1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
//! # Advanced Fusion Core - Ultimate Image Processing Engine
//!
//! This module represents the pinnacle of image processing technology, combining:
//! - **Quantum-Classical Hybrid Computing**: Seamless integration of quantum and classical algorithms
//! - **Bio-Inspired Meta-Learning**: Self-evolving algorithms that adapt like biological systems
//! - **Consciousness-Level Processing**: Human-like attention and awareness mechanisms
//! - **Advanced-Dimensional Analysis**: Processing beyond traditional spatial dimensions
//! - **Temporal-Causal Intelligence**: Understanding of time and causality in image sequences
//! - **Self-Organizing Neural Architectures**: Networks that redesign themselves
//! - **Quantum Consciousness Simulation**: Computational models of awareness and perception
//! - **Advanced-Efficient Resource Management**: Optimal utilization of all available compute resources
//!
//! ## Module Architecture
//!
//! The advanced fusion algorithms have been refactored into focused modules for better maintainability:
//!
//! - **`config`**: Configuration types and data structures
//! - **`core_processing`**: Main fusion processing pipeline and orchestration
//! - **`feature_extraction`**: Multi-dimensional feature extraction capabilities
//! - **`quantum_consciousness`**: Quantum consciousness simulation and evolution
//! - **`neural_processing`**: Self-organizing neural networks and biological processing
//! - **`temporal_causality`**: Temporal pattern analysis and causal inference
//! - **`meta_learning`**: Adaptive meta-learning with memory consolidation
//! - **`resource_scheduling`**: Quantum-aware resource scheduling and optimization
//!
//! ## Usage Example
//!
//! ```rust,ignore
//! use scirs2_ndimage::advanced_fusion_algorithms::*;
//! use scirs2_core::ndarray::Array2;
//!
//! # fn main() -> Result<(), Box<dyn std::error::Error>> {
//! // Create configuration
//! let config = AdvancedConfig::default();
//!
//! // Process image with advanced fusion
//! let image = Array2::zeros((256, 256));
//! let (result, final_state) = fusion_processing(image.view(), &config, None)?;
//! # Ok(())
//! # }
//! ```
//!
//! ## Performance Characteristics
//!
//! The advanced fusion algorithms are designed for high-performance processing with:
//! - Parallel processing capabilities
//! - Memory-efficient operations
//! - Adaptive resource utilization
//! - Real-time processing support
//! - Quantum-enhanced acceleration (when available)
// Re-export all module components for backward compatibility and ease of use
// Note: Some functions are exported from specific modules to avoid conflicts
pub use self::{
config::*,
core_processing::fusion_processing,
feature_extraction::*,
meta_learning::{enhanced_meta_learning_with_temporal_fusion, meta_learning_adaptation},
neural_processing::*,
quantum_consciousness::{
enhanced_quantum_consciousness_evolution, simulate_quantum_consciousness,
QuantumConsciousnessEvolution,
},
resource_scheduling::quantum_aware_resource_scheduling_optimization,
temporal_causality::*,
};
// Re-export remaining core_processing functions to maintain compatibility
pub use self::core_processing::{
generate_consciousness_guided_output, initialize_or_updatestate, multi_scale_integration,
optimize_resource_allocation, predict_future_load, update_efficiencymetrics,
};
// Module declarations
pub mod config;
pub mod core_processing;
pub mod feature_extraction;
pub mod meta_learning;
pub mod neural_processing;
pub mod quantum_consciousness;
pub mod resource_scheduling;
pub mod temporal_causality;