FreeScopes AI I - Description

FreeScopes AI I is an advanced configuration of the FreeScopes Radar Control Center, designed to provide hands-on training in Artificial Intelligence and Machine Learning. This module allows students to work through every phase of AI model development, from data collection and labeling to model optimization and deployment, all within the familiar FreeScopes environment.

The FreeScopes AI I software includes a customizable AI configuration and training panel, which integrates directly into the FreeScopes block diagram. Students can work with live or pre-recorded radar data, develop neural networks, and deploy them as operational blocks within the FreeScopes system. This ensures a smooth transition from theoretical learning to practical AI experimentation.

Users can create multiple projects, each focusing on different AI tasks, including data collection, preprocessing, model training, and real-time testing. The system supports the creation, validation, and optimization of classification neural networks, offering a robust AI training environment that operates alongside radar data processing.

FreeScopes AI I allows students to engage in AI-driven radar experiments, enhancing their understanding of how machine learning can optimize radar data processing. Here are some example exercises:

Capturing and labeling radar data to create a balanced dataset
Preprocessing data to remove noise, augment existing data, and ensure consistency
Developing and training neural networks for radar data classification
Optimizing AI models and conducting real-time testing within a radar environment
Deploying trained AI models as FreeScopes blocks and testing them in practical scenarios
Conducting radar experiments using AI-trained models to improve target detection and classification

AI - Panel

The AI training panel in FreeScopes AI I allows users to manage the complete AI workflow, from project creation to model deployment. It provides a user-friendly interface for students to:

Set up and configure radar data collection
Preprocess data for AI model development
Train and validate AI models
Deploy models directly within FreeScopes for real-time testing and experimentation

Data Collection & Labeling

Students can use the system to capture radar data, ensuring it is balanced and relevant for training purposes. The labeling process is semi-automated, allowing for faster dataset organization while still giving students control over the quality and accuracy of the labels.

Data Preprocessing

Students can clean the captured radar data by removing noise and irrelevant information, ensuring that the dataset is high-quality and consistent. The system also supports data augmentation, allowing students to expand the dataset by generating variations of the existing data. Additionally, data normalization ensures that all input data is properly scaled and formatted, optimizing it for AI model training. This process helps maintain the integrity of the data, leading to more accurate and reliable model performance.

Model Development

FreeScopes AI I supports multiple neural network architectures. Students can select, train, and monitor the progress of their models, ensuring they meet performance expectations. Hyperparameters can be adjusted to optimize model performance, and detailed evaluation tools are included to validate the effectiveness of the models

Model Optimization & Testing

Once a model is developed, students can further optimize it for specific radar tasks. Real-time testing environments allow students to validate their models before deployment, ensuring accuracy and reliability under operational conditions.

Model Integration & Deployment

Trained models can be exported as FreeScopes blocks, enabling them to be integrated into radar workflows. The system ensures that these AI models work seamlessly within the existing FreeScopes framework, allowing for comprehensive testing and operational use.