Reaction Signal Determination in Information Processing Systems
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining reaction signals in information processing systems, such as those in magnetic resonance tomography, struggle to assign signals to specific locations and determine characteristic reaction parameters, leading to inaccurate evaluations due to averaging and limited spatial and temporal resolution.
Innovation Solution
A method involving the application of input signals to an information processing system, detection of output signals, formation of triple value sets, and determination of reaction signals as the maximum or minimum output signal values within a defined period, with optional use of approximation values and exponential functions to account for measurement errors and technical limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If averaging methods are used for evaluating measurement data, then the evaluation process is simplified, but the accuracy and ability to determine characteristic reaction parameters at specific locations is reduced
Solution Approach 1:
The patent extracts the maximum or minimum values from measurement data sets, separating these extreme values from the average calculation process. This allows the system to identify characteristic reaction parameters at specific locations without being diluted by averaging effects, thereby maintaining evaluation simplicity while improving measurement precision through selective data extraction.
Solution Approach 2:
The patent applies local quality analysis by determining reaction signals for specific locations within the sample rather than treating the entire sample uniformly. By identifying maximum or minimum output signals at particular spatial positions, the method provides location-specific characteristic parameters that preserve both analytical simplicity and local measurement accuracy.
2Loss of information
If the number of measurements is increased to capture dynamic changes, then the completeness of system observation is improved, but the measurement time and complexity increase
Solution Approach 1:
The patent extracts only the maximum or minimum values from each measurement cycle, eliminating the need to process and store all intermediate measurement data. This selective extraction captures the essential dynamic characteristics of the system while significantly reducing data processing time and complexity, thus maintaining complete system observation without proportional time loss.
Solution Approach 2:
The patent performs partial action by focusing measurement and evaluation only on the extreme values (maxima or minima) rather than all measurement points. This partial approach captures the critical dynamic changes in the system with fewer processed data points, reducing measurement and processing time while maintaining adequate observation completeness.
3Measurement precision
If spatial resolution is increased to assign signals to specific locations, then the detail of system characterization is improved, but the complexity of data processing and measurement setup increases
Solution Approach 1:
The patent extracts maximum or minimum output signal values from measurement data, associating these extreme values with specific spatial locations. This extraction method provides high spatial resolution for characterizing system structures without requiring complex data processing algorithms, as the extreme values naturally highlight location-specific characteristics.
Solution Approach 2:
The patent implements local quality analysis by determining reaction signals for specific locations within the sample. By focusing on maximum or minimum values at particular spatial positions, the method achieves detailed system characterization with simplified processing, as each location's extreme value independently reveals local structural properties.
Data Source
AI summary
A method and a device for determining a reaction signal for a selected location in an information processing system (2) in response to at least one input signal (XIn) over an application period T. The effect of an input signal (XIn) on the information processing system is determined by: a) applying input signal(s) (X) to the information processing system (2), b) detecting output signal(s) (Yout) associated with a selected location in the information processing system in response to the input signal(s) (XIn), c) forming a value set (XIn, YOut, Zt) for an output signal (YOut) at the selected location in response to an input signal (XIn) for a given time (Zt), and d) determining the reaction signal (YOutMax) for the selected location.