Projects & Publications
Selected work in signal processing, statistical modeling, and applied research.
This page presents selected academic and clinical research work that demonstrates the methodological foundation behind TS-Analytics, including signal processing, inverse problems, Bayesian inference, statistical analysis, and reproducible computational workflows.
The listed projects and publications were conducted in previous academic and clinical research roles at TU Graz and Medical University of Graz.
Selected Work
Selected examples illustrate how methods from engineering, statistics, and computational analysis were applied to complex measurement, clinical, and research data.
Inverse Capacitive Flow Metering in Industrial
Pneumatic Conveying Processes
Academic research · TU Graz · PhD Thesis
This doctoral research addressed non-invasive flow measurement in horizontal pneumatic conveying systems, where particle distributions and velocity fields are spatially inhomogeneous and difficult to measure directly.
Model-based signal processing and Bayesian inverse problem methods for electrical capacitance tomography were developed, including application-specific prior models, sensor cross-sensitivity compensation, uncertainty analysis, particle velocity estimation, and mass flow rate evaluation.
Role: Doctoral research, method development, and first-author publications.
Signal Processing · Inverse Problems · Bayesian Estimation · Measurement Data · Uncertainty Analysis · Sensor Modeling
Bayesian Meta-Analysis of NIRS-Guided Neonatal Resuscitation
Clinical research · Medical University of Graz
This project evaluated whether interventions guided by cerebral oxygen saturation monitoring during neonatal resuscitation were associated with improved clinical outcomes in preterm infants.
The analysis used individual patient data from randomized studies and applied a Bayesian fixed-effect meta-analysis with binomial likelihoods, weakly informative priors, posterior probability estimates, credible intervals, and probability-of-treatment-benefit interpretation.
Role: Bayesian analysis, statistical implementation, posterior inference, visualization, and contribution to interpretation and manuscript preparation.
Bayesian Meta-Analysis · Clinical Outcomes · Posterior Inference · Probabilistic Interpretation · Medical Statistics
Bayesian estimation of discrete system states from high-dimensional data
Academic research · TU Graz
This project focused on estimating discrete system states from high-dimensional measurement data, where the original 56-dimensional measurement vector required dimensionality reduction before probabilistic interpretation.
The method combined PCA-based feature extraction with Gaussian mixture models in the reduced principal-component space and a Hidden Markov Model for sequential Bayesian state estimation, enabling robust state inference from noisy and complex measurement patterns.
Role: Method development, and implementation of the state-estimation workflow.
Bayesian Inference · Hidden Markov Models · Gaussian Mixture Models · PCA · State Estimation · High-Dimensional Data · Probabilistic Modeling
Automated Quantitative Analysis of Lung Ultrasound Video Loops
Clinical research · Medical University of Graz
This project focused on quantitative evaluation of neonatal lung ultrasound video data, where conventional visual scoring may miss subtle grayscale and artefact-pattern differences related to respiratory status.
The workflow used manually defined polygonal regions of interest in raw ultrasound video loops and a MATLAB-based analysis framework to extract grayscale and heterogeneity metrics across frames, enabling objective assessment of lung aeration patterns and temporal variability.
Role: MATLAB workflow implementation, statistical evaluation, and contribution to methodological interpretation.
MATLAB · Video Analysis · Medical Imaging · Automated Workflows · Feature Extraction · Statistical Analysis
Inverse Capacitive Flow Metering in Industrial
Pneumatic Conveying Processes
Academic research · TU Graz · PhD Thesis
This doctoral research addressed non-invasive flow measurement in horizontal pneumatic conveying systems, where particle distributions and velocity fields are spatially inhomogeneous and difficult to measure directly.
Model-based signal processing and Bayesian inverse problem methods for electrical capacitance tomography were developed, including application-specific prior models, sensor cross-sensitivity compensation, uncertainty analysis, particle velocity estimation, and mass flow rate evaluation.
Role: Doctoral research, method development, and first-author publications.
Signal Processing · Inverse Problems · Bayesian Estimation · Measurement Data · Uncertainty Analysis · Sensor Modeling
Bayesian Meta-Analysis of NIRS-Guided Neonatal Resuscitation
Clinical research · Medical University of Graz
This project evaluated whether interventions guided by cerebral oxygen saturation monitoring during neonatal resuscitation were associated with improved clinical outcomes in preterm infants.
The analysis used individual patient data from randomized studies and applied a Bayesian fixed-effect meta-analysis with binomial likelihoods, weakly informative priors, posterior probability estimates, credible intervals, and probability-of-treatment-benefit interpretation.
Role: Bayesian analysis, statistical implementation, posterior inference, visualization, and contribution to interpretation and manuscript preparation.
Bayesian Meta-Analysis · Clinical Outcomes · Posterior Inference · Probabilistic Interpretation · Medical Statistics
Bayesian estimation of discrete system states from high-dimensional data
Academic research · TU Graz
This project focused on estimating discrete system states from high-dimensional measurement data, where the original 56-dimensional measurement vector required dimensionality reduction before probabilistic interpretation.
The method combined PCA-based feature extraction with Gaussian mixture models in the reduced principal-component space and a Hidden Markov Model for sequential Bayesian state estimation, enabling robust state inference from noisy and complex measurement patterns.
Role: Method development, and implementation of the state-estimation workflow.
Bayesian Inference · Hidden Markov Models · Gaussian Mixture Models · PCA · State Estimation · High-Dimensional Data · Probabilistic Modeling
Automated Quantitative Analysis of Lung Ultrasound Video Loops
Clinical research · Medical University of Graz
This project focused on quantitative evaluation of neonatal lung ultrasound video data, where conventional visual scoring may miss subtle grayscale and artefact-pattern differences related to respiratory status.
The workflow used manually defined polygonal regions of interest in raw ultrasound video loops and a MATLAB-based analysis framework to extract grayscale and heterogeneity metrics across frames, enabling objective assessment of lung aeration patterns and temporal variability.
Role: MATLAB workflow implementation, statistical evaluation, and contribution to methodological interpretation.
MATLAB · Video Analysis · Medical Imaging · Automated Workflows · Feature Extraction · Statistical Analysis
Methodological Focus
Across different application areas, the selected work is connected by a common methodological foundation.
Signal Processing →
Analysis and processing of measurement, sensor, image, video, and time-series data.
Modeling & Inference →
Model-based estimation, Bayesian methods, inverse problems, and probabilistic interpretation.
Statistical Analysis →
Statistical analysis including uncertainty evaluation, method comparison, and performance assessment.
Automated Workflows →
Reproducible analysis pipelines, automated reporting, and project-specific tools
Scientific Support →
Clear method descriptions, result summaries, publication figures, and transparent documentation.
Selected Publications
Selected peer-reviewed publications and research contributions are listed below. A complete publication record is available via Google Scholar and ORCID.
Engineering & Measurement Science
M. Neumayer, T. Suppan, T. Bretterklieber et al., “Fast numerical techniques for FE simulations in electrical capacitance tomography ,” in COMPEL, vol. 42(5), 2023
Contribution: Support in method development and implementation.
DOI · Publisher · Google Scholar
T. Suppan, M. Neumayer, T. Bretterklieber et al., “Electrical capacitance tomography-based estimation of slug flow parameters in horizontally aligned pneumatic conveyors,” in Powder Technology, vol. 420, 2023
Contribution: Method development, experimental measurements, data analysis, and manuscript preparation
DOI · Publisher · Google Scholar
T. Suppan, M. Neumayer, T. Bretterklieber et al., “Thermal Drifts of Capacitive Flow Meters: Analysis of Effects and Model-Based Compensation,” in IEEE Transactions on Instrumentation and Measurement, vol. 70, 2021
Contribution: Method development, experimental measurements, data analysis, and manuscript preparation
DOI · Publisher · Google Scholar
M. Neumayer, T. Suppan, T. Bretterklieber et al., “Statistical solution of inverse problems using a state reduction ,” in COMPEL, vol. 38(5), 2019
Contribution: Support in method development and implementation.
DOI · Publisher · Google Scholar
T. Suppan, M. Neumayer, T. Bretterklieber et al., “Prior design for tomographic volume fraction estimation in pneumatic conveying systems from capacitive data,” in Transactions of the Institute of Measurement and Control, vol. (42)4, 2019
Contribution: Method development, experimental measurements, data analysis, and manuscript preparation
DOI · Publisher · Google Scholar
Clinical Research & Applied Statistics
M. Winkler, T. Suppan, A. Seigner et al., “Introduction of a video-based heterogeneity index for quantitative lung ultrasound assessment in neonates with respiratory distress – a proof-of-concept study,” in Paediatric Respiratory Reviews, vol 58, 2026
Contribution: Role: MATLAB workflow implementation, Statistical methodology, contribution to the methods section
DOI · Publisher · Google Scholar
S. Kurath-Koller, D. Scherr, N. Oeffl et al., “Feasibility and diagnostic agreement of Apple Watch and KardiaMobile electrocardiograms compared with standard 12-lead ECG in children,” in Scientific Reports, 2025
Contribution: Statistical methodology
DOI · Publisher · Google Scholar
M. Bruckner, T. Suppan, E. Suppan et al., “Brain oxygenation monitoring during neonatal stabilization and resuscitation and its potential for improving preterm infant outcomes: a systematic review and meta-analysis with Bayesian analysis,” in European Journal of Pediatrics, vol 184 (305), 2025
Contribution: Statistical methodology, model development, contribution to the methods section
DOI · Publisher · Google Scholar
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