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Found 43 papers, 10 papers with code
A Bayesian Framework for Clustered Federated Learning
One of the main challenges of federated learning (FL) is handling non-independent and identically distributed (non-IID) client data, which may occur in practice due to unbalanced datasets and use of different data sources across clients.
AI-Aided Kalman Filters
We review both generic and dedicated DNN architectures suitable for state estimation, and provide a systematic presentation of techniques for fusing AI tools with KFs and for leveraging partial SS modeling and data, categorizing design approaches into task-oriented and SS model-oriented.
KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators
Furthermore they lack a systematic data-driven approach to perform data assimilation, that is, exploiting noisy measurements on the fly in the forecasting task.
Mitigation of Radar Range Deception Jamming Using Random Finite Sets
This paper presents a radar target tracking framework for addressing main-beam range deception jamming attacks using random finite sets (RFSs).
Continuously Optimizing Radar Placement with Model Predictive Path Integrals
Continuously optimizing sensor placement is essential for precise target localization in various military and civilian applications.
Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
To address this, we propose a fully Bayesian RATR framework employing Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
The proposed framework is capable of producing fast and accurate predictions over long time horizons, dealing with irregularly sampled noisy measurements to correct the solution, and benefits from the decoupling between the spatial and temporal dynamics of this class of PDEs.
On the Impact of Sampling on Deep Sequential State Estimation
In this paper, importance sampling is applied to the DKF framework for learning deep Markov models, resulting in the IW-DKF, which shows an improvement in terms of log-likelihood estimates and KL divergence between the variational distribution and the transition model.
Tubular Curvature Filter: Implicit Pointwise Curvature Calculation Method for Tubular Objects
This paper presents a Tubular Curvature Filter method that locally calculates the acceleration of bundles of curves that traverse along the tubular object parallel to the centerline.
Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder
New subjects only demonstrate the single component gestures and we seek to extrapolate from these to all possible single or combination gestures.
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.
A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time.
User Training with Error Augmentation for Electromyogram-based Gesture Classification
We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wrist-band configuration.
A Vision for Cleaner Rivers: Harnessing Snapshot Hyperspectral Imaging to Detect Macro-Plastic Litter
To address this problem, we analyze the feasibility of macro-plastic litter detection using computational imaging approaches in river-like scenarios.
Jammer classification with Federated Learning
Jamming signals can jeopardize the operation of GNSS receivers until denying its operation.
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.
Online Fusion of Multi-resolution Multispectral Images with Weakly Supervised Temporal Dynamics
To evaluate the proposed methodology we consider a water mapping task where real data acquired by the Landsat and MODIS instruments are fused generating high spatial-temporal resolution image estimates.
Recursive classification of satellite imaging time-series: An application to land cover mapping
Specifically, balanced classification accuracy improves by up to 26. 95% for SIC, 12. 4% for GMM, and 13. 81% for LR in water mapping, and by 15. 25%, 14. 17%, and 14. 7% in deforestation detection.
Jamming Source Localization Using Augmented Physics-Based Model
Monitoring interferences to satellite-based navigation systems is of paramount importance in order to reliably operate critical infrastructures, navigation systems, and a variety of applications relying on satellite-based positioning.
Bayesian data fusion with shared priors
When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.
Inv-SENnet: Invariant Self Expression Network for clustering under biased data
Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well.
Recursive Estimation of User Intent from Noninvasive Electroencephalography using Discriminative Models
We study the problem of inferring user intent from noninvasive electroencephalography (EEG) to restore communication for people with severe speech and physical impairments (SSPI).
Neural Network-based OFDM Receiver for Resource Constrained IoT Devices
Here, ML blocks replace the individual processing blocks of an OFDM receiver, and we specifically describe this swapping for the legacy channel estimation, symbol demapping, and decoding blocks with Neural Networks (NNs).
Online multi-resolution fusion of space-borne multispectral images
Satellite imaging has a central role in monitoring, detecting and estimating the intensity of key natural phenomena.
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation.
Cubature Kalman Filter Based Training of Hybrid Differential Equation Recurrent Neural Network Physiological Dynamic Models
Results demonstrate that state dynamics corresponding to the missing ODEs can be approximated well using a neural network trained using a recursive Bayesian filtering approach in a fashion coupled with the known state dynamic differential equations.
Personalized Federated Learning over non-IID Data for Indoor Localization
Localization and tracking of objects using data-driven methods is a popular topic due to the complexity in characterizing the physics of wireless channel propagation models.
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing
Autoencoder (AEC) networks have recently emerged as a promising approach to perform unsupervised hyperspectral unmixing (HU) by associating the latent representations with the abundances, the decoder with the mixing model and the encoder with its inverse.
Fast Unmixing and Change Detection in Multitemporal Hyperspectral Data
However, MESMA does not consider the relationship between the different HIs, and its computational complexity is extremely high for large spectral libraries.
Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review
The spectral signatures of the materials contained in hyperspectral images, also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an image.
Kalman Filtering and Expectation Maximization for Multitemporal Spectral Unmixing
The recent evolution of hyperspectral imaging technology and the proliferation of new emerging applications presses for the processing of multiple temporal hyperspectral images.
Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis
Multiple Endmember Spectral Mixture Analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering variability of the endmembers (EMs).
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing
Furthermore, we employ a theory-based statistical framework to devise a consistent strategy to estimate all required parameters, including both the regularization parameters of the algorithm and the number of superpixels of the transformation, resulting in a truly blind (from the parameters setting perspective) unmixing method.
Deep Generative Endmember Modeling: An Application to Unsupervised Spectral Unmixing
The proposed EM model is applied to the solution of a spectral unmixing problem, which we cast as an alternating nonlinear least-squares problem that is solved iteratively with respect to the abundances and to the low-dimensional representations of the EMs in the latent space of the deep generative model.
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor
Afterwards, we solve a matrix-factorization problem to estimate the fractional abundances using the variability scaling factors estimated in the previous step, what leads to a significantly more well-posed problem.
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral Variability
Recently, tensor-based strategies considered low-rank decompositions of hyperspectral images as an alternative to impose low-dimensional structures on the solutions of standard and multitemporal unmixing problems.
Super-Resolution for Hyperspectral and Multispectral Image Fusion Accounting for Seasonal Spectral Variability
This paper introduces a novel HS-MS image fusion strategy that combines an unmixing-based formulation with an explicit parametric model for typical spectral variability between the two images.
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability
Spectral variability in hyperspectral images can result from factors including environmental, illumination, atmospheric and temporal changes.
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing
Tensor-based methods have recently emerged as a more natural and effective formulation to address many problems in hyperspectral imaging.
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent limitations of endmember extraction algorithms in many applications.
Generalized linear mixing model accounting for endmember variability
Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images.
Technical Report: Band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem
Kernel-based nonlinear mixing models have been applied to unmix spectral information of hyperspectral images when the type of mixing occurring in the scene is too complex or unknown.
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images
The detection approach is based on the comparison of the reconstruction errors using both a Gaussian process regression model and a linear regression model.
