Evolvable Digital Pulse Shaper for Optimal Reconfiguration under Sensor Degradation

Core Concepts

An evolvable cusp-like digital pulse shaper that can autonomously adapt its parameters to maintain optimal performance under sensor degradation.

Abstract

The paper presents an evolvable cusp-like digital pulse shaper that can automatically reconfigure its parameters to maintain optimal performance when the input signal from the sensor degrades over time due to factors like high radiation levels, temperature cycles, and power supply issues.
The key highlights are:

The cusp-like digital shaper is defined by four parameters (k, l, m1, m2) that need to be configured based on the characteristics of the sensor input signal.

When the sensor degrades, the input signal changes, requiring the shaper parameters to be reconfigured. The authors implement an evolvable hardware (EHW) approach to autonomously evolve the shaper parameters to match the new input signal.

The EHW platform includes a configurable cusp-like digital shaper, a fitness evaluation module to assess the shaper performance, and a MicroBlaze processor running a genetic algorithm to evolve the shaper parameters.

Three different fitness functions are evaluated, with the cumulative error (F2) showing the best convergence times and hardware resource utilization.

Experiments using real data from a neutron monitor and synthetic data with severe signal degradation demonstrate the ability of the evolvable shaper to reliably reconfigure and restore the original signal characteristics.

The convergence times for the evolvable cusp-like shaper are 10 times faster than for a previously studied evolvable trapezoidal shaper, indicating the search space complexity is lower for the cusp-like design.

Stats

The peak voltage of the sensor output events is reduced due to sensor degradation.
The leakage current is increased, leading to higher serial and parallel noise at the preamplifier output.
The evolvable shaper was able to reconfigure the parameters (k, l, m1, m2) to restore the original signal characteristics, with a relative error less than 8% even under severe signal degradation.

Quotes

"The evolvable digital shaper may automatically optimize its parameters to reach the same output signal as the one obtained under the reference input signal."
"Results show that the shaper is reconfigured in less than 1 minute, and that the level of improvement is higher as the degeneration is increased."
"The evolution time for the cusp-like shaper is 10 times lower that for the trapezoidal shaper and, consequently, we have proved that the evolution times are strongly-dependant of the type of filter, although the optimal configuration is attained always."

Key Insights Distilled From

Real Time Evolvable Hardware for Optimal Reconfiguration of Cusp-Like Pulse Shapers

by Juan... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11592.pdf

Deeper Inquiries

How could the evolvable shaper be extended to handle multiple sensor inputs and dynamically adapt to changes in a multi-sensor system

To extend the evolvable shaper to handle multiple sensor inputs in a multi-sensor system, several modifications and enhancements can be implemented. Firstly, the system architecture would need to be adapted to accommodate data from multiple sensors, each with its own unique characteristics and degradation patterns. The evolvable hardware would need to be designed to dynamically adjust its parameters based on the inputs from these sensors.
One approach could involve creating a sensor fusion algorithm that combines data from multiple sensors before feeding it into the evolvable shaper. This algorithm would need to preprocess and normalize the data from each sensor to ensure compatibility with the shaper's input requirements. The evolvable hardware would then optimize its parameters to generate an output that best fits the combined sensor data.
Additionally, the evolvable shaper could be equipped with a mechanism to prioritize sensor inputs based on their reliability or importance. This would allow the system to adapt to changes in sensor quality or availability, ensuring that the most critical data is processed accurately.
Furthermore, the evolvable shaper could incorporate feedback mechanisms to continuously monitor the performance of the system and adjust its parameters in real-time. This feedback loop would enable the system to self-optimize and adapt to evolving sensor conditions, making it more resilient in a multi-sensor environment.

What other types of digital filters or signal processing algorithms could benefit from an evolvable hardware approach, and how would the search space complexity and convergence times compare

Various types of digital filters and signal processing algorithms could benefit from an evolvable hardware approach, especially in scenarios where the input data is subject to degradation or variability. One such example is adaptive filters, such as the Least-Mean-Square (LMS) or Wiener algorithms, which are commonly used in signal processing applications.
The search space complexity and convergence times of evolvable hardware compared to traditional optimization methods would depend on the specific algorithm and problem domain. In general, evolvable hardware offers the advantage of parallel processing and real-time adaptation, which can lead to faster convergence times in dynamic environments. However, the search space complexity may increase with the number of parameters to be optimized, potentially impacting convergence speed.
For complex signal processing algorithms with high-dimensional parameter spaces, evolvable hardware may offer a more efficient and adaptive solution compared to traditional optimization techniques. By leveraging evolutionary algorithms and reconfigurable hardware, the system can dynamically adjust its parameters to changing input conditions, leading to improved performance and robustness.

Could the evolvable shaper be integrated with other self-healing or fault-tolerant techniques to create a more comprehensive resilient system for harsh environments

Integrating the evolvable shaper with other self-healing or fault-tolerant techniques can enhance the resilience of the system in harsh environments. One approach could be to combine the evolvable hardware with redundancy mechanisms, such as error detection and correction codes, to ensure data integrity and reliability.
By incorporating self-healing capabilities into the evolvable shaper, the system can automatically detect and recover from faults or errors in the hardware or sensor inputs. This could involve implementing error recovery algorithms that reconfigure the hardware in response to anomalies or failures, ensuring continuous operation in challenging conditions.
Furthermore, integrating the evolvable shaper with adaptive fault-tolerant strategies, such as dynamic reconfiguration or hot-swapping of components, can enhance the system's ability to withstand environmental stressors. By proactively adapting to changing conditions and mitigating potential failures, the system can maintain optimal performance and reliability in harsh environments.