document

Elimination of traffic induced operational variability with Robust Principal Component Analysis (rPCA)

Sigurðsson, Snorri Þór (author)

Regular maintenance of civil engineering structures is essential for their safety. Current maintenance regimes involve periodic inspections at regular time intervals. In the time between inspections, there can be a critical development in the structural integrity of a structure, which can be expensive to repair or could even lead to structural...

master thesis 2023

document

Output Controllability of a Linear Dynamical System with Sparse Controls

Joseph, G. (author)

In this article, we study the conditions to be satisfied by a discrete-time linear system to ensure output controllability using sparse control inputs. A set of necessary and sufficient conditions can be directly obtained by extending the Kalman rank test for output controllability. However, the verification of these conditions is...

journal article 2023

document

A Survey on Accelerating Sparse CNN Inference on GPUs

Chen, Qilin (author)

Convolutional neural networks (CNNs) are often pruned to achieve faster training and inference speed while also requiring less memory. Nevertheless, during computation, most modern GPUs cannot take advantage of the sparsity automatically, especially on networks with unstructured sparsity. Therefore, many libraries that exploit sparsity, have...

bachelor thesis 2022

document

Fusion of Data from Multiple Automotive Radars for High-Resolution DoA Estimation

Suvarna, Anusha Ravish (author), Koppelaar, Arie (author), Jansen, Feike (author), Wang, J. (author), Yarovoy, Alexander (author)

High angular resolution is in high demand in automotive radar. To achieve a high azimuth resolution a large aperture antenna array is required. Although MIMO technique can be used to form larger virtual apertures, a large number of transmitter-receiver channels are needed, which is still technologically challenging and costly. To circumvent this...

conference paper 2022

document

Sparse Bayesian deep learning for dynamic system identification

Zhou, H. (author), Chahine, I. (author), Zheng, Wei Xing (author), Pan, W. (author)

This paper proposes a sparse Bayesian treatment of deep neural networks (DNNs) for system identification. Although DNNs show impressive approximation ability in various fields, several challenges still exist for system identification problems. First, DNNs are known to be too complex that they can easily overfit the training data. Second, the...

journal article 2022

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State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

Joseph, G. (author), Varshney, Pramod K. (author)

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...

conference paper 2022

document

Interference Mitigation for FMCW Radar With Sparse and Low-Rank Hankel Matrix Decomposition

Wang, J. (author), Ding, Ming (author), Yarovoy, Alexander (author)

In this paper, the interference mitigation for Frequency Modulated Continuous Wave (FMCW) radar system with a dechirping receiver is investigated. After dechirping operation, the scattered signals from targets result in beat signals, i.e., the sum of complex exponentials while the interferences lead to chirp-like short pulses. Taking...

journal article 2022

document

Temporal Delta Layer: Training Towards Brain Inspired Temporal Sparsity for Energy Efficient Deep Neural Networks

Preetha Vijayan, Preetha (author)

In the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and...

master thesis 2021

document

Principal Component hierarchy for sparse quadratic programs

Vreugdenhil, Robbie (author)

We propose a novel approximation hierarchy for cardinality-constrained, convex quadraticprograms that exploits the rank-dominating eigenvectors of the quadratic matrix. Each levelof approximation admits a min-max characterization whose objective function can be op-timized over the binary variables analytically, while preserving convexity in the...

master thesis 2021

document

Sparsity maximization approach to multiple daily injections therapy for type 1 diabetic patients

van Wilsum, Max (author)

Based on sparsity maximization we propose a controller for Multiple Daily Injections (MDI)therapy for Type 1 Diabetes melitus (T1DM) individuals. Based on convex relaxations onthe sparsity maximization problems and by implementing personalised linear models of pa-tient we formulate a model predictive controller to determine insulin boluses. By...

master thesis 2021

document

Differentiating Task-Based Functional Ultrasound Signals via Data-Driven Decompositions

Enthoven, Maarten (author)

At the Center for Ultrasound and Brain imaging at Erasmus MC in Rotterdam, a mouse's visual cortex had been imaged using the fUS technique. The mouse had been exposed to different visual stimuli. The stimuli varied in position, size, and shape. We investigate how the measured task-based fUS signals differ depending on the visual stimuli...

master thesis 2021

document

On the Atoms of Robustness: Robust Matrix Decomposition for Spectral Imaging

Moens, Roger (author)

Modern imaging modalities across many application domains increasingly acquire a large number of very high-dimensional measurements, commonly collecting hundreds to millions of variables per spatial resolution element. That high-dimensional nature can severely challenge traditional (often Euclidean distance based) approaches to noise and...

master thesis 2021

document

Bayesian Deep Learning for Dynamic System Identification

Chahine, Ibrahim (author)

System identification is a mature field in physical sciences and an emerging field in social sciences, with a vast range of applications. Nevertheless, it remains of great focus in academia. The main challenge is the efficient use of data to generate good model fits. System identification involves multi-disciplinary techniques from statistical,...

master thesis 2021

document

Optimization of virtually aperiodic linear sparse arrays

Aslan, Y. (author)

Multi-port multi-mode antenna elements have the ability to move their phase centers and modify their radiation patterns electronically. Arrays composed of such elements are referred to as virtually aperiodic arrays in this paper. Herein, optimization of the mode excitation coefficients in virtually aperiodic sparse linear arrays is proposed,...

journal article 2021

document

Design of Sparse Multiband Signal for Precise Positioning with Joint Low-Complexity Time Delay and Carrier Phase Estimation

Dun, H. (author), Tiberius, C.C.J.M. (author), Diouf, C.E.V. (author), Janssen, G.J.M. (author)

This paper presents a methodology to design a sparse multiband ranging signal with a large virtual bandwidth, from which time delay and carrier phase are estimated by a low complexity multivariate maximum likelihood (ML) method. In the estimation model for a multipath channel, not all reflected paths are considered, and time delay and carrier...

journal article 2021

document

Identify dominant dimensions of 3D hand shapes using statistical shape model and deep neural network

Yang, Y. (author), Zhou, H. (author), Song, Y. (author), Vink, P. (author)

Hand anthropometry is one of the fundamentals of ergonomic research and product design. Many studies have been conducted to analyze the hand dimensions among different populations, however, the definitions and the numbers of those dimensions were usually selected based on the experience of the researchers and the available equipment. Few...

journal article 2021

document

Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting

Nagtegaal, M.A. (author), Koken, Peter (author), Amthor, Thomas (author), Doneva, Mariya (author)

<br/>Purpose<br/><br/>To develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.<br/>Methods<br/><br/>Different tissues or components within a voxel are potentially separable in MRF because of...

journal article 2020

document

Myelin water imaging from multi-echo T₂ MR relaxometry data using a joint sparsity constraint

Nagtegaal, M.A. (author), Koken, Peter (author), Amthor, Thomas (author), de Bresser, Jeroen (author), Mädler, Burkhard (author), Vos, F.M. (author), Doneva, Mariya (author)

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce...

journal article 2020

document

Bayesian Learning Applied to Radio Astronomy Image Formation

Tang, Yajie (author)

Radio astronomy image formation can be treated as a linear inverse problem. However, due to physical limitations, this inverse problem is ill-posed. To overcome the ill-posedness, side information should be involved. Based on the sparsity assumption of the sky image, we involve l1-regularization. We formulate the image formation problem into a...

master thesis 2019

document

Sparsity based hybrid system identification using a SAT solver

Zwart, Joost (author)

System identification for switched linear systems from input output data has received substantial attention in recent years. There is a growing interest for techniques that pose the identification problem as a sparse optimisation problem. At the same time a vast amount of research is dedicated to improving SAT solvers which as a result become...

master thesis 2019