Search Results for author:
Found 48 papers, 11 papers with code
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.
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.
Stabilizing Subject Transfer in EEG Classification with Divergence Estimation
Classification models for electroencephalogram (EEG) data show a large decrease in performance when evaluated on unseen test sub jects.
Corticomorphic Hybrid CNN-SNN Architecture for EEG-based Low-footprint Low-latency Auditory Attention Detection
In this paper, we propose a hybrid convolutional neural network-spiking neural network (CNN-SNN) corticomorphic architecture, inspired by the auditory cortex, which uses EEG data along with multi-speaker speech envelopes to successfully decode auditory attention with low latency down to 1 second, using only 8 EEG electrodes strategically placed close to the auditory cortex, at a significantly higher accuracy of 91. 03%, compared to the state-of-the-art.
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 water mapping, land cover classification and deforestation detection
Our method also improves the performance of deep learning-based classifiers without the need for additional training data.
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).
Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI
Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering.
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.
AutoTransfer: Subject Transfer Learning with Censored Representations on Biosignals Data
We provide a regularization framework for subject transfer learning in which we seek to train an encoder and classifier to minimize classification loss, subject to a penalty measuring independence between the latent representation and the subject label.
Variation is the Norm: Brain State Dynamics Evoked By Emotional Video Clips
For the last several decades, emotion research has attempted to identify a "biomarker" or consistent pattern of brain activity to characterize a single category of emotion (e. g., fear) that will remain consistent across all instances of that category, regardless of individual and context.
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.
Efficient Modeling of Morphing Wing Flight Using Neural Networks and Cubature Rules
We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions.
Circular-Symmetric Correlation Layer based on FFT
Despite the vast success of standard planar convolutional neural networks, they are not the most efficient choice for analyzing signals that lie on an arbitrarily curved manifold, such as a cylinder.
EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signals
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification tasks.
On the Sample Complexity of Rank Regression from Pairwise Comparisons
We consider a rank regression setting, in which a dataset of $N$ samples with features in $\mathbb{R}^d$ is ranked by an oracle via $M$ pairwise comparisons.
Stochastic Mutual Information Gradient Estimation for Dimensionality Reduction Networks
We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient.
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues.
Inference of Upcoming Human Grasp Using EMG During Reach-to-Grasp Movement
We collected and utilized data from large gesture vocabularies with multiple dynamic actions to encode the transitions from one grasp intent to another based on common sequences of the grasp movements.
Multimodal Fusion of EMG and Vision for Human Grasp Intent Inference in Prosthetic Hand Control
Conclusion: Our experimental data analyses demonstrate that EMG and visual evidence show complementary strengths, and as a consequence, fusion of multimodal evidence can outperform each individual evidence modality at any given time.
EEG-based Texture Roughness Classification in Active Tactile Exploration with Invariant Representation Learning Networks
Multiple cortical brain regions are known to be responsible for sensory recognition, perception and motor execution during sensorimotor processing.
On the use of generative deep neural networks to synthesize artificial multichannel EEG signals
Recent promises of generative deep learning lately brought interest to its potential uses in neural engineering.
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand
For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities.
NetCut: Real-Time DNN Inference Using Layer Removal
Deep Learning plays a significant role in assisting humans in many aspects of their lives.
Universal Physiological Representation Learning with Soft-Disentangled Rateless Autoencoders
Human computer interaction (HCI) involves a multidisciplinary fusion of technologies, through which the control of external devices could be achieved by monitoring physiological status of users.
Disentangled Adversarial Autoencoder for Subject-Invariant Physiological Feature Extraction
Recent developments in biosignal processing have enabled users to exploit their physiological status for manipulating devices in a reliable and safe manner.
Stopping Criterion Design for Recursive Bayesian Classification: Analysis and Decision Geometry
In this paper, we propose a geometric interpretation over the state posterior progression and accordingly we provide a point-by-point analysis over the disadvantages of using such conventional termination criteria.
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference
Learning data representations that capture task-related features, but are invariant to nuisance variations remains a key challenge in machine learning.
Disentangled Adversarial Transfer Learning for Physiological Biosignals
Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways.
Active recursive Bayesian inference using Rényi information measures
The proposed active RBI framework is applied to the trajectory of the posterior changes in the probability simplex that provides a coordinated active querying and decision making with specified confidence.
Adversarial Feature Learning in Brain Interfacing: An Experimental Study on Eliminating Drowsiness Effects
Across- and within-recording variabilities in electroencephalographic (EEG) activity is a major limitation in EEG-based brain-computer interfaces (BCIs).
Information Theoretic Feature Transformation Learning for Brain Interfaces
Objective: A variety of pattern analysis techniques for model training in brain interfaces exploit neural feature dimensionality reduction based on feature ranking and selection heuristics.
Adversarial Deep Learning in EEG Biometrics
Deep learning methods for person identification based on electroencephalographic (EEG) brain activity encounters the problem of exploiting the temporally correlated structures or recording session specific variability within EEG.
Accelerated Experimental Design for Pairwise Comparisons
Pairwise comparison labels are more informative and less variable than class labels, but generating them poses a challenge: their number grows quadratically in the dataset size.
Transfer Learning in Brain-Computer Interfaces with Adversarial Variational Autoencoders
We introduce adversarial neural networks for representation learning as a novel approach to transfer learning in brain-computer interfaces (BCIs).
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.
Invariant Representations from Adversarially Censored Autoencoders
In this method, an adversarial network attempts to recover the nuisance variable from the representation, which the VAE is trained to prevent.
Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection
One of the major challenges in training such networks raises when data is unbalanced, which is common in many medical imaging applications such as lesion segmentation where lesion class voxels are often much lower in numbers than non-lesion voxels.
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization can achieve accurate results with a wide capture range in real-time (<100ms).
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time.
Tversky loss function for image segmentation using 3D fully convolutional deep networks
One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels.
Auto-context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging
Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines.
Asymptotic Analysis of Objectives based on Fisher Information in Active Learning
In particular, we show that FIR can be asymptotically viewed as an upper bound of the expected variance of the log-likelihood ratio.
Manifold unwrapping using density ridges
Research on manifold learning within a density ridge estimation framework has shown great potential in recent work for both estimation and de-noising of manifolds, building on the intuitive and well-defined notion of principal curves and surfaces.
