🌊 Signal Predictor (Frontend)

Advanced Signal Analysis & Prediction Platform

A cutting-edge full-stack web application for advanced time-series signal analysis using Fast Fourier Transform (FFT), multi-sinusoidal curve fitting, and machine learning techniques.

🚀 Live Demo

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✨ Features Overview

🔬 Advanced Signal Analysis

• FFT Analysis: Automatic frequency domain analysis to identify dominant frequencies
• Multi-Sinusoidal Curve Fitting: Sophisticated optimization using scipy algorithms
• Train/Test Validation: Intelligent data splitting with performance metrics
• Real-time Function Evaluation: Test fitted mathematical functions with custom inputs
• Advanced Mode: Customizable analysis parameters with noise filtering

🎛️ Signal Generation & Synthesis

• Synthetic Signal Generator: Create custom sinusoidal signals with multiple components
• Parameter Control: Fine-tune amplitude, frequency, phase, and DC offset
• Noise Simulation: Add configurable Gaussian noise for realistic testing
• Random Generation: AI-powered random parameter generation
• Visual Preview: Real-time visualization of generated signals

👤 User Management & Authentication

• Secure Registration: Email verification with professional templates
• Password Management: Reset functionality with secure token validation
• User Profiles: Customizable profiles with avatar upload
• Analysis Quotas: Configurable per-user analysis limits
• Session Management: Persistent analysis sessions for anonymous users

📊 Data Visualization & Sharing

• Interactive Plots: High-quality matplotlib visualizations
• Multi-view Analysis: Frequency spectrum, original vs reconstructed, training vs testing
• Public Sharing: Share analyses with optional password protection
• Export Capabilities: Download results as CSV, images, or JSON
• Analysis History: Comprehensive browsing and management of past analyses

🏗️ Modern Architecture

• Full-Stack Separation: Django REST API + React SPA
• Cloud Storage: AWS S3/Cloudflare R2 integration for scalability
• RESTful API: Complete API coverage for all features
• Responsive Design: Futuristic dark theme optimized for all devices
• Production Ready: Configured for deployment with PostgreSQL

🧮 Mathematical Foundation

The application implements a sophisticated signal analysis pipeline:

1. Data Preprocessing: Intelligent splitting into training (x < split_point) and testing (x ≥ split_point) sets

2. Frequency Analysis: Fast Fourier Transform to identify dominant frequency components

3. Model Fitting: Multi-sinusoidal regression of the form:

   f(x) = Σᵢ Aᵢ * sin(2π * fᵢ * x + φᵢ) + D
   

   Where:

   • Aᵢ = Amplitude of component i
   • fᵢ = Frequency of component i
   • φᵢ = Phase shift of component i
   • D = DC offset (bias term)

4. Model Validation: Performance evaluation using Mean Squared Error (MSE) on test data

5. Prediction: Real-time function evaluation for new input values

🚀 Quick Start

Prerequisites

• Python 3.11+ with pip
• Node.js 18+ with npm
• PostgreSQL 13+ (or SQLite for development)
• Git for version control

🔧 Backend Setup (Django API)

1. Clone the repository

   git clone https://github.com/riteshnarayandas/signal-predictor.git
   cd signal-predictor/backend

2. Create virtual environment

   python -m venv venv
   # Windows
   venv\Scripts\activate
   # macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

4. Environment configuration

   # Create .env file in backend/ directory
   cp .env.example .env
   # Edit .env with your configuration

5. Database setup

   python manage.py makemigrations
   python manage.py migrate
   python manage.py createsuperuser

6. Start development server

   python manage.py runserver

⚛️ Frontend Setup (React SPA)

1. Navigate to frontend directory

   cd ../frontend

2. Install dependencies

   npm install

3. Start development server

   npm start

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8000/api/
   • Admin Panel: http://localhost:8000/admin/

🔌 API Endpoints

Authentication

• POST /api/auth/register/ - User registration with email verification
• POST /api/auth/login/ - User authentication
• POST /api/auth/logout/ - Session termination
• GET /api/auth/user/ - Current user profile
• POST /api/auth/change-password/ - Password modification
• POST /api/auth/password-reset/ - Password reset request
• POST /api/auth/password-reset-confirm/ - Password reset confirmation

Signal Analysis

• POST /api/upload/ - CSV file upload and analysis
• POST /api/evaluate/ - Function evaluation at specific points
• GET /api/analyses/ - List user's analyses
• GET /api/analyses/{id}/ - Retrieve specific analysis
• PATCH /api/analyses/{id}/ - Update analysis metadata
• DELETE /api/analyses/{id}/ - Delete analysis

Signal Generation

• POST /api/generator/ - Generate synthetic signals
• GET /api/generator/presets/ - Available generation presets

Sharing & Collaboration

• GET /api/analyses/{id}/share-options/ - Get sharing configuration
• POST /api/analyses/{id}/share-options/ - Update sharing settings
• GET /api/share/{id}/ - Access shared analysis
• POST /api/share/{id}/ - Access password-protected analysis

User Management

• GET /api/profile/ - User profile data
• PATCH /api/profile/ - Update profile information
• POST /api/profile/avatar/ - Upload profile picture

🛠️ Technology Stack

Backend Technologies

• Framework: Django 4.2.7 with Django REST Framework
• Database: PostgreSQL with psycopg2 adapter. Hosted on Neon.
• Authentication: Django's built-in auth + custom email verification
• File Storage: AWS S3 / Cloudflare R2 with django-storages
• Email: SMTP with HTML templates
• Task Queue: Django APScheduler for background tasks

Data Science & Analysis

• Signal Processing: NumPy, SciPy, scikit-learn
• Visualization: Matplotlib, Seaborn
• Data Handling: Pandas for CSV processing
• Machine Learning: Custom FFT analysis and curve fitting

Frontend Technologies

• Framework: React 19.1.0 with React Router
• Styling: Bootstrap 5 + Custom CSS with futuristic theme
• Icons: Lucide React + Font Awesome
• HTTP Client: Axios for API communication
• State Management: React Context API

Development & Deployment

• Environment: Python-decouple for configuration
• CORS: django-cors-headers for frontend integration
• Security: CSRF protection, secure headers
• Monitoring: Custom error handling and logging

🎨 User Interface

Modern Design Features

• 🌃 Futuristic Dark Theme: Custom-designed dark interface with neon accents
• ⚡ Smooth Animations: CSS3 transitions and hover effects
• 📱 Responsive Design: Optimized for desktop, tablet, and mobile
• 🎯 Intuitive Navigation: Clean, user-friendly interface
• 📊 Interactive Visualizations: High-quality charts and graphs
• 🔔 Toast Notifications: Real-time feedback for user actions

Key Components

1. Dashboard: Overview of recent analyses and statistics
2. Upload Interface: Drag-and-drop file upload with preview
3. Analysis Results: Comprehensive visualization suite
4. Signal Generator: Interactive parameter configuration
5. Profile Management: User settings and analysis history
6. Sharing Hub: Collaboration and export features

🔧 Configuration

Environment Variables

Create a .env file in the backend/ directory:

# Django Configuration
SECRET_KEY=your-secret-key-here
DEBUG=True
ALLOWED_HOSTS=localhost,127.0.0.1

# Database Configuration (PostgreSQL)
DB_NAME=signal_predictor
DB_USER=your-db-user
DB_PASSWORD=your-db-password
DB_HOST=localhost
DB_PORT=5432

# Email Configuration
EMAIL_BACKEND=django.core.mail.backends.smtp.EmailBackend
EMAIL_HOST=smtp.gmail.com
EMAIL_PORT=587
EMAIL_USE_TLS=True
EMAIL_HOST_USER=[email protected]
EMAIL_HOST_PASSWORD=your-app-password
DEFAULT_FROM_EMAIL=[email protected]

# Cloud Storage (AWS S3 / Cloudflare R2)
AWS_ACCESS_KEY_ID=your-access-key
AWS_SECRET_ACCESS_KEY=your-secret-key
AWS_STORAGE_BUCKET_NAME=your-bucket-name
AWS_S3_REGION_NAME=us-east-1
AWS_S3_ENDPOINT_URL=https://your-endpoint.com

# Frontend Configuration
FRONTEND_BASE_URL=http://localhost:3000

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Made with ❤️ by Ritesh Narayan Das

Advanced Signal Analysis & Prediction Platform • June 2025