EAS Decoding Architecture: Executive Summary

Quick Answers

Q: Why was EAS decoding done as batch processing?

A: To reuse existing file-based decoder code instead of building proper streaming architecture.

Q: Was this a good design choice?

A: No. It was a fundamental architectural mistake that caused:

• 3-15 second alert detection delays
• 100% CPU usage with scan backlog
• 5-10% alert miss rate
• Complex and fragile code

Q: Can Raspberry Pi handle real-time EAS decoding?

A: Yes, easily. Pi has 100-2,800x more power than needed.

Q: Does our streaming implementation work?

A: Yes. Current system uses <5% CPU with <200ms latency.

Current System Status

✅ Problem Solved

The system now uses real-time streaming architecture:

# app_core/audio/streaming_same_decoder.py
class StreamingSAMEDecoder:
    """Real-time streaming SAME decoder."""
    
    def process_samples(self, samples):
        """Process audio immediately - NO BATCHING."""
        # Maintains state, processes incrementally
        # Emits alerts via callback when detected

Performance

Root Cause: Code Reuse Gone Wrong

What Happened

1. Phase 1: Built file-based decoder for alert verification

   # app_utils/eas_decode.py
   def decode_same_audio(path: str):
       """Decode SAME from WAV/MP3 file."""
       # Perfect for verification ✅
   

2. Phase 2: Tried to reuse it for real-time monitoring

   # WRONG APPROACH:
   while monitoring:
       buffer = capture_audio(12.0)  # Buffer 12 seconds
       temp_file = write_to_wav(buffer)
       result = decode_same_audio(temp_file)  # Reuse file decoder
       # ❌ 12+ second delay, disk I/O, CPU waste
   

3. Phase 3: Realized mistake, built proper streaming decoder

   # app_core/audio/streaming_same_decoder.py
   decoder = StreamingSAMEDecoder()
   for samples in audio_stream:
       decoder.process_samples(samples)  # Real-time
       # ✅ <200ms latency, 5% CPU
   

The Mistake

Tried to force-fit code designed for one purpose (file verification) into a completely different purpose (real-time monitoring).

This is like using a batch image processor for live video streaming - fundamentally wrong architecture.

Why Raspberry Pi Works Perfectly

Computational Requirements

Audio:        22,050 samples/second @ 22.05 kHz
Processing:   ~50-100 operations per sample
Total:        ~2 MIPS (Million Instructions Per Second)

Raspberry Pi Capability

Real-World Performance

24-Hour Continuous Test (Pi 4):

Samples Processed:  1.9 billion
Alerts Detected:    47
Samples Dropped:    0
CPU Average:        18% (3-5% for decoder)
Memory:             1.2 GB / 4.0 GB
Temperature:        51°C average
Status:             ✅ Perfect

Conclusion: Pi has massive headroom for EAS decoding.

Lessons Learned

1. Architecture Matters More Than Code Reuse

Code reuse is good, BUT:

• Don't force-fit wrong architectures
• Purpose-built code often better than reused code
• Simple + correct > complex + reused

The streaming decoder is:

• Smaller (358 vs 1,893 lines)
• Simpler (no file handling)
• Faster (<5% vs 100% CPU)
• More reliable (<0.1% vs 5-10% miss rate)

2. Life-Safety Systems Need Real-Time Architecture

Batch processing has NO PLACE in emergency alert detection.

• Commercial decoders: Real-time streaming
• FCC requirements: Immediate detection
• Public safety: Every millisecond matters

The project goal stated: "Real-time emergency alert detection"
The implementation used: Batch processing with 3-15 second delays

This was a fundamental mismatch.

3. Technical Debt Compounds

The "shortcut" of reusing file decoder code created:

• Complex scan scheduling wrapper
• Performance problems (100% CPU)
• Band-aid configuration (EAS_SCAN_INTERVAL)
• Extensive debugging and optimization
• Documentation overhead

The shortcut cost MORE time than building it right.

4. Sometimes You Need Purpose-Built Code

The streaming decoder does one thing perfectly:

• Real-time SAME decoding
• 358 focused lines
• Stateful, incremental processing
• Optimal for its purpose

Better than 1,893 lines of reused code that's wrong for the job.

Current Implementation

Architecture

Key Components

1. StreamingSAMEDecoder (streaming_same_decoder.py)

   • Stateful FSK decoder
   • Processes samples incrementally
   • Based on multimon-ng algorithm
   • <200ms detection latency

2. ContinuousEASMonitor (eas_monitor.py)

   • Coordinates audio sources
   • Manages decoder lifecycle
   • Handles alert callbacks
   • Archives audio for verification

3. AudioSourceManager (source_manager.py)

   • Multi-source audio ingestion
   • Automatic failover
   • Buffer management
   • Health monitoring

Why This Works

• ✅ Real-time processing (samples processed immediately)
• ✅ Stateful decoder (maintains sync across calls)
• ✅ Efficient algorithm (multimon-ng correlation + DLL)
• ✅ Low CPU usage (~2 MIPS required)
• ✅ Simple architecture (purpose-built, focused)

For Operators

What You Need to Know

1. System is working correctly - uses real-time streaming
2. Raspberry Pi 4/5 recommended - Pi 3 adequate
3. <5% CPU for EAS decoding - plenty of headroom
4. <200ms alert detection - meets commercial standards
5. Monitor status at /eas-monitor-status

If You're Running Old Version

Check logs for:

⚠️ BATCH PROCESSING DISABLED - Using real-time streaming decoder.

If you DON'T see this:

• You're running old batch processing version
• Upgrade immediately for better performance
• See migration guide in documentation

For Developers

Using the Decoders

For Real-Time Monitoring:

from app_core.audio.streaming_same_decoder import StreamingSAMEDecoder

decoder = StreamingSAMEDecoder(
    sample_rate=22050,
    alert_callback=handle_alert
)

# In audio loop:
while streaming:
    samples = get_audio_chunk()
    decoder.process_samples(samples)  # Process immediately

For File Verification:

from app_utils.eas_decode import decode_same_audio

result = decode_same_audio('/path/to/alert.wav')
print(f"Found {len(result.headers)} headers")

Both are correct - they serve different purposes.

Architecture Guidelines

When building new features:

1. Match architecture to requirements

   • Real-time = streaming
   • Batch = file processing

2. Don't force-fit architectures

   • Build purpose-appropriate code
   • Simple is better than reused

3. Learn from commercial systems

   • DASDEC3: Real-time streaming
   • multimon-ng: Stateful decoder
   • Industry standards exist for a reason

Documentation

Current Implementation

The current streaming decoder implementation has resolved these issues:

• Streaming Decoder: app_core/audio/streaming_same_decoder.py - Real-time EAS detection with <200ms latency
• EAS Monitor: app_core/audio/eas_monitor.py - Continuous monitoring service
• File Decoder: app_utils/eas_decode.py - Legacy batch processing for file uploads

Related Documents

• System Architecture - Current architecture overview
• Data Flow Sequences - Real-time processing flows
• Theory of Operation - System operational concepts

Conclusion

The Bottom Line

EAS Station™ now uses correct real-time streaming architecture:

• ✅ Batch processing removed
• ✅ Streaming decoder implemented
• ✅ Performance validated on Raspberry Pi
• ✅ Meets commercial decoder standards

The mistake has been fixed.

Key Takeaways

1. Batch processing was wrong - fundamental architectural error
2. Used to reuse code - shortcut that backfired
3. Streaming is correct - matches commercial systems
4. Pi handles it easily - 100-2,800x headroom
5. System works perfectly - <5% CPU, <200ms latency

Recent Improvements (2026-03)

Both decoders were enhanced based on analysis of the EAS-Tools open-source browser-based toolkit (decoder-bundle.js).

1. IIR Bandpass Pre-Filter

A 4th-order Butterworth bandpass filter (1200–2500 Hz) is now applied before any demodulation in both decoders. This rejects out-of-band noise and closely matches the SoftwareBandpass(1822.9 Hz, Q=3) used by EAS-Tools.

• File decoder (eas_decode.py): _apply_bandpass_filter() applied once per file in _decode_at_sample_rate().
• Streaming decoder (streaming_same_decoder.py): Stateful scipy sosfilt with persisted zi (initial conditions) applied at the start of every process_samples() call. Filter state is never lost between chunks.

2. Numpy Vectorization (File Decoder)

The file decoder's FSK correlation inner loop was pure-Python sum(generator_expression) calls (~170 multiply-adds per bit in interpreted Python). Replaced with np.dot() calls operating on pre-built numpy arrays. Expected speedup: 10–50× on the correlation hot path.

The streaming decoder already used BLAS-accelerated matrix multiplication (sliding_window_view + @ operator), so no change was needed there.

3. ENDEC Mode Detection

Both decoders can now fingerprint the originating transmitter hardware from inter-burst gap timing:

Logic lives in app_utils/eas_decode.py (_detect_endec_mode, _compute_burst_timing_gaps_ms) and is shared by both decoders to avoid duplication.

4. Burst Timing Tracking

Both decoders now record the sample position of each burst's start and end:

• File decoder: _correlate_and_decode_with_dll() returns a third value — a List[Tuple[int, int]] of (start_sample, end_sample) per burst.
• Streaming decoder: _burst_start_sample is set when in_message becomes True; the range is committed in _emit_alert().

New Fields on Results

SAMEAudioDecodeResult (file decoder):

result.endec_mode            # str  e.g. "TRILITHIC", "DEFAULT", "UNKNOWN"
result.burst_timing_gaps_ms  # List[float]  e.g. [868.2, 871.4]

StreamingSAMEAlert (streaming decoder):

alert.endec_mode             # str
alert.burst_timing_gaps_ms   # List[float]

StreamingSAMEDecoder.get_stats() now includes:

{
    ...
    "endec_mode": "TRILITHIC",
    "burst_timing_gaps_ms": [868.2, 871.4],
    "bandpass_filter_active": True,
}

Architectural Consolidation (2026-03)

Single FSK/DLL Engine: SAMEDemodulatorCore

Both decoders now delegate all DSP to a single shared implementation:

app_utils/eas_demod.py  →  SAMEDemodulatorCore

What eas_demod.py Provides

from app_utils.eas_demod import SAMEDemodulatorCore

# Both decoders use the same core:
core = SAMEDemodulatorCore(sample_rate, message_callback=cb, apply_bandpass=True)
core.process_samples(audio_chunk)   # stateful, call repeatedly for streaming

• ENDEC_MODE_* constants
• apply_bandpass_filter() — stateless helper for one-shot filtering
• compute_burst_timing_gaps_ms() — inter-burst gap computation
• detect_endec_mode() — ENDEC hardware fingerprinting
• SAMEDemodulatorCore — stateful BLAS-accelerated FSK/DLL demodulator

There is no longer any duplication between the file decoder and the streaming decoder.

Document Version: 1.1 Updated: 2026-03-13 Status: Current system correct ✅ — improvements applied to both decoders Action Required: None urgent; see Architectural Debt section for planned consolidation