A machine learning software scheduler for electronic support receivers that minimizes intercept time and ensures a high interception rate in electronic warfare.
DRDO · Department of Defence Production /IDEX · Software
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Decomposed from what the description asks for. Nothing added.
Receiver System Model
Develop a system model for the receiver using measurements from a simulated RF environment with truth information.
Figures of Merit
Implement performance metrics like probability of detection, false alarm rate, sensitivity, and average intercept time error.
ML Based Scheduler
Develop a robust scheduler using machine learning to minimize intercept time and ensure a high interception rate.
Training and Optimization
Train the model based on hits and misses, and outline approaches to optimally intercept periodic scan receivers.
Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.
The jury will check if you have built a machine learning based receiver scheduler software, evaluated it against figures of merit like probability of detection and intercept time error, and trained it on a simulated RF environment.
A jury can still ask about these. Decide them deliberately rather than by accident.
Generated from the brief's own wording and the competition's published rules — never from a guess about what this ministry prefers.
Specific parameters or dimensions of the simulated RF environment?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Target hardware or performance runtime constraints for the software?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for receiver system model. How would you build that?
Decoded from SIH26055 itself — Develop a system model for the receiver using measurements from a simulated RF environment with truth information. The brief asks for it by name.
Each one is quoted from a gap in the brief, not a guess about your team.
No specific user named
The description states the expected solution as software but never names a specific human end user.
What the organisers attached, and what the brief assumes you can get.
Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.
Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.
Who has this problem, what already exists, and what you would have to find out.
The brief asks for figures of merit. How would you build that?
Decoded from SIH26055 itself — Implement performance metrics like probability of detection, false alarm rate, sensitivity, and average intercept time error. The brief asks for it by name.
The brief asks for ml based scheduler. How would you build that?
Decoded from SIH26055 itself — Develop a robust scheduler using machine learning to minimize intercept time and ensure a high interception rate. The brief asks for it by name.
The brief asks for training and optimization. How would you build that?
Decoded from SIH26055 itself — Train the model based on hits and misses, and outline approaches to optimally intercept periodic scan receivers. The brief asks for it by name.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check if you have built a machine learning based receiver scheduler software, evaluated it against figures of merit like probability of detection and intercept time error, and trained it on a simulated RF environment.
This is the evaluator read for your problem statement, decoded from the brief's own wording.