arXiv subjects

Orestis Kaparounakis

Publications and source records attributed to Orestis Kaparounakis.

5 recordsLinked to original sources

arXiv2026

Dynamic Online Processor-Native Inference for State Estimation

Sensor-rich data-driven applications increasingly use Bayesian approaches to infer latent states of dynamic systems from noisy sensor measurements and physical models. Yet the computation of the likelihood remains an essential bottleneck for accurate posteriors and performant inference. This paper presents a Bayesian filtering technique that uses processor-native uncertainty tracking for both uncertainty propagation and inference. The technique implements deterministic hierarchical importance restructuring through a native operation, giving deterministic latency and bounded memory use for arbitrary models written as program code. Benchmarks across three nonlinear state-space systems compare the approach against particle filters and Monte-Carlo-based likelihood estimators. The technique enables deterministic approximate filtering with as high as 805$\times$ average speedup against direct Monte Carlo work at matched result quality for model evaluation, and Pareto-dominant accuracy-latency trade-offs for posterior inference while remaining competitive in RMSE with baseline particle filters.

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arXiv2026

Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering

Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, with further applications in speech processing and medicine (patient monitoring). Yet, their inherent weakness at representing the likelihood of the observation (which often leads to particle degeneracy) remains unaddressed for real-time resource-constrained systems. Improvements such as the optimal proposal and auxiliary particle filter mitigate this issue under specific circumstances and with increased computational cost. This work presents a new particle filtering method and its implementation, which enables tunably-approximative representation of arbitrary likelihood densities as program transformations of parametric distributions. Our method leverages a recent computing platform thatcan perform deterministic computation on probability distributionrepresentations (UxHw) without relying on stochastic methods. For non-Gaussian non-linear systems and with an optimal-auxiliary particle filter, we benchmark the likelihood evaluation error and speed for a total of 294840 evaluation points. For such models, the results show that the UxHw method leads to as much as 37.7x speedup compared to the Monte Carlo alternative. For narrow uniform measurement uncertainty, the particle filter falsely assigns zero likelihood as much as 81.89% of the time whereas UxHw achieves 1.52% false-zero rate. The UxHw approach achieves filter RMSE improvement of as much as 18.9% (average 3.3%) over the Monte Carlo alternative.

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arXiv2026

Digital Methods to Quantify Sensor Output Uncertainty in Real Time

Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characterization of sensor output uncertainty is important for reliable data interpretation. Here, we present a method for real-time on-device dynamic uncertainty quantification for sensor outputs which depend on pre-stored calibration data. We show how sensor calibration compensation equations (essential in advanced sensing systems) propagate uncertainties resulting from the quantization of calibration parameters to the sensor output. We use a low-cost thermal sensor as a motivating example and show these ideas are practical and possible on actual embedded sensor systems by prototyping them on two commercially-available uncertainty tracking hardware platforms. One has average power dissipation 16.7 mW and achieves 42.9x speedup compared to the equal-accuracy Monte Carlo computation (the status quo), and the other 147.15 mW and achieves 94.4x speedup. We present a proof-of-usefulness application using the quantified uncertainty in edge detection over ten test scenes where we show accuracy and precision average improvement by 4.97 and 40.25 percentage points, respectively, trading off sensitivity. Another application example examines uncertainty quantification for four different calibration-data storage scenarios and compute that a 48% increase in memory yields 75% smaller uncertainty metrics over the baseline.

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arXiv2024

Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty

Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of ICA, the effect of measurement uncertainty on the artifact removal process has not been thoroughly investigated. We first characterize the measurement uncertainty distribution of a common ADC and show that it quantitatively conforms to a Gaussian distribution. We then evaluate the effect of measurement uncertainty on the artifact identification process through several computer simulations. These computer simulations evaluate the performance of two different ICA algorithms, FastICA and Infomax, in removing eyeblink artifacts from five different electrode configurations with varying levels of measurement uncertainty. FastICA and Infomax show similar performance in identifying the eyeblink artifacts for a given uncertainty level and electrode configuration. We quantify the correlation performance degradation with respect to SNR and show that in general, an SNR of greater than 15 dB results in less than a 5% degradation in performance. The biggest difference in performance between the two algorithms is in their execution time. FastICA's execution time is dependent on the amount of measurement uncertainty, with a 50% to 85% reduction in execution time over an SNR range of 20 dB. This contrasts with Infomax's execution time, which is unaffected by measurement uncertainty.

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arXiv2020

Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation

We present a new method for automatically generating the implementation of state-estimation algorithms from a machine-readable specification of the physics of a sensing system and physics of its signals and signal constraints. We implement the new state-estimator code generation method as a backend for a physics specification language and we apply the backend to generate complete C code implementations of state estimators for both linear systems (Kalman filters) and non-linear systems (extended Kalman filters). The state estimator code generation from physics specification is completely automated and requires no manual intervention. The generated filters can incorporate an Automatic Differentiation technique which combines function evaluation and differentiation in a single process. Using the description of physical system of a range of complexities, we generate extended Kalman filters, which we evaluate in terms of prediction accuracy using simulation traces. The results show that our automatically-generated sensor fusion and state estimation implementations provide state estimation within the same error bound as the human-written hand-optimized counterparts. We additionally quantify the code size and dynamic instruction count requirements of the generated state estimator implementations on the RISC-V architecture. The results show that our synthesized state estimation implementation employing Automatic Differentiation leads to an average improvement in the dynamic instruction count of the generated Kalman filter of 7%-16% compared to the standard differentiation technique. This is improvement comes at the limited cost of an average 4.5% increase in the code size of the generated filters.

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