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Found 7 papers, 0 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.
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.
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.
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.
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.
Decoding Emotional Experience through Physiological Signal Processing
Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner.
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.
