Library · Signals, sensing & imaging
What Digital Filtering & Signal Denoising got wrong, from 67 theses
Summary written by a model from the records below: Digital filtering and signal denoising methods often degrade performance when operations induce signal distortion, phase delay, or numerical instability. Across diverse applications, complex filters and state-space estimators frequently underperform simpler baselines or corrupt essential signal dynamics. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Denoising pipelines and complex filters underperform simple baselines or raw signals
Advanced denoising algorithms and linear smoothers frequently achieve worse performance than median filtering, ensemble averaging, or training directly on raw unfiltered data. In multiple tasks, filters mistook geological structures or valid peaks for noise while smoothing operations tracked outliers and degraded downstream accuracy.
Tried and failed
single-trial LSTM prediction applied to somatosensory evoked potential estimation. Outcome: worse than baseline. Reason: high electroencephalogram noise prevented statistically significant improvement over stimulus-averaged baselines
Neurological Disease Diagnosis and Treatment via Precise Robotic Intervention · Georgia Tech · PhD
Tried and failed
spectral subtraction and Wiener filtering for denoising applied to transient experimental flow measurements. Outcome: worse than baseline. Reason: Provided no practical improvement over simple ensemble averaging across repetitions.
Vortex formation downstream of an active vane vortex generator · Cranfield · PhD
Tried and failed
standard SVM kernels for cross-correlation applied to weak signal detection in noise. Outcome: worse than baseline. Reason: underperformed compared to linear matched filtering and standard cross-correlation baselines
Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm · Virginia Tech · Master's
Tried and failed
deep learning denoising preprocessing before phase picking applied to noisy seismic time series data. Outcome: worse than baseline. Reason: downstream deep learning models were trained on raw, unfiltered data rather than pre-denoised waveforms
Surface deformation and seismicity at Cerro Pabellón and Cordón Caulle (Chile): Insights from geothermal production and volcanic eruption · Cornell · PhD
Tried and failed
thresholding outliers via moving mean and variance applied to noisy time-series sensor data. Outcome: worse than baseline. Reason: failed to reliably distinguish true signal peaks from measurement outliers compared to median filtering
QUANTIFYING TURBIDITY IN THE NEARSHORE OCEAN · Naval Postgraduate School · Master's
Tried and failed
moving average filtering applied to power spectral density estimation. Outcome: worse than baseline. Reason: it over-smoothed spectral peaks compared to ensemble dataset averaging
Wind Tunnel Testing to Evaluate Noise Emissions from a Small Wind Turbine · Carleton University · Master's
Tried and failed
moving average or polynomial smoothing filters applied to time-series data with outlier noise. Outcome: worse than baseline. Reason: linear smoothing filters track outliers instead of rejecting them, degrading parameter estimation accuracy
Composite load modeling for power systems: Model reduction, identification, and application to conservation voltage reduction · Iowa State · PhD
Lost to a baseline
TDTV lost to NPT on field seismic denoising because it over-compensated for noise-induced signal loss and heavily distorted the seismic image by retaining excessive non-noise data in the extracted noise difference.
Developing Deep Learning Approaches towards Automatic Seismic Fault Interpretation · University College Dublin · PhD
Lost to a baseline
SNN filter achieved lower F1 score (0.771 on PD1, 0.665 on PD2) than moving average (0.84) and Gaussian (1.0) filtering in LPBF anomaly detection.
A Neuromorphic Approach towards Detection and Control of Features of Interest in Dynamical Systems · University College Dublin · PhD
Lost to a baseline
On the Beatrice field dataset denoising task, BM3D, MFFCNN, ASPP, MPRNet, and DnCNN lost to NPT by misinterpreting noise as geological signal and retaining it in the output.
Developing Deep Learning Approaches towards Automatic Seismic Fault Interpretation · University College Dublin · PhD
Lost to a baseline
Piecewise filtered Fourier reconstruction (mean SSIM 0.83302) lost to unfiltered Fourier baseline (mean SSIM 0.90462) and filtered Fourier (mean SSIM 0.90831) on the 2D synthetic dataset.
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected mean filter for displacement preprocessing in favor of a median filter because mean filtering blurred adjacent pixels, whereas median filtering preserved local wavefield details across 1/8 wavelength windows.
Application of optical coherence elastography to assess mechanical properties of the anterior cerebral artery · Boston University · Master's
Lost to a baseline
STRAY algorithm with Savitzky-Golay/Gaussian filtering lost to unfiltered STRAY on clean Dataset 2 photodiode data (unfiltered BD F1 0.92 vs smoothed F1 0.63-0.79).
In-situ process monitoring of laser powder bed fusion for laser parameter optimisation of Ti-6Al-4V alloy parts with overhang features · University College Dublin · PhD
Lost to a baseline
Gegenbauer reconstruction (SSIM 0.85003) and Piecewise filtered Fourier (SSIM 0.84592) lost to unfiltered Fourier baseline (SSIM 0.91128) and filtered Fourier (SSIM 0.92145) on 2D MRI datasets with estimated edges.
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected Savitsky-Golay noise-reduction filtering, opting to feed raw multi-channel temporal sensor data directly into the 1D-CNN since 1D-CNNs natively learn temporal features from raw signals.
BAYESIAN CONVOLUTIONAL NEURAL NETWORKS FOR ANOMALY DETECTION IN POWER SYSTEMS · Naval Postgraduate School · Master's
Filtering distorts essential waveform morphology and attenuates dynamic signal features
Aggressive low-pass, high-pass, or moving average filtering frequently erases critical high-frequency kinetics, electrogram amplitudes, and low-frequency physiological trends. These operations over-smooth meaningful signal variations and introduce severe waveform distortion that degrades diagnostic and anomaly detection tasks.
Tried and failed
Kalman filtering with signal quality indices applied to physiological time series denoising. Outcome: worse than baseline. Reason: Denoising removed motion and noise patterns that were informative and correlated with the target event
Self-Aware Machine Learning for Chronic Pathology Monitoring on Wearable Devices · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected conventional Butterworth filtering for limb EMG line noise removal in favor of a time-domain sine subtraction method to avoid distorting mEP waveform morphology.
Optimizing Brain Stimulation for Parkinson's Disease, Memory Enhancement, and Optogenetic Control · Georgia Tech · PhD
Tried and failed
broadband filtering for thermal noise reduction applied to time-domain transient electrical signals. Reason: filtering distorted and attenuated the signal due to spectral overlap between noise and fast pulse transients
Time, momentum, spin, and energy resolved tunneling spectrum of a two-dimensional electron system · MIT · PhD
Tried and failed
digital audio bleed removal filtering applied to acoustic piano microphone recordings. Reason: the denoising processing introduced unacceptable audio distortion into the primary piano signal
Dearly belated : recorded concept album for mixed chamber ensemble, rock band, vocal trio, chorus, and electronics · UT Austin · Doctorate
Tried and failed
brick-wall frequency filtering for displacement estimation applied to tissue motion ultrasound time series. Outcome: worse than baseline. Reason: removes low-frequency physiological movement data, degrading measurement repeatability
Radiomic and Image-Based Biomechanical Approaches for Sonographic Evaluation of the Thoracolumbar Fascia in Patients With Mechanical Low Back Pain · Virginia Tech · Doctorate
Tried and failed
frequency domain filtering to suppress low frequencies applied to image sensor noise artifact extraction. Outcome: worse than baseline. Reason: filtering removed critical low-frequency camera artifact signals, degrading performance to random guessing
NoiseLearner: An Unsupervised, Content-agnostic Approach to Detect Deepfake Images · Virginia Tech · Master's
Considered and rejected
Considered and rejected: Rejected aggressive signal pre-filtering prior to the inverse solution because it distorts reconstructed electrogram features compared to ground truth.
Feasibility of improving risk stratification in the inherited cardiac conditions · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected relying purely on digital high-pass filtered data (0.005 Hz cutoff) for characterizing spreading depolarizations because filtering distorts event duration and morphology on long-duration events, requiring reversion to unfiltered DC recordings
Quantifying Biomarkers for Brain Disease State Monitoring and Intervention · Duke · Doctorate
Considered and rejected
Considered and rejected: Rejected conventional analog low-pass filtering because it removes high-frequency kinetic and mechanistic components of single-entity events; adopted digital band-stop filtering a posteriori instead.
Advances in Single Entity Electrochemistry for Semiconducting Nanocrystal Studies · University of Nevada, Reno · Doctorate
Considered and rejected
Considered and rejected: Aggressive Butterworth filtering (critical freq 0.05) rejected for general preprocessing because it distorted underlying EGM morphology and amplitudes
Predicting electrophysiological function of ex-vivo hearts using machine learning · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected temporal downsampling/filtering because it removes high-frequency components and distorts underlying causal structure
Biomarker discovery and statistical modeling with applications in childhood epilepsy and Angelman syndrome · Boston University · PhD
Considered and rejected
Considered and rejected: Rejected Savitzky-Golay filtering and Bruker baseline correction because they over-smoothed or failed to remove IR artifacts, selecting Whittaker smoothing instead.
Novel reaction discovery with rapid high-throughput experimentation via infrared spectroscopy and enzymatic electrochemical oxidation of alcohols · Boston University · PhD
Considered and rejected
Considered and rejected: Rejected applying Savitzky-Golay noise filtering because it smoothed out real anomalies, added computational cost, and increased latency.
ANOMALY DETECTION IN A MICROGRID USING MACHINE LEARNING METHODS TO ENHANCE POWER SECURITY · Naval Postgraduate School · Master's
Kalman and state-space filters fail under non-linearities and covariance misspecification
Linear and extended Kalman filters struggle when confronted with nonlinear dependencies, non-Gaussian noise, or poorly tuned process noise covariance values. These estimation errors lead to oversmoothed dynamic peaks, degraded tracking relative to simple sliding averages, and filter instability.
Tried and failed
unscented Kalman filter for motion state estimation applied to pedestrian multi-object tracking. Outcome: worse than baseline. Reason: nonlinear filtering degraded tracking accuracy compared to standard linear Kalman filter models
Improved 2D Camera-Based Multi-Object Tracking for Autonomous Vehicles · Virginia Tech · Master's
Lost to a baseline
Classic Kalman filtering (1 GMM component) failed to resolve nonlinear/non-Gaussian parabolic joint dependencies between state variables during data assimilation compared to the GMM-DO filter.
Lost to a baseline
Multivariate linear Gaussian state-space model filtering underperformed Kalman filter (test MSE 0.46 vs 0.2846).
Some statistical contributions to deep learning · Boston University · PhD
Lost to a baseline
Misspecified linear Kalman filter yielded higher correlations but worse relative standard deviations in non-linear state space setups compared to the partial information filter.
ESSAYS ON MACRO FINANCE · Penn · PhD
Lost to a baseline
In Scenario 2 linear motion preliminary tests, a simple sliding average filter (window length w=100) outperformed EKF in smoothing ALG. B outputs.
Towards Precision Positioning For Smart Logistics Using Ultra Wide-Band Systems and LEO Satellite-Based Technologies · University of Vaasa · PhD
Lost to a baseline
Kalman filter prediction method (185 m/year average RMSE) was beaten by simple linear regression rate extrapolation (128 m/year average RMSE).
Coastal Erosion Hazard in Bangladesh: Space-time pattern analysis and empirical forecasting, impacts on land use/cover, and human risk perception · Virginia Tech · Doctorate
Lost to a baseline
Kalman Filter and Moving Average smoothing produced higher TTC MAE (0.81 s and 0.86 s) compared to raw UWB measurements (0.62 s excluding 20 m).
Utilization of Wireless Sensors for Pedestrian Safety Studies · Carleton University · PhD
Lost to a baseline
Colored noise Kalman filter with augmented random bias state loses optimality guarantee, yielding higher K-L divergence metric Eopt than an ideal Kalman filter with known disturbance input
Aerospace Applications of Noise Covariance Identification with Autocovariance Least Squares · University of Washington · PhD
Lost to a baseline
Manually tuned Extended Kalman filters (EKF 1, EKF 2, EKF 3) were outperformed by the LTV-ALS tuned Extended Kalman filter, which achieved lower K-L divergence metric Eopt and smaller initial transient oscillations
Aerospace Applications of Noise Covariance Identification with Autocovariance Least Squares · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected the Extended Kalman Filter (EKF) in favor of the Unscented Kalman Filter (UKF) to avoid linearisation approximation errors from Taylor series expansion while keeping computational complexity low.
Identification of nonlinear and time-varying systems under dynamic and seismic excitation · Politecnico di Torino · PhD
Considered and rejected
Considered and rejected: Rejected low-pass filtering on MEMS-IMU high-frequency white noise because filtering erases essential dynamic information leading to EKF instability; handled via measurement noise tuning instead.
Resilient, Low-Cost Navigation in Urban Environments with GNSS Precise Point Positioning, Inertial Measurement Unit and Precise Clock Sensor Fusion · York University · PhD
Tried and failed
online Kalman filtering for multi-rate sensor fusion applied to longitudinal acceleration estimation. Outcome: unstable. Reason: non-systematic measurement noise from low-frequency observation sources degraded state tracking
Crowd-sourced Road Geometry and Accurate Vehicle State Estimation Using Mobile Devices · University at Buffalo · PhD
Lost to a baseline
Proposed ML filter performed 'about 10% worse than the filter with Gaussian process noise' in position RMSE for the zero-thrust scenario due to regularization uncertainty inflation.
Bayesian approaches to low-thrust maneuvering spacecraft tracking · UT Austin · PhD
Adaptive filters and inverse deconvolution suffer from instability and numerical divergence
Least mean squares, recursive least squares, and direct inverse deconvolution methods frequently diverge due to ill-conditioned matrices, near-zero frequency responses, or excessive adaptation step sizes. Iterative deconvolution algorithms also overfit background noise and generate ringing artifacts when cutoffs or iteration counts are poorly regulated.
Considered and rejected
Considered and rejected: Rejected naive low-pass/band-pass direct inverse filtering due to large noise spikes and high-frequency informational loss, replacing it with wavelet denoising.
NONDESTRUCTIVE TESTING AND MATERIAL CHARACTERIZATION BY TERAHERTZ PULSED IMAGING AND TIME-DOMAIN SPECTROSCOPY · Georgia Tech · PhD
Tried and failed
iterative deconvolution with higher low-pass cutoffs applied to noisy empirical seismic waveforms. Outcome: did not generalise. Reason: High-frequency noise amplification caused ringing artifacts on real data, unlike synthetic tests.
A comparative study of methods used to compute PdP underside reflection functions · Texas Tech · Master's
Tried and failed
iterative deconvolution for signal separation applied to noisy precursor seismic waveforms. Outcome: overfit. Reason: High iteration counts overfit background noise causing clutter, while too few iterations missed true reflection profiles
Seismic Investigation in Upper Mantle Beneath the Asia Using PP precursor Analyses · Texas Tech · Master's
Considered and rejected
Considered and rejected: Rejected standard deconvolution inverse filtering of reflection data because wavelets are band-limited, causing massive deconvolution errors.
Computational Methods for One-Dimensional Scattering in Non-Smooth Media · York University · Master's
Tried and failed
Recursive least squares adaptive linear prediction filtering applied to instantaneous frequency tracking of magnetic signals. Outcome: worse than baseline. Reason: Suffered from poor transient response and noise rejection compared to normalized LMS
Measurement and Modeling for Resource Monitoring · MIT · PhD
Tried and failed
least mean squares adaptive filter applied to time-series feature forecasting. Outcome: did not converge. Reason: constant learning rate and high sensitivity to input feature scaling
Lower back muscle activity and fatigue during activities of daily living using a novel wearable device and machine learning · Imperial · PhD
Tried and failed
linear adaptive FIR filtering with LMS updates applied to nonlinear dynamic plant identification. Outcome: did not converge. Reason: Linear FIR filters cannot capture nonlinear dynamics regardless of filter length or step size.
A novel Adaptive Filtering approach to Drive File Identification for Service Environment Replication · Virginia Tech · Doctorate
Tried and failed
adaptive threshold tracking via peak mean estimation applied to real-time spike detection. Outcome: unstable. Reason: noise peaks reduced estimated peak values and thresholds, creating positive feedback instability without frequent resets
Real-time neural signal processing and low-power hardware co-design for wireless implantable brain machine interfaces · Imperial · PhD
Tried and failed
direct inverse filtering of adaptive equalization applied to signal reconstruction and cancellation. Outcome: unstable. Reason: filter frequency response contained zeros or near-zeros causing numerical divergence without regularization
Technologies For Next-Generation Optical Communication Systems · Georgia Tech · PhD
Lost to a baseline
Adaptive RLS filter achieves 'lower overall accuracy than the batch method' at 40 cycles due to recency bias / forgetting factor overfitting.
Modeling and Gait Control for Principally Kinematic Locomotion Systems · Johns Hopkins · PhD
Lost to a baseline
On field measurements with uncertainties, unconstrained RLS achieved 0.19% MAPE while the proposed method had 0.36% MAPE (though RLS undesirably tracked noise and lacked robustness).
Composite load modeling for power systems: Model reduction, identification, and application to conservation voltage reduction · Iowa State · PhD
Linear filters fail when noise spectra overlap with signals or violate stationary assumptions
Classical filtering techniques cannot effectively separate noise from signals when their spectral bandwidths overlap or when noise possesses multiplicative and non-stationary characteristics. Under such conditions, linear filtering cancels underlying signal components alongside noise or degrades severely when environmental dynamics violate stationary Gaussian assumptions.
Considered and rejected
Considered and rejected: Rejected bandpass filtering because temporal filtering is ineffective at eliminating signals above Nyquist frequency (~0.143 Hz) and aliases physiological noise.
Atypical Relations Between Default, Dorsal Attention, and Frontoparietal Control Networks in Autism Spectrum Disorder · York University · Master's
Tried and failed
matched filtering on untransformed fluctuating intensity data applied to signals with multiplicative noise. Reason: signal-dependent multiplicative noise is canceled alongside signal information during linear filtering
Tried and failed
low-pass filtering of sensor signals applied to inertial measurement unit acceleration data. Outcome: did not generalise. Reason: broadband motion frequencies overlapped with noise during dynamic movement phases
Multi-sensor systems and models for high accuracy indoor positioning · Imperial · PhD
Tried and failed
Wiener filter denoising applied to non-stationary vibration and flux signals. Outcome: did not generalise. Reason: assumes stationarity which failed under fluctuating supply and environmental harmonic interference
Stray Flux Monitoring and Multi-Sensor Fusion Condition Monitoring for Squirrel Cage Induction Machines · Georgia Tech · PhD
Tried and failed
graph convolutional network on patch-induced graphs applied to spectrally diverse graph patches. Outcome: worse than baseline. Reason: fixed low-pass filtering assumptions conflicted with spectrally diverse graph patches
Towards Open World Graph Learning and Applications · Virginia Tech · Doctorate
Tried and failed
least-mean-square adaptive filtering applied to time delay estimation at low SNR. Reason: receiver noise signals were correlated across channels under low signal-to-noise ratio conditions
Phase Transform Time Delay Estimation to Counteract Spectral Haystacking Effects in Jet Exhaust Flow Measurements · Virginia Tech · Master's
Tried and failed
Iterative adaptive minimum-variance filtering with structured covariance applied to partially coherent waveform radar pulse compression. Outcome: did not generalise. Reason: Clutter returns across delays increase clutter subspace rank, exhausting available adaptive degrees of freedom
Non-Orthogonal Waveform Use in MIMO Ground Moving Target Indication Radar · Georgia Tech · PhD
Filter phase lag and group delay degrade real-time tracking and control stability
Heavy digital filtering and windowed averaging introduce phase delays that undermine time-critical detection and tracking workflows. In closed-loop feedback systems, the resulting phase lag erodes stability margins and can destabilize control loops completely.
Tried and failed
cross-correlation impulse response estimation using filtered sensor signals applied to grid frequency dynamics estimation. Outcome: worse than baseline. Reason: High-pass filtering in measurement units distorted signals and introduced phase unsynchronization artifacts
Data-driven modeling and graph learning for power system operations · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected Butterworth and Chebyshev filters for remote pressure signal processing because they introduced unwanted phase distortion
Data-Driven Approaches in Gusty Aerodynamics: Insights from Sparse Surface Pressure Measurements · Queen's University · PhD
Considered and rejected
Considered and rejected: Rejected moving average filter for online magnetic field denoising because it averages all frequency components rather than selectively shrinking coefficients below a threshold, introduces window-dependent time delays, and fails to retain GIC-correlated peaks.
Vulnerability Assessment of Power Transformers and Power Systems to Geomagnetic Disturbances · York University · PhD
Tried and failed
moving average window filtering applied to real-time signal detection. Outcome: too slow. Reason: Introduced unacceptable detection phase delays compared to linear-phase Savitzky-Golay filtering.
A Decentralized Privacy-Preserving Data-Driven Methodology for Energy Trading in the Smart Grid · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected the use of the Moving Average Window (MAW) filter for real-time invariant feature extraction due to phase delays compared to the Savitzky-Golay filter.
A Decentralized Privacy-Preserving Data-Driven Methodology for Energy Trading in the Smart Grid · Georgia Tech · PhD
Tried and failed
increasing low-pass filter order applied to hardware-in-the-loop stability interface. Outcome: unstable. Reason: higher filter order degrades stability margins without systematic data-driven tuning
Data-driven Power Electronic Converter Control Design in Power System Applications · EPFL · PhD
Lost to a baseline
Notch filter exhibited higher sensitivity and instability under ±50% parameter mismatch (mass and stiffness) compared to lag filter and open loop.
Control Strategies for Piezoelectrically Actuated Fast Mechanical Disconnect Switches for Hybrid Circuit Breakers · Georgia Tech · PhD
Lost to a baseline
Notch filter introduced small irregular high-frequency noise after switching-off compared to PI-only and lag filter.
Control Strategies for Piezoelectrically Actuated Fast Mechanical Disconnect Switches for Hybrid Circuit Breakers · Georgia Tech · PhD
Aggressive thresholding and SNR filtering discard genuine signals and introduce bias
Applying strict automated SNR cutoffs, uncertainty thresholds, or outlier filtering often discards low-amplitude signals and genuine physical phenomena. In addition to eliminating valid data, such filtering rules can introduce systematic estimation biases toward high-magnitude components.
Tried and failed
high-pass median filtering for signal extraction applied to spectroscopic data processing. Outcome: did not generalise. Reason: weaker baseline throughput trends made the filter ineffective and degraded signal extraction
Resolving the Mysteries of Highly Irradiated Planets: Observations and Simulations · MIT · PhD
Tried and failed
filtering low-SNR data by parameter estimation uncertainty threshold applied to diffusion MRS metabolite signal estimation. Reason: filtering out high-uncertainty samples biased results toward high-concentration and slowly diffusing species
New insights into rodent brain microstructure and metabolism in hepatic encephalopathy · EPFL · PhD
Tried and failed
phase derivative thresholding for superresolution wave imaging applied to complex media with interfering scatterers. Outcome: did not generalise. Reason: interfering nearby scatterers distort the phase derivative, causing true scattering locations to be filtered out
Geometrically-informed methods of wave-based imaging · MIT · PhD
Considered and rejected
Considered and rejected: Rejected automated SNR-threshold filtering for cleaning distant wildlife seismic labels because it mistakenly discards valuable low-amplitude signals.
Inference and machine learning across the spatial scales in geophysics · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected SNR and correlation (COR) filtering from ADV hardware manuals because valid data points are frequently misidentified as subpar.
Hydraulic Characterization of Mounded Gravel Fish Nests: Incipient Motion Criteria and Despiking Acoustic Doppler Velocimeter Data · Virginia Tech · Master's
Tried and failed
strict quality filtering of training data applied to spectroscopic deep learning quantification. Outcome: did not generalise. Reason: over-filtering excluded natural variance and outliers, reducing model robustness and generalizability on unseen data
Spectroscopic MRI in the Study and Clinical Translation of Pediatric High-Grade Glioma and Neurologic Disorders · Georgia Tech · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Develop a robust observation noise variance estimator for source localization to prevent overestimation from broadband background neural power. Blocker: Lacks a specified algorithmic formulation or mathematical approach for distinguishing cortical observation noise from broadband power
State Space Methods Using Biologically-Relevant Generative Models to Analyze Neural Signals · MIT · PhD
Left open
Tune wavelet bases, filter configurations, and KNN hyperparameters in the ECG motion artifact classification and denoising pipeline. Blocker: None
MACHINE LEARNING-BASED CLASSIFICATION AND REDUCTION OF MOTION ARTIFACT NOISE IN ECG SIGNALS FOR WEARABLE VITAL SIGN MONITORING DEVICES · Dalhousie · PhD
Left open
Evaluate alternative signal embedding methods and optimize density filtering techniques beyond radial histograms for toroidal blind signal separation. Blocker: Vague task specification without defined alternative embeddings, filtering objectives, or benchmark datasets
TOPOLOGY-INSPIRED TECHNIQUES FOR BLIND SIGNAL PROCESSING OF CONSTANT MODULUS SIGNALS · Naval Postgraduate School · Doctorate
Left open
Develop a principled residual uncertainty tracking method for low-rank Kalman filters to prevent overconfidence without heuristic covariance inflation. Blocker: Lacks a concrete mathematical formulation or specific algorithmic mechanism for tracking truncated residual uncertainty.
Probabilistic Inference for Spatiotemporal Dynamics · Tübingen · PhD
Left open
Validate AutoEKF on synthetic data and benchmark against particle filter methods, or replace linearization with variational autoencoders. Blocker: None
TOWARDS DATA DRIVEN NETWORK EPIDEMIC MODELING. · Penn · PhD
Left open
Generalize filter temporal ablation methods to multichannel EEG deep learning models by implementing per-channel filter perturbations. Blocker: None
Novel Explainability Approaches for Analyzing Functional Neuroinformatics Data with Supervised and Unsupervised Machine Learning · Georgia Tech · PhD
Left open
Develop statistical variance models for voltammetric current measurements incorporating baseline drift and capacitive effects. Blocker: Requires experimental voltammetry data with known ground-truth noise profiles or a wet lab to collect calibration datasets
Voltammetric Methods Augmented with Physical Models and Statistical Inference · MIT · PhD
Left open
Benchmark RobustICA and adaptive filtering against the proposed template subtraction pipeline for ECG motion artifact isolation. Blocker: None
Motion Artifact Data to Facilitate Bioelectric Signal Quality Analysis Research · Carleton University · Master's
Left open
Test the adaptive denoising algorithm on wearable EMG and EEG recordings under real-world motion noise. Blocker: Requires collecting real-world motion-corrupted wearable EMG wristband and EEG recording data with physical hardware
Decoding peripheral neural correlates of dexterous movements · Imperial · PhD
Left open
Evaluate the 2D Gaussian low-pass filtering method on higher-order strain fields and complex experimental DIC gradient datasets. Blocker: Requires experimental DIC measurement data with complex physical strain gradients.
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Kimberly Hinson · University of Washington · PhD · 2025
Aerospace Applications of Noise Covariance Identification with Autocovariance Least Squares - Yue Wang · University College Dublin · PhD · 2024
On the Epistemology of Gibbs Ringing Reduction Algorithm Performance - Styliani Ioanna Kampezidou · Georgia Tech · PhD · 2023
A Decentralized Privacy-Preserving Data-Driven Methodology for Energy Trading in the Smart Grid - Zixiao Ma · Iowa State · PhD · 2023
Composite load modeling for power systems: Model reduction, identification, and application to conservation voltage reduction - Yu An · University College Dublin · PhD · 2023
Developing Deep Learning Approaches towards Automatic Seismic Fault Interpretation - Chunmeng Xu · Georgia Tech · PhD · 2021
Control Strategies for Piezoelectrically Actuated Fast Mechanical Disconnect Switches for Hybrid Circuit Breakers - Matthew Joseph Jackson · Georgia Tech · PhD · 2026
Non-Orthogonal Waveform Use in MIMO Ground Moving Target Indication Radar - Shreyan Banerjee · University College Dublin · PhD · 2026
A Neuromorphic Approach towards Detection and Control of Features of Interest in Dynamical Systems - John J. Power · University College Dublin · PhD · 2026
In-situ process monitoring of laser powder bed fusion for laser parameter optimisation of Ti-6Al-4V alloy parts with overhang features - Salah Fakhoury · Carleton University · PhD · 2026
Utilization of Wireless Sensors for Pedestrian Safety Studies - LINDA SCUSSOLINI · Politecnico di Torino · PhD · 2026
Identification of nonlinear and time-varying systems under dynamic and seismic excitation - Shuaicheng Zhang · Virginia Tech · Doctorate · 2026
Towards Open World Graph Learning and Applications
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