- Country: Netherlands
- Summary: Machine Learning Techniques for Data Rate Reduction
- NLT Ref No: 101320428
- Deadline: 02 Sep 2024
- Financier: Agency for the Cooperation of Energy Regulators (ACER)
- Purchaser Ownership: Government
- Tender Value: Refer Document
- Notice Type: Tender
- Document Ref. No.: 1-12223
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- Description:
- Tenders are invited for Machine Learning Techniques for Data Rate Reduction (Artes at 7b.079) The objective of this activity is to design, implement and test Machine Learning (ML) and Artificial Intelligence (AI) based data rate reduction techniques for ground terminals. A testbed will be developed to assess performance savings in terms of throughput, complexity and power consumption.Targeted Improvements: Decrease the transmitted data rate by at least 40% for specific applicationsDescription: Many types of data transmissions and reception require a high bandwidth occupancy and they can be very demanding in terms of decoding and consumed power at the terminal. Machine Learning (ML)/Artificial Intelligence (AI) could potentially reduce capacity demands and enable less complex and less power consuming coding schemes in the receiver. For instance, data, such as video and voice, often contains replica (e.g., pixels, words) of previous data. ML and AI could identify such repetition and avoid the transmission of such information, reducing capacity demands. In addition, regarding signal decoding, very robust code rates, and complex and power consuming coding schemes are being used today to ensure correct decoding of the information at the receiver. These can be relaxed for specific data types, such as voice and IoT, as ML and AI, after extensive training can assist to predict the correct information hidden in erroneous received data. This would allow less robust coding rates to be used at the transmitter but also less complex and power consuming coding schemes to be used at the receiver.Research has shown that this application of ML and AI is viable and this activity will therefore develop and test ML and AI-based data rate reduction techniques for ground terminals of specific applications whose low levels of false predictions will not have a significant impact on the quality of service. The performance improvements will be tested in a laboratory testbed with inputs of real data, such as voice and video.Procurement Policy: C(1) = Activity restricted to non-prime contractors (incl. SMEs). For additional information please go to:http://www.esa.int/About_Us/Business_with_ESA/Small_and_Medium_Sized_Enterprises/Opportunities_for_SMEs/Procurement_policy_on_fair_access_for_SMEs_-_the_C1-C4_ClausesRead more
- Documents:
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CLICK HERE TO LOGINMachine Learning Techniques for Data Rate Reduction - Netherlands Tender
The EUROPEAN SPACE RESEARCH AND TECHNOLOGY CENTRE (ESTEC), a Government sector organization in Netherlands, has announced a new tender for Machine Learning Techniques for Data Rate Reduction. This tender is published on NetherlandsTenders under NLT Ref No: 101320428 and is categorized as a Tender. Interested and eligible suppliers are invited to participate by reviewing the tender documents and submitting their bids before the deadline on 2024-09-02.
The estimated tender value is Refer Document, and full details, including technical specifications and submission requirements, are provided in the official tender documents. Ensure all submissions meet the criteria outlined to be considered for evaluation.
