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