Add WiFi DensePose implementation and results

- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads.
- Added a comprehensive training utility for the model, including loss functions and training steps.
- Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.

tests/unit/test_csi_processor.py

@@ -0,0 +1,105 @@
+import pytest
+import numpy as np
+import asyncio
+from unittest.mock import Mock, AsyncMock, patch
+from src.core.csi_processor import CSIProcessor
+
+
+class TestCSIProcessor:
+    """Test suite for CSI processor following London School TDD principles"""
+    
+    @pytest.fixture
+    def mock_csi_data(self):
+        """Generate synthetic CSI data for testing"""
+        # 3x3 MIMO, 56 subcarriers, 100 temporal samples
+        amplitude = np.random.uniform(0.1, 2.0, (3, 3, 56, 100))
+        phase = np.random.uniform(-np.pi, np.pi, (3, 3, 56, 100))
+        return {
+            'amplitude': amplitude,
+            'phase': phase,
+            'timestamp': 1234567890.0,
+            'rssi': -45,
+            'channel': 6
+        }
+    
+    @pytest.fixture
+    def csi_processor(self):
+        """Create CSI processor instance for testing"""
+        return CSIProcessor()
+    
+    async def test_process_csi_data_returns_normalized_output(self, csi_processor, mock_csi_data):
+        """Test that CSI processing returns properly normalized output"""
+        # Act
+        result = await csi_processor.process(mock_csi_data)
+        
+        # Assert
+        assert result is not None
+        assert 'processed_amplitude' in result
+        assert 'processed_phase' in result
+        assert result['processed_amplitude'].shape == (3, 3, 56, 100)
+        assert result['processed_phase'].shape == (3, 3, 56, 100)
+        
+        # Verify normalization - values should be in reasonable range
+        assert np.all(result['processed_amplitude'] >= 0)
+        assert np.all(result['processed_amplitude'] <= 1)
+        assert np.all(result['processed_phase'] >= -np.pi)
+        assert np.all(result['processed_phase'] <= np.pi)
+    
+    async def test_process_csi_data_handles_invalid_input(self, csi_processor):
+        """Test that CSI processor handles invalid input gracefully"""
+        # Arrange
+        invalid_data = {'invalid': 'data'}
+        
+        # Act & Assert
+        with pytest.raises(ValueError, match="Invalid CSI data format"):
+            await csi_processor.process(invalid_data)
+    
+    async def test_process_csi_data_removes_nan_values(self, csi_processor, mock_csi_data):
+        """Test that CSI processor removes NaN values from input"""
+        # Arrange
+        mock_csi_data['amplitude'][0, 0, 0, 0] = np.nan
+        mock_csi_data['phase'][0, 0, 0, 0] = np.nan
+        
+        # Act
+        result = await csi_processor.process(mock_csi_data)
+        
+        # Assert
+        assert not np.isnan(result['processed_amplitude']).any()
+        assert not np.isnan(result['processed_phase']).any()
+    
+    async def test_process_csi_data_applies_temporal_filtering(self, csi_processor, mock_csi_data):
+        """Test that temporal filtering is applied to CSI data"""
+        # Arrange - Add noise to make filtering effect visible
+        noisy_amplitude = mock_csi_data['amplitude'] + np.random.normal(0, 0.1, mock_csi_data['amplitude'].shape)
+        mock_csi_data['amplitude'] = noisy_amplitude
+        
+        # Act
+        result = await csi_processor.process(mock_csi_data)
+        
+        # Assert - Filtered data should be smoother (lower variance)
+        original_variance = np.var(mock_csi_data['amplitude'])
+        filtered_variance = np.var(result['processed_amplitude'])
+        assert filtered_variance < original_variance
+    
+    async def test_process_csi_data_preserves_metadata(self, csi_processor, mock_csi_data):
+        """Test that metadata is preserved during processing"""
+        # Act
+        result = await csi_processor.process(mock_csi_data)
+        
+        # Assert
+        assert result['timestamp'] == mock_csi_data['timestamp']
+        assert result['rssi'] == mock_csi_data['rssi']
+        assert result['channel'] == mock_csi_data['channel']
+    
+    async def test_process_csi_data_performance_requirement(self, csi_processor, mock_csi_data):
+        """Test that CSI processing meets performance requirements (<10ms)"""
+        import time
+        
+        # Act
+        start_time = time.time()
+        result = await csi_processor.process(mock_csi_data)
+        processing_time = time.time() - start_time
+        
+        # Assert
+        assert processing_time < 0.01  # <10ms requirement
+        assert result is not None