Nguyen Linh-Trung
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View article: Goal-Oriented Resource Allocation and Scheduling for Integrated Sensing and Communications
Goal-Oriented Resource Allocation and Scheduling for Integrated Sensing and Communications Open
International audience
View article: Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis
Latent Representation Learning from 3D Brain MRI for Interpretable Prediction in Multiple Sclerosis Open
We present InfoVAE-Med3D, a latent-representation learning approach for 3D brain MRI that targets interpretable biomarkers of cognitive decline. Standard statistical models and shallow machine learning often lack power, while most deep lea…
View article: Robust Sparse Subspace Tracking from Corrupted Data Observations
Robust Sparse Subspace Tracking from Corrupted Data Observations Open
Subspace tracking is a fundamental problem in signal processing, where the goal is to estimate and track the underlying subspace that spans a sequence of data streams over time. In high-dimensional settings, data samples are often corrupte…
View article: Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects
Comparative Evaluation of Graph Construction Methods for Individual Brain Metabolic Network from FDG-PET Images: an ADNI study in Healthy Subjects Open
Purpose: Connectivity analyses of fluorodeoxyglucose positron emission tomography (FDG-PET) static images provides a valuable means of investigating brain network organization by capturing metabolic activity at rest. Graph theory is emerge…
View article: FDG-PET-based brain network analysis: a brief review of metabolic connectivity
FDG-PET-based brain network analysis: a brief review of metabolic connectivity Open
This review provides an insight into how graph theory can be used to study metabolic connectivity patterns under various conditions including neurological and psychiatric disorders.
View article: Low-Cost Blind and Semi-Blind Equalizers for Nonlinear SIMO Systems
Low-Cost Blind and Semi-Blind Equalizers for Nonlinear SIMO Systems Open
Nonlinearities in systems, such as those encountered in optical and satellite communications, can significantly degrade signal quality and require advanced equalization techniques for effective compensation. This work introduces an innovat…
View article: Enhancing Depression Diagnosis Using FDG-PET Images with HyperGraphs
Enhancing Depression Diagnosis Using FDG-PET Images with HyperGraphs Open
International audience
View article: Wasserstein-Based Distance for Constructing Multi-Scale Individual Brain Networks from FDG-PET Images: Application to Alzheimer's Disease Diagnosis
Wasserstein-Based Distance for Constructing Multi-Scale Individual Brain Networks from FDG-PET Images: Application to Alzheimer's Disease Diagnosis Open
International audience
View article: Tensor Kernel Learning for Classification of Alzheimer’s Conditions using Multimodal Data
Tensor Kernel Learning for Classification of Alzheimer’s Conditions using Multimodal Data Open
View article: Semi-Supervised Learning for Anomaly Detection in Blockchain-based Supply Chains
Semi-Supervised Learning for Anomaly Detection in Blockchain-based Supply Chains Open
Blockchain-based supply chain (BSC) systems have tremendously been developed recently and can play an important role in our society in the future. In this study, we develop an anomaly detection model for BSC systems. Our proposed model can…
View article: Enhancing Feature Selection in MCI Diagnosis using FDG-PET Images: Leveraging Multiple Simple Autoencoder Architectures
Enhancing Feature Selection in MCI Diagnosis using FDG-PET Images: Leveraging Multiple Simple Autoencoder Architectures Open
Alzheimer’s Disease (AD) is the most common type of neurodegenerative brain disease in elderly people. Early diagnosis of AD is crucial for providing suitable care. Positron Emission Tomography (PET) images and machine learning can be used…
View article: Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks
Real-time Cyberattack Detection with Collaborative Learning for Blockchain Networks Open
With the ever-increasing popularity of blockchain applications, securing\nblockchain networks plays a critical role in these cyber systems. In this\npaper, we first study cyberattacks (e.g., flooding of transactions, brute pass)\nin blockc…
View article: Spatially temporally distributed informative path planning for multi-robot systems
Spatially temporally distributed informative path planning for multi-robot systems Open
This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their moveme…
View article: Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease
Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease Open
Alzheimer's Disease (AD) is the leading cause of dementia, affecting brain structure, function, cognition, and behaviour. While previous studies have linked brain regions to univariate outcomes (e.g., disease status), the relationship betw…
View article: Securing Channel State Information via Fake Path Injection in SIMO Communication
Securing Channel State Information via Fake Path Injection in SIMO Communication Open
International audience
View article: Physical Layer Location Privacy in SIMO Communication Using Fake Path Injection
Physical Layer Location Privacy in SIMO Communication Using Fake Path Injection Open
Fake path injection is an emerging paradigm for inducing privacy over wireless networks. In this paper, fake paths are injected by the transmitters into a single-input multiple-output (SIMO) communication channel to obscure their physical …
View article: Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing
Cascaded Thinning in Upscale and Downscale Representation for EEG Signal Processing Open
Smoothing filters are widely used in EEG signal processing for noise removal while preserving signals' features. Inspired by our recent work on Upscale and Downscale Representation (UDR), this paper proposes a cascade arrangement of some e…
View article: A New Framework for Cyber Risk Assessment for IIoT and Recommendations for Vietnam
A New Framework for Cyber Risk Assessment for IIoT and Recommendations for Vietnam Open
Industry 4.0 has brought huge benefits to a wide range of industries. Its development, however, has raised more cyber security risks in both Information Technology (IT) and Operational Technology (OT) systems. In this paper, potential cybe…
View article: Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI
Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI Open
Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g.…
View article: On the Semi-Blind Mutually Referenced Equalizers for MIMO Systems
On the Semi-Blind Mutually Referenced Equalizers for MIMO Systems Open
Minimizing training overhead in channel estimation is a crucial challenge in wireless communication systems. This paper presents an extension of the traditional blind algorithm, called "Mutually referenced equalizers" (MRE), specifically d…
View article: A Novel Recursive Least-Squares Adaptive Method For Streaming Tensor-Train Decomposition With Incomplete Observations
A Novel Recursive Least-Squares Adaptive Method For Streaming Tensor-Train Decomposition With Incomplete Observations Open
Abstract: Tensor tracking which is referred to as online (adaptive) decomposition of streaming tensors has recently gained much attention in the signal processing community due to the fact that many modern applications gen…
View article: Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts
Collaborative Learning Framework to Detect Attacks in Transactions and Smart Contracts Open
With the escalating prevalence of malicious activities exploiting vulnerabilities in blockchain systems, there is an urgent requirement for robust attack detection mechanisms. To address this challenge, this paper presents a novel collabor…
View article: Fisher information estimation using neural networks
Fisher information estimation using neural networks Open
In estimation theory, the Fisher information matrix (FIM) is a fundamental concept from which we can infer the well-known Cramér-Rao bound. A closed-form expression of the FIM is often intractable due to the lack or sophistication of stati…
View article: Intense squeezed light from lasers with sharply nonlinear gain at optical frequencies
Intense squeezed light from lasers with sharply nonlinear gain at optical frequencies Open
Non-classical states of light, such as number-squeezed light, with fluctuations below the classical shot noise level, have important uses in metrology, communication, quantum information processing, and quantum simulation. However, generat…
View article: Tracking online low-rank approximations of higher-order incomplete streaming tensors
Tracking online low-rank approximations of higher-order incomplete streaming tensors Open
In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recu…
View article: Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors
Tracking Online Low-Rank Approximations of Higher-Order Incomplete Streaming Tensors Open
Abstract: In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD)…
View article: Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems
Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems Open
The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitation…
View article: Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems
Causal Deep Operator Networks for Data-Driven Modeling of Dynamical Systems Open
The deep operator network (DeepONet) architecture is a promising approach for learning functional operators, that can represent dynamical systems described by ordinary or partial differential equations. However, it has two major limitation…
View article: One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels
One-Bit Massive MIMO Precoding for Frequency-Selective Fading Channels Open
One-bit digital-to-analog converters (DACs) are a practical and promising solution for reducing cost and power consumption in massive multiple-input multiple-output (MIMO) systems. However, the one-bit precoding problem is NP-hard and even…
View article: Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration
Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration Open