501 lines
17 KiB
Markdown
501 lines
17 KiB
Markdown
# System Architecture
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This document provides a comprehensive overview of the NowYouSeeMe holodeck environment architecture, including system design, data flow, and component interactions.
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## 🏗️ High-Level Architecture
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```
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ NowYouSeeMe Holodeck │
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├─────────────────────────────────────────────────────────────────────────────┤
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│ 📷 Camera Module │ 📡 RF Module │ 🧠 Processing Module │
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│ • OpenCV/GStreamer │ • Intel 5300 │ • SLAM Algorithms │
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│ • Real-time capture │ • Nexmon CSI │ • Sensor Fusion │
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│ • Calibration │ • AoA Estimation │ • Neural Enhancement │
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└─────────────────────┼─────────────────────┼───────────────────────────────┘
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│ │
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▼ ▼
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ 🎯 Core Processing Engine │
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│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────────────┐ │
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│ │ Vision SLAM │ │ RF SLAM │ │ Sensor Fusion │ │
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│ │ • ORB-SLAM3 │ │ • AoA Estimation│ │ • EKF Filter │ │
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│ │ • Feature Track │ │ • CIR Analysis │ │ • Particle Filter │ │
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│ │ • Pose Graph │ │ • RF Mapping │ │ • Multi-sensor Fusion │ │
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│ └─────────────────┘ └─────────────────┘ └─────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────────────────┘
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│
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▼
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┌─────────────────────────────────────────────────────────────────────────────┐
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│ 🎨 Rendering & Output │
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│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────────────┐ │
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│ │ 3D Scene │ │ NeRF Render │ │ Export Engine │ │
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│ │ • OpenGL │ │ • Neural Fields │ │ • Unity/Unreal │ │
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│ │ • Real-time │ │ • Photo-real │ │ • VR/AR Support │ │
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│ │ • Interactive │ │ • GPU Accelerated│ │ • Projection Mapping │ │
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│ └─────────────────┘ └─────────────────┘ └─────────────────────────┘ │
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└─────────────────────────────────────────────────────────────────────────────┘
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```
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## 🔄 Data Flow Architecture
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### Primary Data Flow
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```mermaid
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graph TD
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A[Camera Input] --> B[Image Processing]
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C[WiFi CSI] --> D[RF Processing]
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B --> E[Feature Extraction]
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D --> F[AoA Estimation]
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E --> G[Vision SLAM]
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F --> H[RF SLAM]
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G --> I[Sensor Fusion]
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H --> I
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I --> J[Pose Estimation]
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J --> K[3D Scene Update]
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K --> L[Rendering Engine]
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L --> M[User Interface]
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N[Azure Cloud] --> O[GPU Computing]
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O --> P[Neural Enhancement]
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P --> L
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```
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### Real-time Processing Pipeline
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```
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┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
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│ Camera Capture │ │ Wi-Fi CSI Capture│ │ Calibration │
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│ (OpenCV/GStream)│ │ (Intel 5300/Nex) │ │ Store │
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└─────────┬───────┘ └────────┬─────────┘ └─────────────────┘
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│ │
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▼ ▼
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┌─────────────────────────────────────────────────────────────┐
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│ Sensor Fusion Module │
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│ - RF point cloud & occupancy grid │
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│ - Vision pose graph & dense point cloud │
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└─────────────┬───────────────────────────────┬─────────────┘
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│ │
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▼ ▼
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┌─────────────────┐ ┌─────────────────────┐
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│ Export Engine │ │ Rendering Engine │
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│ (Unity/UE4) │ │ (VR/Projection Map) │
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└─────────────────┘ └─────────────────────┘
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```
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## 🧩 Component Architecture
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### 1. Data Ingestion Layer
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#### Camera Module (`src/ingestion/capture.py`)
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```python
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class CameraCapture:
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"""Real-time camera data acquisition"""
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def __init__(self, config: CameraConfig):
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self.config = config
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self.cap = cv2.VideoCapture(config.device_id)
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def get_frame(self) -> np.ndarray:
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"""Capture and return current frame"""
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ret, frame = self.cap.read()
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return frame if ret else None
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def calibrate(self) -> CalibrationResult:
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"""Perform camera calibration"""
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# Implementation for intrinsic/extrinsic calibration
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```
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#### CSI Module (`src/ingestion/csi_acquirer.py`)
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```python
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class CSIAcquirer:
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"""WiFi Channel State Information capture"""
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def __init__(self, config: CSIConfig):
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self.config = config
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self.interface = config.interface
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def capture_csi(self) -> CSIPacket:
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"""Capture CSI data from WiFi interface"""
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# Implementation for CSI packet capture
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```
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### 2. Processing Layer
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#### Vision SLAM (`src/vision_slam/`)
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```cpp
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class VisionSLAM {
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public:
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VisionSLAM(const VisionConfig& config);
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// Main processing methods
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PoseResult processFrame(const cv::Mat& frame);
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std::vector<MapPoint> getMapPoints() const;
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void reset();
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private:
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// ORB-SLAM3 integration
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std::unique_ptr<ORB_SLAM3::System> slam_system;
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// Feature tracking and pose estimation
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};
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```
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#### RF SLAM (`src/rf_slam/`)
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```cpp
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class RFSLAM {
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public:
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RFSLAM(const RFConfig& config);
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// RF processing methods
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AoAResult estimateAoA(const CSIPacket& packet);
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RFMap generateRFMap() const;
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void updateRFModel();
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private:
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// CIR analysis and AoA estimation
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std::unique_ptr<CIRConverter> cir_converter;
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std::unique_ptr<AoAEstimator> aoa_estimator;
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};
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```
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#### Sensor Fusion (`src/fusion/`)
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```cpp
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class SensorFusion {
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public:
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SensorFusion(const FusionConfig& config);
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// Multi-sensor fusion
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FusionResult fuseData(const VisionData& vision, const RFData& rf);
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PoseResult getCurrentPose() const;
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void updateFusionModel();
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private:
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// EKF and particle filter implementations
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std::unique_ptr<EKFFusion> ekf_fusion;
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std::unique_ptr<ParticleFilter> particle_filter;
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};
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```
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### 3. Rendering Layer
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#### 3D Scene (`src/ui/holodeck_ui.py`)
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```python
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class HolodeckUI(QMainWindow):
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"""Main user interface for the holodeck environment"""
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def __init__(self):
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super().__init__()
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self.setup_ui()
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self.setup_3d_scene()
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self.setup_controls()
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def setup_3d_scene(self):
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"""Initialize 3D OpenGL scene"""
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self.gl_widget = HolodeckGLWidget()
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self.setCentralWidget(self.gl_widget)
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def update_scene(self, pose_data: PoseData):
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"""Update 3D scene with new pose data"""
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self.gl_widget.update_pose(pose_data)
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```
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#### NeRF Rendering (`src/nerf/`)
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```python
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class NeRFRenderer:
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"""Neural Radiance Fields rendering"""
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def __init__(self, config: NeRFConfig):
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self.config = config
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self.model = self.load_nerf_model()
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def render_scene(self, pose: np.ndarray) -> np.ndarray:
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"""Render photo-realistic scene from pose"""
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# GPU-accelerated NeRF rendering
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```
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### 4. Cloud Integration
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#### Azure Integration (`src/cloud/azure_integration.cpp`)
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```cpp
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class AzureIntegration {
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public:
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AzureIntegration(const AzureConfig& config);
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// Cloud GPU management
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bool provisionGPUResource(const std::string& vm_name);
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bool deployModel(const std::string& model_name);
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ComputeJob submitJob(const JobRequest& request);
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private:
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// Azure SDK integration
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std::unique_ptr<Azure::Compute::VirtualMachines> vm_client;
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std::unique_ptr<Azure::AI::ML::Workspace> ml_workspace;
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};
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```
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## 🔧 System Configuration
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### Configuration Hierarchy
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```
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config/
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├── camera_config.json # Camera settings
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├── csi_config.json # WiFi CSI settings
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├── slam_config.json # SLAM parameters
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├── fusion_config.json # Sensor fusion settings
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├── nerf_config.json # NeRF rendering settings
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├── azure_config.json # Azure integration settings
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└── ui_config.json # User interface settings
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```
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### Configuration Example
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```json
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{
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"system": {
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"latency_target": 20,
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"accuracy_target": 10,
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"fps_target": 30
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},
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"camera": {
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"device_id": 0,
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"width": 1280,
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"height": 720,
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"fps": 30
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},
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"csi": {
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"interface": "wlan0",
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"channel": 6,
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"bandwidth": 20,
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"packet_rate": 100
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},
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"slam": {
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"vision_enabled": true,
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"rf_enabled": true,
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"fusion_enabled": true
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},
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"rendering": {
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"nerf_enabled": true,
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"gpu_acceleration": true,
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"quality": "high"
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}
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}
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```
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## 🚀 Performance Architecture
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### Real-time Constraints
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| Component | Latency Target | Throughput | Resource Usage |
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|-----------|----------------|------------|----------------|
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| **Camera Capture** | <5ms | 30-60 FPS | Low CPU |
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| **CSI Processing** | <10ms | 100+ pkt/s | Medium CPU |
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| **Vision SLAM** | <15ms | 30 FPS | High CPU/GPU |
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| **RF SLAM** | <10ms | 100 pkt/s | Medium CPU |
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| **Sensor Fusion** | <5ms | 30 Hz | Medium CPU |
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| **Rendering** | <10ms | 30-60 FPS | High GPU |
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| **Total Pipeline** | <20ms | 30 Hz | Optimized |
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### Resource Management
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```python
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class ResourceManager:
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"""Manages system resources and performance"""
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def __init__(self):
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self.cpu_monitor = CPUMonitor()
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self.gpu_monitor = GPUMonitor()
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self.memory_monitor = MemoryMonitor()
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def optimize_performance(self):
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"""Dynamically adjust settings based on resources"""
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cpu_usage = self.cpu_monitor.get_usage()
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gpu_usage = self.gpu_monitor.get_usage()
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if cpu_usage > 80:
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self.reduce_processing_quality()
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if gpu_usage > 90:
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self.reduce_rendering_quality()
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```
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## 🔒 Security Architecture
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### Data Protection
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```python
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class SecurityManager:
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"""Handles data security and privacy"""
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def __init__(self):
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self.encryption = AESEncryption()
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self.authentication = OAuth2Auth()
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def secure_data_transmission(self, data: bytes) -> bytes:
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"""Encrypt data for transmission"""
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return self.encryption.encrypt(data)
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def authenticate_user(self, credentials: dict) -> bool:
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"""Authenticate user access"""
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return self.authentication.verify(credentials)
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```
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### Privacy Considerations
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- **Local Processing**: Sensitive data processed locally
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- **Data Encryption**: All transmissions encrypted
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- **User Consent**: Clear data usage policies
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- **Data Retention**: Configurable retention periods
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## 🔄 Scalability Architecture
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### Horizontal Scaling
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```yaml
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# docker-compose.yml
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services:
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nowyouseeme:
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image: nowyouseeme/nowyouseeme
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scale: 3 # Multiple instances
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load_balancer: true
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redis:
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image: redis:alpine
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# Shared state management
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postgres:
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image: postgres:alpine
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# Persistent data storage
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```
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### Vertical Scaling
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```python
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class ScalabilityManager:
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"""Manages system scaling"""
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def auto_scale(self):
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"""Automatically scale based on load"""
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load = self.get_system_load()
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if load > 80:
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self.scale_up()
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elif load < 30:
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self.scale_down()
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```
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## 🧪 Testing Architecture
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### Test Pyramid
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```
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┌─────────────────────────────────────┐
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│ E2E Tests │ (10%)
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│ Complete system workflows │
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└─────────────────────────────────────┘
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┌─────────────────────────────────────┐
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│ Integration Tests │ (20%)
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│ Component interaction tests │
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└─────────────────────────────────────┘
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┌─────────────────────────────────────┐
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│ Unit Tests │ (70%)
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│ Individual component tests │
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└─────────────────────────────────────┘
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```
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### Test Categories
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```python
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# Unit Tests
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class TestVisionSLAM:
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def test_feature_extraction(self):
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"""Test feature extraction from images"""
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def test_pose_estimation(self):
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"""Test pose estimation accuracy"""
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# Integration Tests
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class TestSensorFusion:
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def test_vision_rf_fusion(self):
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"""Test fusion of vision and RF data"""
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def test_real_time_performance(self):
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"""Test real-time performance constraints"""
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# End-to-End Tests
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class TestHolodeckWorkflow:
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def test_complete_session(self):
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"""Test complete holodeck session"""
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def test_calibration_workflow(self):
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"""Test camera and RF calibration"""
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```
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## 📊 Monitoring Architecture
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### Metrics Collection
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```python
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class MetricsCollector:
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"""Collects system performance metrics"""
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def __init__(self):
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self.prometheus_client = PrometheusClient()
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self.grafana_client = GrafanaClient()
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def collect_metrics(self):
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"""Collect real-time metrics"""
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metrics = {
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'latency': self.measure_latency(),
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'accuracy': self.measure_accuracy(),
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'fps': self.measure_fps(),
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'cpu_usage': self.measure_cpu(),
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'gpu_usage': self.measure_gpu(),
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'memory_usage': self.measure_memory()
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}
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self.prometheus_client.push_metrics(metrics)
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```
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### Alerting System
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```python
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class AlertManager:
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"""Manages system alerts and notifications"""
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def check_alerts(self):
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"""Check for alert conditions"""
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if self.latency > 20:
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self.send_alert("High latency detected")
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if self.accuracy < 10:
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self.send_alert("Low accuracy detected")
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```
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## 🔮 Future Architecture
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### Planned Enhancements
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1. **Edge Computing**: Distributed processing nodes
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2. **5G Integration**: Low-latency wireless communication
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3. **AI/ML Enhancement**: Advanced neural networks
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4. **Quantum Computing**: Quantum-accelerated algorithms
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5. **Holographic Display**: True holographic rendering
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### Architecture Evolution
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```
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Current: Single-node processing
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↓
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Future: Distributed edge computing
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↓
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Future+: Quantum-enhanced processing
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↓
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Future++: Holographic reality
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```
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---
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For more detailed information about specific components, see:
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- [API Reference](API_REFERENCE.md) - Complete API documentation
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- [Data Flow](dataflow.md) - Detailed data flow diagrams
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- [Performance Guide](performance.md) - Optimization strategies
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- [Security Guide](security.md) - Security considerations |