Rcs Wavelet Neural Network
F 44 completed
Ai Ml
unknown / python · small
145
Files
37,268
LOC
0
Frameworks
4
Languages
Pipeline State
completedRun ID
#303125Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
51.00Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47727
Member of a group with 1 similar repo(s) — canonical #18123 view group →
Top concepts (12)
RepositoryProject Descriptiondata_accessWeb BackendtestinginfrastructureFactoryTestingLoggingDatabaseConfigurationCaching
Repobility — the code-quality scanner for AI-generated software · https://repobility.com
🧪 Code Distillation
Browse all specs →AI Prompt
Create an intelligent prediction system for aircraft RCS (Radar Cross Section) data. I need this system to use an AutoEncoder deep compression approach. The system should support three modes: Wavelet, Direct, and Differentiable Wavelet. For the network architecture, I need to be able to select various models like `EnhancedWaveletAutoEncoder` or `DirectAutoEncoder`, and I should be able to configure features such as Channel Attention, Dual-Branch architecture, and the small latent space dimension (16-32 dimensions). The system must also support various activation functions like 'sin' or 'gelu' and handle the three-stage training process: AutoEncoder pre-training, ParameterMapper training, and end-to-end fine-tuning. The primary interface should be manageable via a GUI.
python deep-learning autoencoder rcs signal-processing wavelet prediction machine-learning gui
Generated by gemma4:latest
Catalog Information
This project is an intelligent prediction system for aircraft RCS (Radar Cross Section) data using AutoEncoder deep compression.
Description
The huatanshaonian__rcs-wavelet-neural-network project is a high-efficiency RCS data prediction system based on AutoEncoder deep compression. It maps the parameter space to the hidden space, achieving efficient RCS data prediction. The system consists of three modes: Wavelet mode, Direct mode, and Differentiable Wavelet mode. Each mode has its own architecture and training process.
الوصف
هذا المشروع هو نظام تقدير ذكي للبيانات RCS (Radar Cross Section) لطائرات باستخدام ضغط AutoEncoder العميق. يُستخدم هذا النظام لتقليل البيانات RCS بفعالية عالية عن طريق توجيه مساحة المعلمات إلى المساحة المخفية. يتألف النظام من ثلاثة أنماط: نمط موجي، نمط مباشر، ونمط موجي قابل للتفاضل.
Novelty
9/10Tags
rcs-data-prediction autoencoder-deep-compression wavelet-mode direct-mode differentiable-wavelet-mode neural-network-architecture high-efficiency-prediction-system
Technologies
matplotlib numpy pandas pytorch scikit-learn scipy
Claude Models
claude-sonnet-4.5
Quality Score
F
43.7/100
Structure
48
Code Quality
42
Documentation
79
Testing
40
Practices
23
Security
22
Dependencies
90
Strengths
- Consistent naming conventions (snake_case)
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No CI/CD configuration \u2014 manual testing and deployment
- 38 bare except/catch blocks swallowing errors
- Potential hardcoded secrets in 3 files
- 6053 duplicate lines detected \u2014 consider DRY refactoring
- 14 'god files' with >500 LOC need decomposition
Recommendations
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Replace bare except/catch blocks with specific exception types
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
12.1h
Tech Debt (A)
Medium
DORA Rating
A
OWASP (100%)
Repobility · open methodology · https://repobility.com/research/
FAIL
Quality Gate
A
Risk (13)
Unknown
License
15.0%
Duplication
Languages
Frameworks
None detected
Symbols
method692
function202
variable88
class82
constant13
Concepts (13)
| Category | Name | Description | Confidence | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| Repobility · severity-and-effort ranking · https://repobility.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Repository | Found repository-named files | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_description | Project Description | 基于AutoEncoder深度压缩的飞行器RCS（雷达散射截面）智能预测系统，通过参数空间到隐空间的映射实现高效RCS数据预测。 | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | data_access | Detected data_access layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Web Backend | web-backend | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | testing | Detected testing layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| arch_layer | infrastructure | Detected infrastructure layer | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| design_pattern | Factory | Found factory/create_ naming patterns | 60% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Testing | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Logging | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Database | Detected from 27 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Configuration | Detected from 9 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Caching | Detected from 2 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| business_logic | Analytics | Detected from 3 related files | 50% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Methodology: Repobility · https://repobility.com/research/state-of-ai-code-2026/
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