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.

references/script.py

@@ -0,0 +1,97 @@
+# WiFi DensePose Implementation - Core Neural Network Architecture
+# Based on "DensePose From WiFi" by Carnegie Mellon University
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+from typing import Dict, List, Tuple, Optional
+from collections import OrderedDict
+
+# CSI Phase Sanitization Module
+class CSIPhaseProcessor:
+    """
+    Processes raw CSI phase data through unwrapping, filtering, and linear fitting
+    Based on the phase sanitization methodology from the paper
+    """
+    
+    def __init__(self, num_subcarriers: int = 30):
+        self.num_subcarriers = num_subcarriers
+    
+    def unwrap_phase(self, phase_data: np.ndarray) -> np.ndarray:
+        """
+        Unwrap phase values to handle discontinuities
+        Args:
+            phase_data: Raw phase data of shape (samples, frequencies, antennas, antennas)
+        Returns:
+            Unwrapped phase data
+        """
+        unwrapped = np.copy(phase_data)
+        
+        for i in range(1, phase_data.shape[1]):  # Along frequency dimension
+            diff = unwrapped[:, i] - unwrapped[:, i-1]
+            
+            # Apply unwrapping logic
+            unwrapped[:, i] = np.where(diff > np.pi, 
+                                     unwrapped[:, i-1] + diff - 2*np.pi,
+                                     unwrapped[:, i])
+            unwrapped[:, i] = np.where(diff < -np.pi,
+                                     unwrapped[:, i-1] + diff + 2*np.pi,
+                                     unwrapped[:, i])
+        
+        return unwrapped
+    
+    def apply_filters(self, phase_data: np.ndarray) -> np.ndarray:
+        """
+        Apply median and uniform filters to eliminate outliers
+        """
+        from scipy.ndimage import median_filter, uniform_filter
+        
+        # Apply median filter in time domain
+        filtered = median_filter(phase_data, size=(3, 1, 1, 1))
+        
+        # Apply uniform filter in frequency domain
+        filtered = uniform_filter(filtered, size=(1, 3, 1, 1))
+        
+        return filtered
+    
+    def linear_fitting(self, phase_data: np.ndarray) -> np.ndarray:
+        """
+        Apply linear fitting to remove systematic phase drift
+        """
+        fitted_data = np.copy(phase_data)
+        F = self.num_subcarriers
+        
+        for sample_idx in range(phase_data.shape[0]):
+            for ant_i in range(phase_data.shape[2]):
+                for ant_j in range(phase_data.shape[3]):
+                    phase_seq = phase_data[sample_idx, :, ant_i, ant_j]
+                    
+                    # Calculate linear coefficients
+                    alpha1 = (phase_seq[-1] - phase_seq[0]) / (2 * np.pi * F)
+                    alpha0 = np.mean(phase_seq)
+                    
+                    # Apply linear fitting
+                    frequencies = np.arange(1, F + 1)
+                    linear_trend = alpha1 * frequencies + alpha0
+                    fitted_data[sample_idx, :, ant_i, ant_j] = phase_seq - linear_trend
+        
+        return fitted_data
+    
+    def sanitize_phase(self, raw_phase: np.ndarray) -> np.ndarray:
+        """
+        Complete phase sanitization pipeline
+        """
+        # Step 1: Unwrap phase
+        unwrapped = self.unwrap_phase(raw_phase)
+        
+        # Step 2: Apply filters
+        filtered = self.apply_filters(unwrapped)
+        
+        # Step 3: Linear fitting
+        sanitized = self.linear_fitting(filtered)
+        
+        return sanitized
+
+print("CSI Phase Processor implementation completed!")