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Innovative reflectometric platform Aura Ai-X

January 15, 2025
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The field of security systems is actively transforming, transitioning from traditional technologies to intelligent solutions.

In standard conditions, the system demonstrates reliability of up to 98%, maintaining high efficiency even in complex environments. The Aura Ai-X architecture allows for real-time threat classification on perimeters up to 80 km in length, using only one controller.

FFT Aura Ai-X: Engineering Analysis of a Perimeter Security System with Deep Learning

The effectiveness of any perimeter intrusion detection system is measured by two critical parameters:

Probability of detection (POD):

Definition: The system’s reliability in detecting actual intrusion attempts

Influencing factors:

Percentage of successfully detected intrusion attempts

Increased level of facility protection

Optimized integration with other security systems

Industry average: 80-85%

Nuisance Alarm Rate (NAR):

Definition: The system’s ability to distinguish between real threats and natural factors

Influencing factors:

Percentage of alarms not related to actual intrusion attempts

Optimal NAR provides more reliable protection of the object

High NAR reduces operator vigilance due to constant false signals

Industry average: 15-20%

Limitations of traditional security systems

Problem of threshold sensitivity

Traditional systems rely on threshold sensitivity settings, trying to establish a sensitivity level between values of real events and natural disturbances. This approach leads to a high level of false alarms, reaching 20%.

Consequences:

Approximately one-fifth of all alarms are false

Decreased operator efficiency

Risk of missing real threats is increased

Benefits of deep learning

The Aura Ai-X system represents a fundamentally new approach to perimeter security through the use of deep learning technology.

Deep learning architecture

Key difference lies in implementing multi-layered artificial neural networks for input data analysis:

1.

Data collection:

Fiber optic sensors detect vibrations and disturbances along the perimeter

These disturbances create slight changes in light patterns inside the fiber

2.

Signal processing:

The system converts optical signals into digital data

Initial processing occurs to improve signal quality and eliminate noise

3.

Deep learning analysis:

Neural networks analyze patterns in data

The system classifies events based on learned patterns

Alerts are generated only for real threats, while natural factors are filtered

4.

Continuous improvement:

The system learns from new data over time

Regular updates improve detection accuracy

Integration and calibration of Aura Ai-X

During the implementation phase, specific training of the Aura Ai-X system is conducted, taking into account the unique characteristics of each facility. The system is supplemented with specific information about the facility before deployment, ensuring high probability of detection (POD).

1.

Data collection:

Data from a large number of global FFT system installations is used to train effective deep learning models

Site-specific data is entered for configuration

2.

Training infrastructure:

FFT maintains a comprehensive data library from global installations

This library trains specialized deep learning models

3.

Deployment:

Trained models are deployed in Aura Ai-X systems through encrypted file transfer

The deep learning engine performs real-time detection and classification

4.

Event processing:

Fiber optic sensor tracks events along the perimeter

The system classifies these events with high accuracy

This leads to high POD with minimal false alarms

Cloud platform FFT ATLAS

Optional subscription to the cloud-based modeling system FFT ATLAS provides significant advantages:

Annual updates to the event classification library, which include data from thousands of installations worldwide

Regular cybersecurity updates, which provide real-time updates for all system software

Comprehensive maintenance program, which offers corrective and preventive maintenance, ensures uninterrupted system operation, reduces maintenance costs, and minimizes risks

Technical comparison with traditional systems

Parameter Aura Ai-X Traditional systems
Detection algorithm Deep learning neural networks Threshold sensitivity
POD Up to 96% 80-85%
NAR Up to 2% 15-20%
Location accuracy ±2 meters ±4-5 meters
Continuous improvement Yes, through ATLAS Limited
Environmental adaptability High Low to moderate

System variants

Parameter Aura Ai-X Aura Ai-XS
Application Large facilities and critical infrastructure Smaller facilities and installations
Coverage Up to 80 km for fence installations, 110 km for underground applications Up to 10 km with high accuracy and reliability
Ideal for Large areas requiring high precision and reliability Small facilities, up to 5 km per channel
Detection accuracy ±2m (on fence), ±5m (hidden underground) –
Form factor Compact 4RU block Compact 4RU block

Technical specifications

Real-time simultaneous detection on two channels

Damage resistance (immunity) and redundancy

No electronics or power sources in field conditions

Intrinsically safe / immune to electromagnetic, radio frequency interference and lightning

Compact (4RU) modern optoelectronics

Lower total cost of ownership compared to alternative technologies

Penetration tested against cyber attacks

Two-year warranty and mean time between failures (MTBF) >250,000 hours

Cost effectiveness

Implementation of the Aura Ai-X system ensures a quick return on investment due to:

Minimization of operational costs

Improved operator efficiency

Reduced maintenance costs

Extended operational lifetime

Conclusion

The system FFT Aura Ai-X represents a revolutionary approach to perimeter security, combining advanced artificial intelligence technologies with the reliability of fiber optic solutions. The system establishes a new industry standard for critical infrastructure protection, providing an unprecedented level of security and operational efficiency.

Focusing on two key metrics that truly matter — Probability of Detection (POD) and Nuisance Alarm Rate (NAR) — the Aura Ai-X system delivers superior performance where it counts, radically changing the traditional POD versus NAR paradigm by reducing false alarms virtually to zero, while maintaining detection rates above 95%.

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