A new method for cross-matching fingerprints from different sensors.
problem Inefficiency in cross-matching fingerprints from different sensors.
method Co-occurrence of ridge orientations and Gabor-HoG descriptor fusion with CCA.
result Significant enhancement over state-of-the-art methods.
This study aims to improve communication between fragmented blockchain systems in finance.
problem Inefficient and insecure communication in fragmented blockchain systems.
method Analysis of cross-chain interoperability protocols and their properties.
result Comparison and evaluation of cross-chain interoperability protocols.
Interpool solves interoperability issues by minting, exchanging, and burning tokens within a single liquidity pool.
problem Lack of proper interoperability in blockchain use cases.
method Interpool operates as a standalone liquidity pool that mints, exchanges, and burns tokens, optimizing the order of transactions in the mempool.
result Interpool transforms front-running issues into a solution that ensures ultimate liquidity through a burning procedure, enabling trustless design.
Automated market-making for CBDCs and stable coins on blockchain.
problem Creating fair exchange rates for digital assets on blockchain.
method Developed an innovative approach for generating fair exchange rates.
result Illustrated the approach's efficacy on G-10 currency exchange rates.
The FAIRnets Ontology makes neural networks findable, accessible, interoperable, and reusable.
problem The resource-intensive training of neural networks and the lack of training data availability.
method Development of FAIRnets Ontology to model neural networks on a meta-level and creation of a knowledge graph (FAIRnets) of over 18,400 neural networks.
result The FAIRnets Ontology and knowledge graph enable the reuse and recommendation of neural networks to data scientists.
The study analyzes how cross-chain interoperability affects decentralized lending protocols' performance.
problem Understudied cross-chain elements in DeFi lending risk management.
method Panel regression fixed effects and OLS models applied to empirical analysis.
result Cross-chain activity impacts protocol performance, with bridge volume being a critical driver.
This study categorizes RWA tokenization challenges and solutions.
problem Navigating the gap between on-chain deterministic code and off-chain probabilistic reality.
method Taxonomy and comparative analysis of RWA protocols, legal and technical standards.
result RWA tokenization requires overcoming legal and technical interoperability issues.
Cardea automates machine learning for EHRs, improving model building efficiency.
problem Lack of a trusted, open-source framework for automated machine learning in EHRs.
method Uses FHIR for data structure, AUTOML frameworks for feature engineering, model selection, and tuning, and an adaptive data assembler.
result Demonstrates framework's effectiveness on 5 prediction tasks, highlighting its flexibility and human competitiveness.
AI generates a sequence of death causes from hospital records.
problem Accurate death reporting for vital statistics and policy formulation.
method Neural machine translation models to generate causal chains, incorporating medical domain knowledge.
result Achieved 16.04 BLEU score for generating accurate causal chains.
PEHRT harmonizes EHR data for translational research.
problem Barriers in using EHR data for translational research.
method Common pipeline including open-source code, visualization tools, and detailed documentation.
result PEHRT harmonizes EHR data to standardized ontologies and generates robust embeddings.
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.
Blockchain aims to improve trust in AI systems, but lacks systematic studies.
problem Lack of systematic studies on blockchain design principles for AI trust.
method Hybrid qualitative and quantitative studies.
result Vast opportunities for future research and practice in blockchain design.
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.
RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.
problem Calibrating LCAQ sensors against regulatory-grade monitors is expensive and time-consuming.
method PROvably outlier-resistant semi-parametric regression technique.
result RESPIRE offers improved prediction in cross-site, cross-season, and cross-sensor settings.
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.
Low-cost sensors improve air quality prediction accuracy significantly.
problem Improving air quality monitoring networks with affordable sensors.
method Developed a high-resolution air quality prediction engine using low-cost sensors and official data.
result The use of low-cost sensors improves prediction accuracy by 25% and 15% for PM2.5 and PM10 respectively in densely monitored areas.
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.
Adversarial approach enhances sensor fusion for robust target detection.
problem Improving target detection and classification using multi-modal sensor fusion.
method Generative network learns latent space from various sensor modalities, then detects damaged sensors and safeguards performance.
result Automatic robustness against noisy/damaged sensors achieved.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
In this paper, we present a framework to control a self-driving car by fusing raw information from RGB images and depth maps. A deep neural network architecture is used for mapping the vision and depth information, respectively, to steering commands. This fusion of information from two sensor sources allows to provide …
We address the two fundamental problems of spatial field reconstruction and sensor selection in heterogeneous sensor networks: (i) how to efficiently perform spatial field reconstruction based on measurements obtained simultaneously from networks with both high and low quality sensors; and (ii) how to perform query bas…
Regularized recurrent attention filter combines sensor inputs.
problem Combining information from different sensor modalities.
method Regularized recurrent attention filter, co-learning mechanism, probabilistic graphical model.
result Dynamic sensor fusion and latent representation recovery.
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.
ConvGNP improves sensor placement for climate monitoring.
problem Maximizing informativeness of environmental sensor placements in remote regions.
method Convolutional Gaussian neural processes (ConvGNP) for non-stationary spatial predictions.
result ConvGNP outperforms traditional GP models in predicting sensor performance and reducing uncertainty.
Sensor drift is a well-known issue in the field of sensors and measurement and has plagued the sensor community for many years. In this paper, we propose a sensor drift correction method to deal with the sensor drift problem. Specifically, we propose a discriminative subspace projection approach for sensor drift reduct…
This paper designs sensor arrays for estimating unsteady flows efficiently.
problem Estimating high-dimensional unsteady flow fields with limited sensor placement.
method Combines data-driven modeling, Kalman Filter design, and sparsification for sensor selection.
result Proposed sensor arrays are highly effective for flow-field estimation across various conditions.
Optimizes seismic monitoring networks using Bayesian OED.
problem Improve seismic event identification and location.
method Bayesian optimal experimental design (OED) to configure sensor networks.
result Optimized sensor network improves seismic event identification and location.
Robots rely on sensors to provide them with information about their surroundings. However, high-quality sensors can be extremely expensive and cost-prohibitive. Thus many robotic systems must make due with lower-quality sensors. Here we demonstrate via a case study how modeling a sensor can improve its efficacy when em…
With the rising number of interconnected devices and sensors, modeling distributed sensor networks is of increasing interest. Recurrent neural networks (RNN) are considered particularly well suited for modeling sensory and streaming data. When predicting future behavior, incorporating information from neighboring senso…
AutoEncoder smooths noisy sensor data and interpolates missing values.
problem Noisy sensor data and missing timepoints require interpolation.
method Uses AutoEncoder to denoise and interpolate data.
result AutoEncoder improves data quality and reveals dynamics.
Sensor fusion has wide applications in many domains including health care and autonomous systems. While the advent of deep learning has enabled promising multi-modal fusion of high-level features and end-to-end sensor fusion solutions, existing deep learning based sensor fusion techniques including deep gating architec…
Self-attention model improves HAR from wearable sensors.
problem Capturing spatio-temporal context from sensor data.
method Proposes a self-attention based neural network model.
result Significant performance improvement over state-of-the-art models.
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
Predicts traffic flow using reinforcement learning and sensor data.
problem Accurately predict expanding and evolving long-term streaming traffic networks.
method Formulates the problem as a continuous reinforcement learning task, where the agent predicts future traffic based on sensor data.
result The approach improves accuracy in predicting traffic flow by updating the agent's state representation over time.
Deep RL optimizes sensor placement in digital twins for dynamic data acquisition.
problem Limited applicability of traditional sensor placement techniques for online applications.
method Formulates sensor placement as a Markov decision process and uses deep reinforcement learning.
result Improves predictive accuracy and reliability of digital twins through adaptive sensor repositioning.
Weather balloons deploy sensors to collect stratospheric data.
problem Limited data collection in the stratosphere.
method Modeling forecast deviation as a Gaussian process to determine sensor release times; novel hardware system for optimal sensor release.
result Data engineering framework effectively collects stratospheric data through real flights and simulations.
The paper optimizes sensor selection for network time series data.
problem Optimizing sensor selection for network time series data with minimal error.
method Data-driven strategies to turn off sensors or select a sampling set of nodes.
result Proposes and compares various data-driven strategies for sensor selection.
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.
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors m…
VIDON learns operators with variable sensors, overcoming sensor limitations.
problem Fixed sensor locations restrict operator learning applicability.
method Variable-Input Deep Operator Network (VIDON) with random, varying sensors.
result VIDON efficiently approximates operators in PDEs and is robust to sensor permutations.
Deep RL predicts equipment maintenance from sensor data.
problem Equipment downtime due to sensor data overload.
method Model-free Deep Reinforcement Learning for optimal maintenance policy.
result Automatic maintenance policy learning from sensor data.
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…
Graph autoencoders enable ML across diverse sensor networks.
problem Deploying ML across different sensor networks with varying types or layouts.
method Graph Autoencoders for activity recognition across heterogeneous sensor networks.
result Transferable activity classifiers achieve 75% accuracy on unseen sensor layouts.
Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approac…
Deep learning improves sensor performance optimization.
problem Optimizing sensor performance based on key metrics.
method Re-approach non-linear regression using deep learning with Keras and Tensorflow.
result Deep learning models improve sensor performance optimization.
Surveying low-cost sensors for air quality monitoring and calibration.
problem Limited spatial resolution due to expensive environmental monitoring stations.
method Low-cost sensors with machine learning for calibration.
result Machine learning improves sensor accuracy over time.
Emerging wearable sensors have enabled the unprecedented ability to continuously monitor human activities for healthcare purposes. However, with so many ambient sensors collecting different measurements, it becomes important not only to maintain good monitoring accuracy, but also low power consumption to ensure sustain…