SIH26147: Automated model for analysis of .IQ and .wav files along with signal parameter e — SIH 2026 | Wynit

SIH26147: Automated model for analysis of .IQ and .wav files along with signal parameter e — SIH 2026 | Wynit

What they'll ask you

12

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Sample dataset files for training and testing?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific performance or accuracy targets?
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.

9 morethe ones a panel gets to if there is time

The brief asks for file input interface. How would you build that?
Decoded from SIH26147 itself: A graphical user interface to upload and ingest .IQ or .wav signal data files. The brief asks for it by name.
The brief asks for parameter extraction. How would you build that?
Decoded from SIH26147 itself: An automated analysis module to identify sampling frequency, modulation type, FEC, and interleaving. The brief asks for it by name.
The brief asks for demodulation engine. How would you build that?
Decoded from SIH26147 itself: A module to demodulate signals including FSK, QAM, and PSK. The brief asks for it by name.
The brief asks for de-interleaving and fec. How would you build that?
Decoded from SIH26147 itself: A module to carry out de-interleaving and forward error correction like Viterbi decoding and RS block codes. The brief asks for it by name.
The brief asks for bit stream correlation. How would you build that?
Decoded from SIH26147 itself: A component to perform bit stream correlation for identifying header and payload data. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
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 evaluators will check if the GUI can successfully take .IQ and .wav inputs, display spectral features like constellation plots and waterfall diagrams, and correctly perform parameter extraction, demodulation, de-interleaving, and FEC.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (412 words)

• Background The raw data for analysis of signal collected off the air typically range from few Khz to Ghz bands. The analysis is being carried out manually to identify the signal parameters and the resultant data is then utilised for processing signals in the designated sensors. This data is often insufficient for fine grain analysis for parameter extraction such as modulation type, sampling rate, FEC, interleaving, etc. This creates a need for advanced data processing to extract the observation data. • Description The terrestrial signals received from various sources includes data in HF, VHF and UHF bands. The raw data collected in the form of .wav or .IQ format to retain the characteristics of wave form. The analysis of signals is primarily dependent on the basic characteristics of data points selected during recording of these signals. Since the data point are recorded from different sensors and different locations, the parameters may vary. Therefore, the data available for analysis is often insufficient to clearly identify fine details such as sampling rate, modulation type, interleaving, FEC etc. This limitation reduces the accuracy and confidence of interpretation and data analysis that require detailed information. The data saved as .IQ and .wav have different parameters and therefore they store the raw information in different format. These files have to be processed in different ways for signal analysis to extract signal parameters. The problem can be addressed using advanced models such as GNU Radio, python, C++ to enhance the parameter extraction capability and more information rich inputs. The spectral relationship from training data containing both .IQ and .wav formats can be utilised for identifying signal parameters and carry out deeper analysis. The expected solution should be able to demodulate signals. The GUI based model will have features to take .IQ or .wav file as input data and perform following tasks. i.Identify signal parameters (Sampling frequency, Modulation, FEC, Interleaving). Additional features if feasible may be included. ii.Demodulate signals (FSK, QAM PSK) iii.Carry out de-interleaving (Block, Convolution, Diagonal, Pseudo Random). iv.FEC (short-constrained convolution codes with Viterbi decoding, RS block codes, Concatenated codes, LDPC). v.Bit stream correlation. • Expected Solution The expected system should improve feature visibility of signals with the help of GUI, enable automated signal analysis to identify spectral features such as sampling frequency, constellation plot, water fall (time-frequency domain), demodulate signals, carry out de-interleaving and error correction. The output can then be used to carry out correlation of bit stream for identification of header and payload.