Event-Driven Speckle Interrogation

• Event-driven speckle interrogation is a method that analyzes rapid changes in optical speckle patterns to detect minute physical events with high sensitivity.
• It integrates event-based sensing, statistical inference, and machine learning to enable real-time, high-resolution distributed measurements.
• This approach has been successfully applied in fiber-optic sensing, astronomical imaging, and noninvasive diagnostics, offering enhanced spatial and temporal precision.

Event-driven speckle interrogation encompasses a suite of modern methodologies in which information is extracted from the dynamical evolution of optical speckle patterns in response to physical “events”—including minute deformations, rapid dynamic activity, or signal transmission—often in real time and with minimal latency. Leveraging classical optical techniques, advanced statistical inference, high-throughput sensing, and machine learning, event-driven speckle interrogation has emerged as a powerful paradigm for distributed sensing, high-contrast imaging, and noninvasive measurement. Its implementations range from fiber-optic vibration detection and astronomical imaging to biomedical diagnostics, exploiting the sensitivity of speckle formation to perturbations and the ability to process high-dimensional, temporally-resolved optical data.

1. Principles of Speckle Sensitivity and Interrogation

Optical speckle arises when coherent light interacts with a system characterized by either random or structured scattering, inducing a complex interference field. The resultant speckle intensity $I(\mathbf{r}, t)$ at detector position $\mathbf{r}$ and time $t$ is governed by the superposition of many contributing modes, typically written as:

$$I(\mathbf{r}, t) = \left| \sum_{j=1}^N a_j(t) \psi_j(\mathbf{r}) e^{i\phi_j(t)} \right|^2$$

where $a_j(t)$ and $\phi_j(t)$ denote the amplitude and phase of the $j$th mode, and $\psi_j(\mathbf{r})$ its spatial distribution (Monteiro et al., 26 Sep 2025, Lopes et al., 26 Sep 2025).

A defining characteristic is the hypersensitivity of the speckle pattern to small changes—optical path length variations, local deformations, or incident wavefront modulations—driven by external or internal events. This sensitivity forms the physical basis of speckle interrogation: by monitoring speckle dynamics, one can infer and localize physical events with spatial and temporal precision.

In event-driven interrogation, the signal of interest corresponds either to abrupt changes in the speckle pattern (induced by an external event) or cumulative statistics over a sequence of events (e.g., atmospheric turbulence in astronomy, refractive index changes in fiber-sensors, or dynamic activity in material science).

2. Event-Based Sensing Paradigm: High-Speed and Data-Efficient Acquisition

Traditional speckle interrogation has been fundamentally constrained by the frame rate and throughput of standard cameras, limiting application bandwidth and latency. To overcome this, event-based vision sensors (EVS) have been introduced (Lopes et al., 26 Sep 2025). Unlike frame-based imaging, EVS asynchronously report per-pixel intensity changes surpassing a threshold; the sensor outputs a stream of “events,” each signaling a significant temporal transition in intensity. As a result:

• Temporal resolution: Sub-microsecond latencies, supporting high-bandwidth (MHz-range) optical interrogation,
• Dynamic range: Logarithmic pixel responses enable up to 120 dB dynamic range, mitigating pixel saturation and enhancing sensitivity,
• Data-rate scaling: Raw output scales with pattern activity, not frame size, focusing computational resources on change regions only.

Accumulating the event stream over short intervals enables direct mapping to intensity changes associated with localized deformations:

$$\Delta I = I(\delta) - I_0 \propto \mathrm{(number~of~events~in~}\Delta T)$$

where $\delta$ denotes the local deformation parameter.

This approach is fundamental for interrogating multimode fiber sensors or distributed optical fields in real time and is robust against the combinatorial data explosion typical of high-frame-rate imaging in high-dimensional speckle spaces.

3. Tensor-Based and Machine-Learning-Optimized Speckle Decoding

The core challenge in multi-point and distributed speckle interrogation is the separation and attribution of simultaneous, potentially overlapping events—e.g., multiple vibration sources along a fiber. This is addressed by constructing a tensor representation of the event streams and applying multi-point calibration. The theoretical model for small deformations is:

$$I(\{\delta_i\}) \approx I_0 + \sum_i \left(\frac{\partial I}{\partial \delta_i}\right) \delta_i$$

Each column of the Jacobian $\frac{\partial I}{\partial \delta_i}$ defines a characteristic spatial “mode” associated with perturbation $i$.

In practice, due to noise, modal overlap, and system ill-conditioning, direct pseudo-inverse decoding is insufficient. Hence, a data-driven linear layer (optimized, e.g., in PyTorch) is trained using a loss function:

$$\mathcal{L} = \sum_i \frac{ \mathrm{Var}(\delta_i^R(t) | \delta_i(t)=0) + \beta }{ \mathrm{Var}(\delta_i^R(t) | \delta_i(t) \neq 0) }$$

where $\delta_i^R(t)$ is the reconstructed time series, and $\beta$ regularizes for uniformity. This learning step yields optimal interrogation modes (OIMs) that maximize signal separation and minimize crosstalk, enabling simultaneous recovery of multiple event signals even in the “linear superposition” limit.

Machine learning also extends to unsupervised invariant feature extraction from speckle patterns (e.g., SURE—Speckle Unsupervised Recognition and Evaluation (Fan et al., 27 Sep 2024)), using contrastive or agglomerative clustering strategies to classify or detect changes from high-dimensional speckle data sequences without the need for labeled datasets.

4. Multimodal and Hybrid Sensing Architectures

Recent developments advocate the integration of multiple complementary interrogation modalities—exploiting both spatial and polarization degrees of freedom (Monteiro et al., 26 Sep 2025). In such multimodal systems:

• The speckle channel (e.g., via patterned light in MMF at 532 nm) localizes physical disturbances with centimeter-scale spatial precision but is constrained in temporal bandwidth (limited by camera or EVS rates).
• The polarization channel (e.g., SoP monitoring at 1550 nm) detects dynamic variations in the Stokes parameters due to birefringence changes, supporting up to a 40 kHz waveform-reconstruction bandwidth.

Fusion of these channels, via a two-stage sensor fusion architecture, decouples spatial localization from high-bandwidth temporal readout, effectively breaking resolution-bandwidth trade-offs in classical distributed sensing. Calibration routines and pseudo-inverse or learned decoding ensure consistent multimodal readout across a wide range of external perturbations.

5. Applications: Distributed Sensing, Imaging, and Noninvasive Diagnostics

Event-driven speckle interrogation enables high-throughput, real-time monitoring in several distributed and point-sensing scenarios:

• Fiber-optic distributed acoustic/vibration sensing: Distributed actuation along a fiber, with centimeter-scale spatial and kilohertz-scale temporal localization, suitable for infrastructure monitoring, acoustic surveillance, and multipoint biomedical diagnostics (Monteiro et al., 26 Sep 2025, Lopes et al., 26 Sep 2025).
• Astronomical imaging: In hypertelescope systems, event-driven speckle imaging (aided by bispectrum and triple-correlation algorithms) enables diffraction-limited reconstruction despite atmospheric turbulence (Surya et al., 2014).
• Noninvasive biological/fiber-optic communication: Unsupervised clustering techniques extract physiological (e.g., glucose concentration (Fan et al., 27 Sep 2024)) or information-encoded signals from dynamic speckle fields without requiring calibration labels.
• Industrial quality control and defect detection: Simulation-optimized interferometric speckle analysis supports automated defect identification, optimizing instrument parameters and feeding machine learning classifiers for series production (Plassmann et al., 1 Jul 2025).
• Dynamic process monitoring: Modified intensity-based dynamic speckle analysis with spatial/temporal averaging permits tracking of rapid, spatially varying dynamic phenomena (e.g., material drying processes) (Stoykova et al., 2023).

6. Experimental Implementation and Performance Considerations

Event-driven speckle interrogation demands synergistic hardware and algorithmic capabilities. Experiments demonstrate:

• Camera requirements: EVS with microsecond latency and high dynamic range, or high-speed CMOS/sCMOS for frame-based approaches where applicable.
• Data processing: Tensor streaming, calibration cycles (via known point actuators), and PyTorch-based linear decoding yield robust multi-point recovery.
• Separation and SNR: Reported experiments achieve separation of overlapping events (e.g., four actuator signals separated with minimal crosstalk over 400 Hz–20 kHz, localization accuracy ~100%) (Lopes et al., 26 Sep 2025, Monteiro et al., 26 Sep 2025).
• Integration time: Analytical models confirm that high SNR and temporal separation are maintained when the integration window is adapted to the fluctuation timescale of the monitored event.
• Application-specific challenges: Environmental noise (e.g., thermal drift in fiber sensors), nonlinear modal mixing, and spatial/temporal resolution trade-offs in event accumulation and averaging.

7. Challenges and Prospects

Despite demonstrated successes, several technical challenges and research frontiers remain:

• Robustness in complex environments: Sustaining performance amid environmental drift, modal cross-coupling, or fiber nonidealities.
• Scalability in spatial coverage: Extension from bench-scale (meters) to infrastructure-scale (tens–hundreds of meters), requiring re-evaluation of spatial calibration, sensitivity, and event superposition.
• Edge and real-time processing: Integrating event-driven logic, machine learning inference, and sensor fusion in edge devices for rapid, autonomous response.
• Hybrid signal and feature domains: Continued innovation in blending physical models (transmission matrices, modal decompositions) and data-driven feature extraction (contrastive unsupervised learning) for versatile, task-specific interrogation.
• Quantitative calibration: Nonlinear relationships between speckle fluctuations and underlying physical parameters (e.g., dynamic range, deformation amplitude) require ongoing work in algorithmic calibration and transferability across platforms.

Event-driven speckle interrogation thus establishes a foundation for versatile, adaptive sensing and analysis architectures applicable wherever temporally and spatially resolved optical signal extraction is essential, from astronomical imaging and distributed sensing to biomedical diagnostics and industrial automation (Surya et al., 2014, Lopes et al., 26 Sep 2025, Monteiro et al., 26 Sep 2025, Fan et al., 27 Sep 2024, Stoykova et al., 2023, Plassmann et al., 1 Jul 2025).