Optimizes signal detection in particle physics by decorrelating classifiers.
problem Systematic errors in background models can mislead signal detection.
method Use optimal transport to decorrelate classifiers from protected variables, then apply semiparametric mixture model.
result Decorrelation and signal enrichment improve the stability, robustness, and power of signal detection tests.
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched subspace detection problem. Tensor matched subspace detection problem is more challenging because of the intertwined signal dimensions. We s…
Motivated by a range of applications in engineering and genomics, we consider in this paper detection of very short signal segments in three settings: signals with known shape, arbitrary signals, and smooth signals. Optimal rates of detection are established for the three cases and rate-optimal detectors are constructe…
Detects graph topology changes from noisy signals using prior spectral information.
problem Detecting changes in graph topology from graph signals.
method Leverages graph filtering and subspace detection to distill problem into a CUSUM-based algorithm.
result Demonstrates the effectiveness of incorporating prior spectral signatures for change-point detection.
Study detects signal in financial stock correlations using phase-ordering kinetics.
problem Detecting meaningful signals in financial stock return correlations.
method Stochastic field theory model to establish a detection threshold.
result Detection of a signal in the largest eigenvalues of the stock return correlation matrix.
Improves signal detection in non-Gaussian noise using transformed data.
problem Signal detection in rank-one signal-plus-noise data matrices.
method Pre-transforming matrix entries and using linear spectral statistics for hypothesis testing.
result Sharp phase transition of largest eigenvalues in spiked rectangular matrices.
Conformal prediction improves signal detection accuracy in railway images.
problem Improving the reliability of machine learning models for railway signal detection.
method Applying conformal prediction to a novel dataset of train operator perspective images.
result The approach enhances the reliability of machine learning models for detecting railway signals.
Deep learning models detect nanopore translocation events with high accuracy.
problem Manual parameter selection for nanopore signal analysis is prone to error.
method Developed a synthetic signal generator for training ML models.
result Deep learning models achieve over 99% true event detection.
Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the \textit{load-modulated} multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum lik…
Paper studies signal detection in noisy environments with limited communication.
problem Signal detection in Gaussian noise with 1-bit communication constraints.
method Derives lower bounds and exhibits optimal testing strategies.
result Optimal distributed testing strategies attain the derived lower bound.
Better signal detection in undersampled data using joint and cross covariances.
problem Detecting shared signals in high-dimensional data with limited samples.
method Analysis of three covariance matrices: individual, cross, and joint.
result Joint and cross covariance matrices detect signals earlier than individual covariances.
Detecting weak clustered signal in spatial data is important but challenging in applications such as medical image and epidemiology. A more efficient detection algorithm can provide more precise early warning, and effectively reduce the decision risk and cost. To date, many methods have been developed to detect signals…
Constructs Gabor frames for curved manifolds to detect boundaries.
problem Signal analysis on curved manifolds with boundaries.
method Higher-dimensional Gabor frames for local linearizations.
result Detection of higher-dimensional boundaries in curved manifolds.
The paper detects changes in graph signal means offline.
problem Segmenting and detecting changes in multivariate signals over graph nodes.
method Model selection approach exploiting sparsity in spectral domain.
result Proof of non-asymptotic oracle inequality for change-point detection.
Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…
Paper proposes a k-NN classifier for detecting spike-and-wave seizures in EEG.
problem Early detection of epileptic seizures in EEG signals.
method Uses t-location-scale distribution and k-nearest neighbors classifier.
result Demonstrates improved classification accuracy, sensitivity, and specificity on real data.
Light neural network detects modulation in noisy signals.
problem Efficiently detecting modulation in noisy signals.
method Light neural network architecture invariant to impairments.
result Network achieves accuracy under realistic impairments.
Novel graph-based method detects R-peaks in noisy ECG signals without preprocessing.
problem Detecting R-peaks in noisy ECG signals for real-time analysis.
method Graph-constrained Changepoint Detection (GCCD) approach.
result GCCD achieves high sensitivity, positive predictivity, and low detection error rate.
Study detects signals in spiked Wigner models using log likelihood ratio.
problem Detecting signals in rank-one spiked Wigner models with non-Gaussian noise.
method Proved asymptotic normality of log likelihood ratio and computed error thresholds.
result Optimal signal-to-noise ratio threshold for reliable detection.
Study on signal detection in sparse additive models with nonasymptotic minimax rates.
problem Signal detection in sparse additive models.
method Nonasymptotic minimax analysis of signal detection in sparse additive models.
result Established minimax separation rate for signal detection.
Mixed-SCORE+ improves community detection in weak signal networks.
problem Detecting communities in weak signal networks.
method Proposes Mixed-SCORE+ combining properties of Mixed-SCORE and SCORE+.
result Significantly improves detection error rates on Polblogs and weak signal networks.
The paper detects amateur drones using acoustic signals, overcoming interference.
problem Detection of unmonitored UAVs (amateur drones) for security and safety.
method Machine learning approach using Independent Component Analysis (ICA) for acoustic signal detection.
result The RMS values of PSD with KNN outperform other methods in detecting AmDr.
In this paper, we propose a classification based glottal closure instants (GCI) detection from pathological acoustic speech signal, which finds many applications in vocal disorder analysis. Till date, GCI for pathological disorder is extracted from laryngeal (glottal source) signal recorded from Electroglottograph, a d…
Paper tackles outlier detection in signals modeled by generative models with theoretical guarantees.
problem Recovering signals from linear measurements with sparse outliers.
method Proposes an iterative ADMM algorithm and gradient descent algorithm for outlier detection using ℓ1 and squared ℓ1 norm minimization. result Establishes theoretical recovery guarantees for signal reconstruction under sparse outliers.
Deep learning improves GW signal detection efficiency and robustness.
problem Traditional matched-filtering techniques are limited in detecting new GW signals.
method Optimized CNN models with techniques like batch normalization and dropout.
result CNN models are robust to the variation of GW waveform parameters.
This letter presents a novel Block Bayesian Hypothesis Testing Algorithm (Block-BHTA) for reconstructing block sparse signals with unknown block structures. The Block-BHTA comprises the detection and recovery of the supports, and the estimation of the amplitudes of the block sparse signal. The support detection and rec…
Hidden Markov Models detect hand gestures from wearable sEMG signals.
problem Detecting activity regions in continuous sEMG signals.
method Hidden Markov Models applied to sEMG signals for gesture recognition.
result Average accuracy of 96.25% for activity onsets and 87.5% for activity terminations.
Proposes a new signal model for high-dimensional, small-sample-size data.
problem Signal detection in high-dimensional, small-sample-size datasets.
method Intrinsic signal model based on dynamical system assumption.
result Taguchi method effectively detects signals in the proposed model.
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies w…
In this paper, we show an approach to build deep learning algorithms for recognizing signals in distributed fiber optic monitoring and security systems for long perimeters. Synthesizing such detection algorithms poses a non-trivial research and development challenge, because these systems face stringent error (type I a…
A wide variety of application domains are concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the r…
The paper uses conformal prediction to detect railway signals with confidence.
problem Deploying deep learning models in certified systems requires accurate uncertainty estimates.
method The paper uses conformal prediction and risk control to detect railway signals.
result The conformal prediction framework provides reliable and trustworthy uncertainty estimates for model performance.
Study on signal-plus-noise decomposition in nonlinear spiked random matrices.
problem Nonlinear spiked random matrix models with rank-one signal and noise.
method Signal-plus-noise decomposition and phase transition analysis.
result Identified precise phase transitions in signal components at critical thresholds.
We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are …
Study uses LCRN to detect driver distraction from EEG signals.
problem Improving road safety by detecting driver distraction.
method Used a Long-term Recurrent Convolutional Network (LCRN) for EEG-based driver distraction detection.
result LCRN model outperformed state-of-the-art TSC models in detecting driver distraction.
The paper studies phase transitions in random matrices and tensor unfolding for detecting signals.
problem Phase transitions in singular values and vectors of large random matrices.
method Analysis of singular values and vectors of long rectangular random matrices, and tensor unfolding algorithm for asymmetric rank-one spiked tensor models.
result An exact threshold for tensor unfolding to detect signals, independent of unfolding procedure.
This paper aims at detecting an accurate position of the main entrance of the buildings. The proposed approach relies on the fact that the GPS signals drop significantly when the user enters a building. Moreover, as most of the public buildings provide Wi-Fi services, the Wi-Fi received signal strength (RSS) can be uti…
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
problem Optimizing medical imaging systems by assessing their performance using the Ideal Observer model.
method Uses supervised learning methods, specifically convolutional neural networks, to approximate the Ideal Observer for joint signal detection and localization tasks.
result Supervised learning-based methods can approximate the Ideal Observer for joint signal detection and localization tasks, as shown by comparisons to MCMC and analytical methods.
Study compares unsupervised and weakly-supervised methods for anomaly detection at the LHC.
problem Detecting new physics signals at the LHC with model-agnostic techniques.
method Compared unsupervised autoencoder (AE) and weakly-supervised Classification Without Labels (CWoLa) methods.
result Both methods complement each other, providing sensitivity to different types of signals.
A nonparametric method for time series analysis extracts envelopes, detects peaks, and clusters data.
problem Extracting envelopes, detecting peaks, and clustering in time series data.
method Iterative procedure that minimizes L1 drift to create upper and lower bounding signals, using Viterbi-like path tracking and optimal elimination rules. result Efficiently calculated solution with near-linear time complexities for various applications.
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, robust to background noise and non-earthquake signals, and efficient for processing large data volumes. Here, we introduce the Cnn-Rnn Earthq…
Paper proposes a statistical model for detecting mu-suppression in EEG signals.
problem Detecting mu-suppression in motor imagery EEG signals.
method Proposes a statistical model based on the generalized extreme value distribution (GEV) and a linear classifier.
result Preliminary results show good classification accuracy in detecting mu-suppression and distinguishing EEG events.
Detects changes in brain signal topology to predict epileptic seizures.
problem Anomaly detection in EEG signals for epilepsy.
method Extends signature theory to detect changes in topological structure of EEG signals.
result Detection of precursor phenomena to epileptic seizures.
TDA detects financial bubbles through early warning signals.
problem Detecting financial bubbles early.
method Using Log-Periodic Power Law Singularity (LPPLS) model to fit financial time series data.
result TDA generates early warning signals when LPPLS model fits the data.
Graph signal processing detects hallucinations in large language models.
problem Detecting factual reasoning from hallucinations in large language models.
method Modeling transformer layers as dynamic graphs, using spectral analysis to define diagnostics.
result Spectral signatures can distinguish different types of hallucinations and achieve high accuracy.
New method improves anomaly detection in acoustic signals.
problem Poor anomaly detection performance in existing acoustic signal-based unsupervised methods.
method Deep autoencoding Gaussian mixture model with hyper-parameter optimization.
result Significantly improved anomaly detection performance compared to previous methods.
Adaptive sampling results in dramatic improvements in the recovery of sparse signals in white Gaussian noise. A sequential adaptive sampling-and-refinement procedure called Distilled Sensing (DS) is proposed and analyzed. DS is a form of multi-stage experimental design and testing. Because of the adaptive nature of the…
The paper proposes an ensemble of convolution-based methods for fault detection in gearboxes.
problem Fault detection in planetary gearboxes using vibration signals.
method Ensemble of three convolution kernel-based methods (ROCKET, 1D CNN with ResNet, FCN).
result Outperforms other approaches with over 98.8% accuracy.