Neural process model improves real-time condition monitoring signal prediction.
problem Real-time adaptation for complex condition monitoring signals.
method Label-aware neural processes encoding and reconstruction.
result Advantages in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
This work enhances GPS signals using robust GP regression for real-time high precision positioning.
problem Large noise in GPS signals degrades positioning accuracy.
method A three-step approach: signal validity tests, reweighting, and final model training.
result Proposed method achieves centimeter accuracy with 78.4% probability, outperforming other methods.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
Coherent Multiplex analyzes real-time wavelet coherence among multiple signals.
problem Identifying and visualizing coherence among multiple time series.
method Fast spectral similarity based on cosine similarity metrics of Fourier-transformed signals and sparse time-frequency wavelet coherence.
result Scalable real-time system for low-latency inference and monitoring of inter-signal relationships.
Complex-valued Gaussian process improves time series analysis of brain oscillations.
problem Improving time series analysis of complex-valued signals, especially brain oscillations.
method Modeling real-valued signals as the real part of a latent complex-valued Gaussian process with new covariance functions.
result Complex-valued Gaussian process provides better estimates of amplitude and frequency than existing methods.
Paper investigates optimization methods for ICA on real signals, overcoming convergence issues.
problem Optimizing likelihood for ICA on real signals, especially with constrained white signals.
method Rewrites algorithms as quasi-Newton methods, focusing on Hessian approximation.
result Preconditioned ICA for Real Data (Picard) algorithm improves convergence on real signals.
Paper presents DL models for ECG signal denoising.
problem Efficient denoising of ECG signals for wearable devices.
method CNNs, LSTM, RBM, filtering methods, wavelet-based technique.
result CNN model performs well for offline denoising.
ZM-Net efficiently manipulates images with unseen signals in real-time.
problem Efficiently alter images with diverse guiding signals (e.g. paintings, attributes).
method Proposes ZM-Net, a fully-differentiable architecture that jointly optimizes TNet and PNet.
result ZM-Net performs high-quality image manipulation in real-time (tens of milliseconds per image) for unseen signals.
Improved extreme learning machines for graph signal regression.
problem Regression tasks with graph signals and limited/noisy data.
method Graph signal processing regularization for smoothness.
result Regularization improves prediction accuracy with limited data.
Recursive prediction of graph signals with new nodes added.
problem Predicting graph signals with new nodes added over time.
method Recursive prediction of graph signals using incoming nodes.
result Recursive method results in good prediction performance close to full graph knowledge.
Algorithm mines environment assumptions for cyber-physical systems.
problem Modeling and verifying complex cyber-physical systems.
method Supervised learning to mine STL formulas for input signals.
result Algorithm learns both the structure and constants of STL formulas.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
Random walk sampling recovers smooth graph signals from few samples.
problem Efficiently sampling graph signals from large networks.
method Random walk sampling strategy based on network nullspace property.
result Graph signals can be accurately recovered from few samples.
Proposes a new dictionary learning method for high-dimensional graph signals.
problem Challenges of traditional sparse representation methods in high-dimensional graph signals.
method Integrates graph topology implicitly through sparse combinations of graph-wavelet functions and explicitly through graph constraints.
result Demonstrates effectiveness in high-dimensional graph signal processing.
FinGPT uses LLMs for real-time market sentiment analysis.
problem Real-time market sentiment analysis for trading.
method Synthesizes financial news and social media data, integrates with technical indicators, uses FinGPT for sentiment analysis.
result Generates actionable trading signals using LLMs.
We provide an alternative method for analysis of multifractal properties of time series. The new approach takes into account the behaviour of the whole multifractal profile of the generalized Hurst exponent h(q) for all moment orders q, not limited only to the edge values of h(q) describing in MFDFA scaling prope…
A new method for complex-valued signals improves convergence and performance.
problem Nonlinear channel equalization and complex-valued signals with different properties.
method Generalized complex kernel least-mean-square (gCKLMS) algorithm.
result The gCKLMS algorithm converges faster and performs better than previous methods.
Proposes a novel graph signal model using narrowband kernels.
problem Graph signals with multiple concentrated frequency regions.
method Jointly learns graph signal model parameters and coefficients.
result Joint learning improves signal interpolation accuracy.
The paper proposes a machine learning method to detect drivers' affective states using physiological signals.
problem Detecting and assessing drivers' affective states to improve driving safety and well-being.
method Multiview multi-task machine learning approach using physiological signals.
result Accounting for drive-specific differences significantly improves model performance.
Paper develops DL methods for signal demodulation in wireless comms.
problem Signal demodulation in wireless communications.
method Proposes DBN-SVM and AdaBoost demodulators using real modulated signals.
result Proposed DBN-SVM and AdaBoost demodulators outperform traditional methods.
Develops multi-kernel regression for graph signal processing.
problem Smoothness of graph signals over a graph.
method Estimates linear weights to learn effective kernel function using graph smoothness.
result Optimization problem is convex and accelerated projected gradient descent solution proposed.
Paper proposes a deep learning model for real-time ECG signal segmentation.
problem Real-time analysis of large ECG datasets for tele-health monitoring.
method Combines CNN and LSTM for detecting heartbeats' waveforms.
result Achieved high sensitivity and precision in QRS detection.
System guides freehand obstetric ultrasound probe movements.
problem Reducing operator expertise for standard plane acquisition.
method Artificial neural network trained on real scans.
result 88.8% accuracy for goal prediction, 90.9% for action prediction.
This paper presents a novel Block Iterative Bayesian Algorithm (Block-IBA) for reconstructing block-sparse signals with unknown block structures. Unlike the existing algorithms for block sparse signal recovery which assume the cluster structure of the nonzero elements of the unknown signal to be independent and identic…
Paper analyzes bias-variance tradeoff in graph Laplacian regularization.
problem Understanding the optimal regularization parameter for graph Laplacian.
method Spectral graph properties and signal-to-noise ratio parameter used to determine optimal regularization.
result Selecting mediocre regularization is often suboptimal, suggesting near-optimal performance.
Survey on modern traffic signal control methods.
problem Minimizing travel time at road intersections.
method Machine learning, reinforcement learning.
result Promotes interdisciplinary research on traffic signal control.
This paper reconstructs complex graph signals using kernel methods on manifolds.
problem Reconstructing complex graph signals from samples on graph vertices.
method Kernel methods on complex manifolds, embedding vertices into higher-dimensional spaces.
result Effective reconstruction of complex graph signals, outperforming conventional methods.
There are three equivalent ways of representing two jointly observed real-valued signals: as a bivariate vector signal, as a single complex-valued signal, or as two analytic signals known as the rotary components. Each representation has unique advantages depending on the system of interest and the application goals. I…
Novel SCUSUM detects weak spatial signals more efficiently.
problem Detecting weak clustered signals in spatial data.
method Spatial CUSUM (SCUSUM) using CUSUM procedure and false discovery rate control.
result SCUSUM achieves high classification accuracy for weak spatial signals.
GPMM recovers latent signals from noisy mixtures using Bayesian inference.
problem Recovering latent signals from noisy mixed measurements.
method Gaussian process mixture of measurements (GPMM) with Bayesian inference.
result GPMM outperforms standard GP in signal recovery.
DeepInverse learns to invert signals faster than traditional methods.
problem Slow convergence and non-sparse real-world data in compressive sensing.
method Develops a deep convolutional network to learn the inverse signal transformation.
result DeepInverse network approximates state-of-the-art CS recovery algorithms but is significantly faster.
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis. Unfortunately, the analysis of EDA signals is made difficult by the superposition of numerous components which can obscure the signal informatio…
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure. This approach requires looking for a good representation that reveals the signal structure, and sol…
Missing data reduces signal-to-noise ratio, not sample size, for PCA.
problem Effect of missing data on PCA signal structure learning.
method Analytic and simulation studies of probabilistic PCA with missing data.
result Missing data effectively reduces signal-to-noise ratio, not sample size.
Paper develops deep learning for signal recognition in long perimeter fiber optic sensors.
problem Difficult signal-jamming environments and stringent error requirements.
method Two-level event detection architecture with ensemble of deep convolutional networks.
result Efficient and robust multiclass detection algorithms with high adaptability.
TSN improves sparse signal recovery with less complexity.
problem Sparse regression problem of recovering sparse signals from measurements.
method Tree search algorithm driven by deep neural network with pruning.
result TSN outperforms conventional methods in various sensing matrices.
Paper improves signal proportion estimation by accounting for variable dependence.
problem Traditional estimators assume independence, limiting applicability in real-world scenarios.
method Integrates arbitrary covariance dependence information using principal factor approximation.
result Method outperforms state-of-the-art estimators in accuracy and detection of weaker signals.
UniPhyNet improves cognitive load classification accuracy using EEG, ECG, and EDA signals.
problem Classifying cognitive load using multimodal physiological data.
method Unified network architecture integrating multiscale parallel convolutional blocks, ResNet-type blocks, and channel block attention module. Uses bidirectional gated recurrent unit for temporal dependencies.
result Improves raw signal classification accuracy from 70% to 80% (binary) and 62% to 74% (ternary) on CL-Drive dataset.
In sparse Bayesian learning (SBL), Gaussian scale mixtures (GSMs) have been used to model sparsity-inducing priors that realize a class of concave penalty functions for the regression task in real-valued signal models. Motivated by the relative scarcity of formal tools for SBL in complex-valued models, this paper propo…
Paper learns hypergraph structures from signals with smoothness priors.
problem Learning hypergraph structures from signals with high-order relationships.
method Proposes HGSL framework with dual smoothness prior to map signals to hypergraph structure.
result HGSL efficiently infers meaningful hypergraph topologies from signals.
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.
A new autoencoder speeds up audio style transfer.
problem Efficiently generating stylized audio from content audio.
method Autoencoder architecture for real-time audio style transfer.
result Single forward pass for stylized audio generation.
QuantAgent learns trading signals through self-improvement.
problem Building domain-specific knowledge for LLMs in quantitative investment.
method Two-layer loop approach: inner loop refines responses, outer loop tests and learns.
result QuantAgent approximates optimal trading behavior with provable efficiency.
Enhances speech from reverberated and noisy signals using Wide Residual Networks.
problem Improving intelligibility of speech from reverberated and noisy signals.
method Wide Residual Network architecture with residual connections for processing contextually correlated representations.
result Enhanced speech from the proposed method succeeded for both enhancement and speech recognition tasks.
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. Bu…
The paper detects entrance positions using Wi-Fi and GPS signals.
problem Accurate detection of building entrance positions.
method Combines GPS signal drop and Wi-Fi RSS to detect entrances.
result The system's accuracy is one meter.
SMSSVD simplifies biomedical signal decomposition without parameters.
problem Identifying and separating overlaid signals from technical artefacts.
method Unsupervised, parameter-free Singular Value Decomposition (SMSSVD) for denoising and signal representation.
result SMSSVD outperforms existing methods like PCA and SPC in real and synthetic datasets.
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.