Continuous Data Compression Using Adjacent-Value Differencing
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Solution Overview
Problem
The large volume of continuous data from non-invasive blood sugar monitoring using near-infrared spectrometers requires efficient storage and management, as the absorbance data varies continuously with wavelength changes, necessitating a method for effective compression.
Innovation Solution
An apparatus and method that calculate differences between adjacent values in original continuous data, generate new data based on these differences, remove signs and decimal points, and apply lossless or lossy compression algorithms, such as RLE, dictionary-based encoding, or Huffman coding, to compress the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous data is stored in its original form, then measurement precision is maintained, but data volume becomes excessively large requiring efficient compression
Solution Approach 1:
The patent segments continuous data into discrete difference values between adjacent data points. Instead of storing the entire continuous dataset, it divides the data into individual difference components (Δy) that can be stored separately, reducing overall storage requirements while preserving the ability to reconstruct the original data through cumulative summation.
Solution Approach 2:
The patent transforms the data representation from absolute continuous values to relative difference values. By changing the parameter from storing y values to storing Δy (difference) values, the system achieves more efficient compression while maintaining measurement precision through reversible transformation.
2Quantity of substance
If data compression is applied to reduce storage requirements, then data volume decreases, but data processing complexity increases
Solution Approach 1:
The patent applies preliminary differencing transformation to the data before storage. By pre-calculating and storing the difference values (Δy) instead of original values, the system simplifies subsequent processing operations. The differencing is performed once during data preparation, making later retrieval and reconstruction operations more efficient.
Data Source
AI summary
Disclosed are an apparatus and method for compressing continuous data. The apparatus for compressing continuous data may include a data generator configured to calculate differences between adjacent values in original continuous data and generate data based on the calculated differences.