Code Decoder: Mrp40 Morse

def update_speed_estimate(running_pulses, running_spaces, recent_window=20): recent = running_pulses[-recent_window:] + running_spaces[-recent_window:] dot = min(recent) # or 10th percentile return max(dot, 0.5) # avoid zero Use a small language model or dictionary to suggest corrections when timing is ambiguous. 7.3 Waterfall Display & Spectral Analysis Display real-time FFT to let user tune to the signal visually – essential for MRP40 usability. 8. Real-Time Implementation (Pseudocode) import sounddevice as sd def audio_callback(indata, frames, time, status): audio = indata[:, 0] # mono filtered = bandpass_filter(audio) gained = agc(filtered) envelope = np.abs(hilbert(gained)) binary = adaptive_threshold(envelope) pulses, spaces = extract_run_lengths(binary) dot_ms = estimate_dot_length(pulses, spaces, SAMPLE_RATE) text = decode_from_timings(pulses, spaces, dot_ms) print(text, end='', flush=True)

7.1 Fist Character Recognition (Speed Tracking) Human senders vary speed. Continuously update T every few symbols. mrp40 morse code decoder

from sklearn.cluster import KMeans def estimate_dot_length(pulses, spaces, fs=8000): # Convert samples to ms pulses_ms = [p * 1000 / fs for p in pulses] spaces_ms = [s * 1000 / fs for s in spaces] all_durations = pulses_ms + spaces_ms status): audio = indata[: