1 citations · 1 across the 4 of their papers we have counts for
5 papers
Dynamic Online Processor-Native Inference for State Estimation
Orestis Kaparounakis
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 comp…
Approximating Analytically-Intractable Likelihood Densities with Deterministic Arithmetic for Optimal Particle Filtering
Orestis Kaparounakis, Yunqi Zhang, Phillip Stanley-Marbell
Particle filtering algorithms have enabled practical solutions to problems in autonomous robotics (self-driving cars, UAVs, warehouse robots), target tracking, and econometrics, wi…
Digital Methods to Quantify Sensor Output Uncertainty in Real Time
Orestis Kaparounakis, Phillip Stanley-Marbell
Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characte…
Simulated Eyeblink Artifact Removal with ICA: Effect of Measurement Uncertainty
Jennie Couchman, Orestis Kaparounakis, Chatura Samarakoon +1
Independent Component Analysis (ICA) is commonly-used in electroencephalogram (EEG) signal processing to remove non-cerebral artifacts from cerebral data. Despite the ubiquity of I…
Automated Physics-Derived Code Generation for Sensor Fusion and State Estimation
Orestis Kaparounakis, Vasileios Tsoutsouras, Dimitrios Soudris +1
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…