of sub-fields, preventing effective identification and processing of data records.
Innovation Solution
A system and method that generate new time-series by subtracting or performing two's complement operations on input time-series instances to create bit-fields, which are then analyzed to identify fixed consecutive bit positions of sub-fields within a field, allowing for sub-field identification and border determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sub-field positions are not fixed or arrangements vary, then adaptability of data format is improved, but identification and analysis capability deteriorates
Solution Approach 1:
The system performs preliminary actions by generating new time-series data through subtraction operations and two's complement transformations before analysis. These preprocessing steps create standardized bit-field representations that reveal fixed consecutive bit positions, enabling subsequent identification of sub-field arrangements even when original positions vary.
Solution Approach 2:
The system changes parameters by applying mathematical transformations (subtraction, two's complement) to the time-series data. These parameter changes convert variable arrangements into fixed patterns, allowing the analysis system to identify consistent bit position relationships that were obscured in the original variable format.
2Adaptability or versatility
If many different arrangements of sub-fields exist, then versatility of data structure is improved, but analysis capability deteriorates
Solution Approach 1:
The system performs preliminary transformations by generating new time-series through subtraction and two's complement operations. This preprocessing consolidates multiple arrangement variations into standardized bit-field patterns, enabling efficient analysis of sub-field positions without manually handling each arrangement variant.
Solution Approach 2:
The system merges multiple time-series instances into a consolidated bit-field representation by performing bitwise operations. This combining process integrates information from various arrangements into a unified structure, revealing fixed bit position patterns that enable efficient analysis across all variants simultaneously.
3Adaptability or versatility
If sub-field positions are unknown and variable, then flexibility of communication protocol is improved, but measurement precision of data fields deteriorates
Solution Approach 1:
The system applies parameter changes through mathematical transformations (subtraction, two's complement) that convert variable position data into fixed-pattern representations. These transformations preserve the essential information while establishing consistent bit position relationships, enabling precise measurement of sub-field locations despite original variability.
Solution Approach 2:
The system performs preliminary preprocessing operations that standardize the data representation before analysis. By generating new time-series through subtraction and two's complement operations, the system creates a standardized format where fixed consecutive bit positions become identifiable, achieving measurement precision without sacrificing protocol flexibility.
Data Source
AI summary
A system for identifying one or more sub-fields within a field of a given field type, the field comprising a plurality of field bits, and each of the sub-fields comprising a distinct sub-group of the field bits having one or more fixed consecutive bit positions within each instance of a plurality of instances of the field, the system including a processing resource configured to: obtain one or more input time-series, each input time-series of the one or more input time-series including a plurality of input instances of the field; generate one or more new time-series, each new time-series of the generated new time-series being associated with at least one input time-series of the one or more input time-series and including a plurality of new instances of the field; generate a bit-field based on selected instances of the new instances in each new time-series of the generated new time-series; and analyze the bit-field to identify the fixed consecutive bit positions of at least one of the sub-fields within the field.