New RNN model fuses sensor data from multiple stations.
problem Modeling distributed sensor networks for future behavior prediction.
method Multi-Encoder-Decoder RNN architecture with attention mechanism.
result Model improves prediction accuracy on real-world sensor datasets.
New method fuses data from multiple sensors using latent common manifold.
problem Capturing nonlinear geometric structures in multimodal data.
method Alternating diffusion method for latent common manifold learning.
result The method effectively fuses data from multiple sensors.
Nonlinear filtering extracts relevant variables from multimodal sleep data.
problem Recover relevant variables from multiple sensor data.
method Diffusion-based manifold learning for nonlinear filtering.
result Method gives robust data-driven representation correlated with sleep process.
Semi-supervised method predicts faults in systems with multiple operation modes.
problem Fault detection in industrial systems with missing target data.
method Semi-supervised soft sensor modeling incorporating mode transition properties.
result Regression coefficients estimated under constraint conditions from mode transitions.
Unified model predicts multi-mode failure with multi-sensor data.
problem Independent failure mode and RUL prediction ignores inherent relationship.
method Hierarchical Bayesian framework with Cox model, Gaussian process, and multinomial distributions.
result Robust uncertainty quantification and accurate prediction of multi-mode failure.
FATHOM model improves sensor data analysis with attention and LSTM.
problem Scarcity of training data from multiple sensors.
method Federated multi-task hierarchical attention model (FATHOM) with attention mechanism and LSTM.
result FATHOM outperforms baselines in sensor data classification and regression.
Bayesian models fuse multiple sensor types for health monitoring in homes.
problem Challenges in fusing heterogeneous sensor data for health monitoring.
method Introduces Bayesian models for sensor fusion of multiple sensor types.
result Identifies most useful sensor modalities and features for activities.
A method for inferring ground-truth signals from degraded sensor data.
problem Inferring ground-truth signals from multiple degraded sensor signals.
method Iterative correction of degraded signals using a Bayesian multi-sensor data fusion method.
result The method effectively infers ground-truth signals from noisy and degraded sensor data.
Improved DOA estimation with distributed sensors across multiple frequencies.
problem Sensor gain uncertainties and directional perturbations in multi-frequency scenarios.
method Distributed optimization with local coherence models and iterative exchange of information.
result Advantages in statistical and computational efficiency through parallel iterative technique.
Method learns behavioral states from wearable sensor data.
problem Understanding behavioral patterns from sensor data.
method Non-parametric Bayesian approach to model sensor data.
result Learned behavioral states cluster participants into meaningful groups and predict psychological states.
A new algorithm tracks aircraft rotations using multiple sensors.
problem Tracking aircraft rotations with multiple sensors in a robust and unique solution.
method Developed a distributed quaternion Kalman filter for three-dimensional space.
result The algorithm is robust to sensor and link failure and avoids gimbal lock.
Improved robustness in multi-modal sensor fusion with deep learning.
problem Inconsistency in fusion weights leading to poor performance under sensor failures.
method Proposes deep multi-modal sensor fusion architectures with fusion weight regularization and target learning.
result Proposed architectures outperform existing deep learning methods under sensor failures.
Siamese neural networks help learn shared variability from multiple sensors.
problem Learning common variability from multiple sensors.
method Applying Siamese neural networks to learn equivalence classes.
result Siamese networks can learn shared variability without model or label information.
Cooperative perception improves 3D object detection in autonomous vehicles.
problem Limited field-of-view and occlusion in single sensor data.
method Early fusion of point clouds from multiple sensors, late fusion of independently detected bounding boxes, and hybrid combination.
result Early fusion approach outperforms late fusion by significantly higher recall (95%) compared to single-point sensing (30%).
New initialization techniques improve the performance and speed of EMI sensor-based object discrimination.
problem Improving the performance and speed of EMI sensor-based object discrimination.
method Proposed and evaluated new initialization techniques for MI-ACE.
result Comparison of initialization approaches shows improved performance and speed.
This work introduces a method to learn dynamical systems from noisy sensor measurements using multiple shooting.
problem Learning dynamical systems from noisy sensor measurements is challenging due to system instability.
method A scalable method based on multiple shooting.
result Robust learning of latent representations of dynamical systems from noisy measurements.
Enhanced microfluidic sensing platform for particle detection.
problem Electronically acquiring spatially separated particle information in lab-on-a-chip devices.
method Microfluidic CODES combining resistive pulse sensing and code division multiple access.
result Non-orthogonal code waveforms and machine learning for improved multiplexing.
Proposes a neural network for handling multi-sensor time series with varying input dimensions.
problem Handling multi-sensor time series with varying input dimensions.
method Graph neural network conditioning vectors for zero-shot transfer learning.
result Better generalization in activity recognition and equipment prognostics datasets.
We describe a methodology for modeling the performance of decision-level data fusion between different sensor configurations, implemented as part of the JIEDDO Analytic Decision Engine (JADE). We first discuss a Bayesian network formulation of classical probabilistic data fusion, which allows elementary fusion structur…
CICME estimates common and domain-specific causal mechanisms from multi-sensor data.
problem Inferring causal mechanisms from heterogeneous multi-sensor data across multiple domains.
method Three-step approach using Causal Transfer Learning (CTL).
result CICME reliably detects domain-invariant causal mechanisms and guides individual domain causal mechanism estimation.
In this paper, we propose a general collaborative sparse representation framework for multi-sensor classification, which takes into account the correlations as well as complementary information between heterogeneous sensors simultaneously while considering joint sparsity within each sensor's observations. We also robus…
EmbraceNet fusion model for multi-sensor activity recognition.
problem Human activity recognition using multiple sensors is challenging.
method Independent processing of each sensor, feature fusion with EmbraceNet, post-processing, and additional processes.
result Improved performance in SHL recognition challenge.
Paper presents machine learning for multi-target localization using various sensors.
problem Accurate multi-target localization for unmanned systems.
method Machine learning algorithms (K-means clustering, SVM) applied to sensor data.
result Different machine learning algorithms perform differently in data association and localization.
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
problem Source localization in WSNs without sensor parameter knowledge.
method Hitting set approach and feature selection method.
result Effective source localization methods validated through simulations.
Improved sleep apnea detection using sensor fusion and backward shortcut connections.
problem Untreated sleep apnea leads to severe health consequences; automated detection is needed.
method Late sensor fusion using backward shortcut connections to improve deep learning models.
result Significant improvement in predictive performance over single sensor methods.
Paper addresses state estimation in sensor networks with intermittent data.
problem State estimation in sensor networks with packet dropouts and corrupted observations.
method Bayesian variational inference with a dual-mask generative model.
result The method effectively identifies system states and noise parameters.
Novel spatio-temporal LSTM model forecasts oceanic variables across sensors and scales.
problem Data sparsity and lack of connected spatial and temporal information in environmental datasets.
method SPATIAL LSTM architecture that learns across spatial and temporal scales.
result Framework accurately forecasts oceanic variables with comparable performance to state-of-the-art models.
The paper identifies drivers from a single car turn using sensor data.
problem Predicting driver identity from a single car turn using sensor data.
method Time series classification of sensor readings from a single turn, focusing on unique patterns in each driver's style.
result Accurate identification of drivers from a single turn, even in varied driving conditions.
Framework predicts remaining useful life of DSH subsystems under unknown failure modes.
problem Predicting remaining useful life of DSH subsystems with unknown failure modes.
method Unsupervised framework using mixture of Gaussian regressions and Expectation-Maximization algorithm.
result Improved prediction accuracy and interpretability of RUL.
Model uses smartphone data to assess MS trajectories.
problem Personalized longitudinal MS assessment.
method Imputation, generalized estimation equation, ensemble learning, fine-tuning.
result Promising model for predicting MS over time.
Wireless sensor networks are composed of distributed sensors that can be used for signal detection or classification. The likelihood functions of the hypotheses are often not known in advance, and decision rules have to be learned via supervised learning. A specific such algorithm is Fisher discriminant analysis (FDA),…
The paper develops methods to estimate power spectra from quantized sensor data.
problem Estimating power spectra from limited and noisy sensor data.
method Linear compression and quantization of sensor measurements, combined with data-driven and prior information.
result Novel estimators can reconstruct power spectra with minimal bandwidth requirements.
New sensor placement affects robot controller learnability.
problem Catastrophic forgetting in neural controllers for robots.
method Demonstrated how sensor placement alters loss function manifolds.
result Sensor placement can reduce or induce catastrophic forgetting.
Low-cost sensor fusion for organic substance classification.
problem Non-destructive classification and quality control of organic substances.
method Arduino Mega 2560 microcontroller with three sensors, supervised learning models, hyperparameter tuning, PCA/LDA, ensemble voting classifier.
result Test accuracies of 93-94% for identifying organic compounds.
MEx dataset benchmarks HAR and multi-modal fusion for exercise quality.
problem Recognizing and evaluating exercise quality for Musculoskeletal Disorders patients.
method Multi-sensor, multi-modal dataset with four sensors (pressure mat, depth camera, accelerometers) for HAR and exercise quality assessment.
result Reference performance for each sensor identified, exposing their strengths and weaknesses.
Study on adversarial attacks on user identification systems using motion sensors.
problem Adversarial attacks on deep learning models for user identification based on motion sensors.
method Study of adversarial example generation methods and their impact on user identification systems.
result Deep neural networks trained for user identification based on motion sensors are vulnerable to adversarial attacks, leading to high misclassification rates.
Proposes a deep neural network for early disk drive failure prediction.
problem Early prediction of disk drive failure using multivariate time series sensor data.
method Enriched features derived from sensor data through transformations, combined with ensemble learning and deep neural network architecture.
result Significantly improved classification accuracy in predicting disk drive failure.
Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.
problem Characterize spatio-temporal variability of PM2.5 in Bujumbura.
method Used low-cost sensors to collect data from August 2022 to August 2023. Analyzed hourly, daily, and seasonal PM2.5 concentrations using RNN with LSTM.
result Hourly and annual PM2.5 concentrations in Bujumbura exceed WHO standards.
Bayesian model aligns wind turbine sensor data, revealing shared structure.
problem Finding common structure in wind turbine sensor data.
method Latent shared information, non-parametric output warpings, nested variational compression.
result Extracts shared information between wind turbine sensor data.
Detects which features have shifted in data distributions.
problem Identifying which specific features have caused a distribution shift.
method Formalizes the problem as multiple conditional distribution hypothesis tests, proposes non-parametric and parametric statistical tests, and uses a test statistic based on the density model score function.
result Demonstrates methods for identifying when and where a shift occurs in multivariate time-series data.
Proposes a spectral method for jointly smooth functions on multiple manifolds.
problem Registering measurements from different sensors and rejecting noise.
method Two steps: kernel subspace span and spectral method.
result Guaranteed orthogonal functions that are as jointly smooth as possible.
RAN model recognizes multiple activities from unlabeled sensor data.
problem Handling weakly labeled multi-activity data from wearable sensors.
method Recurrent Attention Networks (RAN) for sequential multi-activity recognition and localization.
result RAN model can infer multiple activities and determine activity locations from unlabeled data.
This work builds a sensor graph from DC sensors for anomaly detection.
problem Anomaly detection in data centers with complex sensor relationships.
method Data-driven pipeline (ts2graph) to build a sensor graph from sensor time series.
result Graph neural network (GNN) outperforms existing methods by 2-3 times in anomaly detection.
CoDATS improves DA on time series data with weak supervision.
problem Improving domain adaptation for time series data with limited labeled data.
method CoDATS model for Time Series data, DA-WS method with weak supervision.
result Significant accuracy improvements over state-of-the-art methods.
This research uses PointNets to detect 2D objects from radar data.
problem Detecting 2D objects from sparse radar data for automated driving.
method Adapting PointNets for radar data, performing 2D object classification and bounding box regression.
result Demonstrates the potential of PointNets for 2D object detection in radar data.
Optimizes energy efficiency in wireless sensor networks with limited information.
problem Maximizing energy efficiency in energy harvesting wireless sensor networks with limited channel state information.
method Modeling as a Multi-Armed Bandits problem and developing an Upper Confidence Bound algorithm.
result Significant gains in energy efficiency compared to benchmark schemes.
Develops efficient algorithms for spatial field reconstruction and sensor selection in heterogeneous weather sensor networks.
problem Efficient spatial field reconstruction and query-based sensor set selection in heterogeneous sensor networks.
method Spatial Best Linear Unbiased Estimator (S-BLUE) and Cross Entropy method.
result Efficient algorithms with performance guarantees for spatial field reconstruction and sensor selection.
NeuralPrefix fills in missing sensor data without additional training.
problem Data intermittency in real-world sensing.
method NeuralPrefix is a task-agnostic, zero-shot imputation framework.
result NeuralPrefix accurately recovers missing samples and generalizes to unseen datasets.