Data Stream Compression Using Dependent Substream Removal
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Solution Overview
Problem
Current data compression techniques for real-time health monitoring systems, such as EEG and ECG, result in significant data loss or distortion, which are costly to reconstruct and inefficiently implemented on battery-operated devices, particularly due to their focus on higher abstraction layers and neglect of wireless channel characteristics.
Innovation Solution
An efficient data-specific compression method that leverages the inherent characteristics of EEG signals at the physical layer, using orthogonal frequency division multiplexing (OFDM) and Formal Concept Analysis to decompose data into streams, apply different compression thresholds, and remove dependent substreams, thereby reducing transmitted data without significant overhead and maintaining quality of service.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If current data compression techniques are applied at higher abstraction layers, then data transmission volume is reduced, but data loss and distortion increase significantly
Solution Approach 1:
The patent segments the data stream into multiple substreams based on wireless channel characteristics and signal-to-interference-plus-noise ratio (SINR) conditions. Each substream is processed independently with appropriate compression techniques, allowing selective compression that preserves critical information while reducing overall data volume. This segmentation enables the system to maintain data quality for important signals while aggressively compressing less critical data.
Solution Approach 2:
The patent applies different compression thresholds and techniques to different portions of the data stream based on local characteristics. Specifically, data is processed with varying compression aggressiveness depending on the SINR condition and channel quality of each substream. This local quality approach ensures that critical data portions maintain high fidelity while less critical portions undergo more aggressive compression, resolving the contradiction between compression ratio and data quality.
2Loss of information
If complex reconstruction techniques are used to analyze and recompose data streams, then data loss is reduced, but computational cost and processing complexity increase
Solution Approach 1:
The patent performs preliminary actions at the transmitter side by organizing data into substreams with known dependencies and characteristics before transmission. The formal concept analysis identifies relationships between substreams in advance, allowing the receiver to reconstruct missing data using simple lookup and combination operations rather than complex computational algorithms. This shifts the computational burden to the transmitter where resources are more abundant.
Solution Approach 2:
The patent creates redundant copies of critical data information through the formal concept framework, where dependent substreams contain encoded information that can reconstruct missing primary substreams. Instead of using complex reconstruction algorithms, the system uses pre-prepared copy relationships that enable simple recovery operations at the receiver, reducing computational complexity while maintaining data integrity.
3Reliability
If continuous or near-continuous sensing is performed for real-time health monitoring, then monitoring quality is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic action through the structured organization of data into time-based substreams with formal concept relationships. By processing and transmitting data in periodic substream units rather than continuous streams, the system reduces the instantaneous processing load and enables energy-efficient transmission schedules. The formal concept framework allows the system to determine which substreams are critical and require transmission, enabling selective periodic transmission that maintains monitoring quality while reducing overall energy consumption.
4Productivity
If data is compressed by removing redundant information, then transmission efficiency is improved, but reconstruction accuracy deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the transmitter analyzes the characteristics of the health monitoring data and adjusts compression parameters accordingly. The formal concept analysis provides feedback about the relationships between different data substreams, allowing the system to determine the appropriate level of compression for each substream while ensuring that critical information is preserved. This feedback loop maintains reconstruction accuracy by adapting compression aggressiveness to the actual data characteristics rather than applying fixed compression ratios.
Data Source
AI summary
We provide a method of compressing a data stream for transmission, including: generating a data sequence representing a received data stream, generating a plurality of data substreams, each comprising a portion of the data sequence, identifying a formal concept defining a dependency between a first one of the data substreams and one or more further ones of the data substreams that are dependent on the first data substream, removing those dependent data substreams from the plurality of data substreams, and transmitting the remaining data substreams, and a method of reconstructing a data stream at a receiver, including: receiving a received data sequence representing a received data stream, identifying that a substream has been removed from the data stream prior to transmission, identifying a formal concept definition for regenerating the removed substream based on an identified substream of the received data sequence, regenerating a data substream using the formal concept definition and the identified substream of the received data sequence, and adding the regenerated data substream to the received data sequence.