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A Novel Numerical Method for Relaxing the Minimal Configurations of TOA-Based Joint Sensors and Sources Localization
This work introduces a novel numerical method that relaxes the minimal configuration requirements for joint sensors and sources localization (JSSL) in 3D space using time of arrival (TOA) measurements.
Concerns for Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals
This document presents some insights and observations regarding the paper that was published in IEEE Transactions on Signal Processing (TSP), titled "Self-Localization of Ad-Hoc Arrays Using Time Difference of Arrivals".
Low Rank Properties for Estimating Microphones Start Time and Sources Emission Time
Utilizing this augmented low-rank structural information from both LRP and the proposed variants, we formulate four linear constraints on the UTIm.
Are Microphone Signals Alone Sufficient for Self-Positioning?
In an era where asynchronous environments pose challenges to traditional self-positioning methods, we propose a new transformation to the existing paradigm.
Justices for Information Bottleneck Theory
This study comes as a timely response to mounting criticism of the information bottleneck (IB) theory, injecting fresh perspectives to rectify misconceptions and reaffirm its validity.
Conditioning Optimization of Extreme Learning Machine by Multitask Beetle Antennae Swarm Algorithm
Extreme learning machine (ELM) as a simple and rapid neural network has been shown its good performance in various areas.
Does Normalization Methods Play a Role for Hyperspectral Image Classification?
For Hyperspectral image (HSI) datasets, each class have their salient feature and classifiers classify HSI datasets according to the class's saliency features, however, there will be different salient features when use different normalization method.
Sparse Representation Based Augmented Multinomial Logistic Extreme Learning Machine with Weighted Composite Features for Spectral Spatial Hyperspectral Image Classification
To tackle these two problems, in this paper, we propose a new framework for ELM based spectral-spatial classification of HSI, where probabilistic modelling with sparse representation and weighted composite features (WCF) are employed respectively to derive the op-timized output weights and extract spatial features.
Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification
Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values.
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image
As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances.
