TOMAS Data Transmission Prioritizing Streams by Importance
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Solution Overview
Problem
Conventional data transmission systems are inefficient due to the lack of consideration for the importance of multimedia data components, telecommunication media profiles, system profiles, and user preferences, leading to suboptimal error protection and transmission speed.
Innovation Solution
The TOMAS method prioritizes data streams based on their importance, dynamically reorders them according to the telecommunication media profile, and uses a fast signal processing algorithm to multiplex data streams, ensuring that more critical components are transmitted over less error-prone subbands, thereby matching user preferences with communication system and media profiles in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data streams are transmitted without prioritization based on importance, then transmission simplicity is maintained, but data communication efficiency deteriorates
Solution Approach 1:
The patent applies local quality by differentiating transmission priorities for different data streams based on their importance to the application. Critical data streams receive higher priority transmission resources and error protection, while less critical streams use standard transmission. This resolves the contradiction by optimizing efficiency locally for each data stream type rather than applying uniform treatment to all data.
2Reliability
If error protection is uniformly applied to all data streams, then implementation simplicity is maintained, but transmission reliability deteriorates for critical data
Solution Approach 1:
The patent implements local quality by applying differentiated error protection mechanisms to different data streams based on their criticality. High-priority data streams receive enhanced error protection and redundancy, while low-priority streams use standard protection. This resolves the contradiction by tailoring reliability measures to the specific needs of each data stream rather than applying uniform protection to all.
3Productivity
If data transmission does not consider telecommunication media profile, then system simplicity is maintained, but transmission efficiency deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-characterizing the telecommunication media profile (bandwidth, error rates, latency) before data transmission begins. This profile information is stored and used to optimize data stream allocation and prioritization decisions. By performing this analysis in advance, the system resolves the contradiction by having transmission optimization ready without adding real-time complexity.
4Productivity
If data streams are not dynamically reordered according to media profile, then processing simplicity is maintained, but data communication efficiency deteriorates
Solution Approach 1:
The patent implements dynamics by dynamically reordering data streams based on real-time media profile conditions and stream importance. When channel conditions change or different data types are transmitted, the prioritization and allocation of data streams to communication channels is adjusted accordingly. This resolves the contradiction by making the system adaptive to changing conditions rather than static, improving efficiency without requiring complex real-time processing.
Data Source
AI summary
A method and apparatus for Data Transmission Oriented on the Object, Communication Media, Agents, and State of Communication Systems (TOMAS) is disclosed. TOMAS addresses an issue of efficiency of conventional data communication systems. The superior efficiency of TOMAS is achieved by: 1) matching the requirements of agents with capabilities of the communication systems and the communication media using the features of data objects; 2) monitoring of time-varying characteristics of all components, such as a charge of batteries and a status of all hardware, firmware and software components; 3) using an information about time-invariant characteristics of the systems, such as devices screen sizes, employed operational systems (OS), etc.; 4) using a flexible system architecture; and 5) using a fast signal processing algorithm described in [12] and [9] at the stage of data object analysis-syntesis and the codestream multiplexing-demultiplexing.