Blar i Institutt for elektroniske systemer på dokumenttype "Chapter"
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Consensus-based Distributed Total Least-squares Estimation Using Parametric Semidefinite Programming
(Chapter, 2019)We propose a new distributed algorithm to solve the total least-squares (TLS) problem when data are distributed over a multi-agent network. To develop the proposed algorithm, named distributed ADMM TLS (DA-TLS), we reformulate ... -
Continual local updates for federated learning with enhanced robustness to link noise
(Chapter, 2023)Communication errors caused by noisy links can negatively impact the accuracy of federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that is robust to communication errors while concurrently ... -
Cooperative renewable energy management with distributed generation
(Chapter, 2018)We propose an energy cost minimization strategy for cooperating households equipped with renewable energy generation and storage capabilities. The participating households minimize their collective energy expenditure by ... -
Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering
(Chapter; Peer reviewed, 2019)Abstract—This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzan- tine agents share manipulated data with their neighboring agents. The attack is assumed to be ... -
Cost-Aware Dual Prediction Scheme for Reducing Transmissions at IoT Sensor Nodes
(Chapter, 2019)This paper develops a method for deciding when to update the prediction model or transmit a set of measurements from the sensor to the fusion centre (FC) to achieve minimal data transmission in a dual prediction scheme ... -
Data-Driven Personalized Cervical Cancer Risk Prediction: A Graph-Perspective
(Chapter, 2021)Routine cervical cancer screening at regular periodic intervals leads to either over-screening or too infrequent screening of patients. For this purpose, personalized screening intervals are desirable that account for ... -
Decentralized Graph Federated Multitask Learning for Streaming Data
(Chapter, 2022)In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture ... -
Decentralized Graph Federated Multitask Learning for Streaming Data
(Annual Conference on Information Sciences and Systems (CISS);56, Chapter, 2022)In federated learning (FL), multiple clients connected to a single server train a global model based on locally stored data without revealing their data to the server or other clients. Nonetheless, the current FL architecture ... -
Deep Complex Convolutional Recurrent Network for Multi-Channel Speech Enhancement and Dereverberation
(Chapter, 2021)This paper proposes a neural network based system for multi-channel speech enhancement and dereverberation. Speech recorded indoors by a far field microphone, is invariably degraded by noise and reflections. Recent single ... -
Digital Significance
(Chapter, 2023)This chapter proposes the idea of ‘digital significance’ as a governance and decision- making process for assessing the value of digital collections. This concept is inspired by Australian approaches to valuing heritage, ... -
Distributed Kalman Filtering with Privacy against Honest-but-Curious Adversaries
(Chapter, 2021)This paper proposes a privacy-preserving distributed Kalman filter (PP-DKF) to protect the private information of individual network agents from being acquired by honest-but-curious (HBC) adversaries. The proposed approach ... -
Distributed Kalman filtering: Consensus, diffusion, and mixed
(Chapter, 2018)A distributed Kalman filtering technique is developed for tracking state-space processes via sensor networks. Considering the optimal solution to multi-agent sequential filtering of linear Gaussian state-space processes, ... -
Distributed Learning over Networks with Non-Smooth Regularizers and Feature Partitioning
(Chapter, 2021)We develop a new algorithm for distributed learning with non-smooth regularizers and feature partitioning. To this end, we transform the underlying optimization problem into a suitable dual form and solve it using the ... -
Distributed Learning with Non-Smooth Objective Functions
(Chapter, 2020)We develop a new distributed algorithm to solve a learning problem with non-smooth objective functions when data are distributed over a multi-agent network. We employ a zeroth-order method to minimize the associated augmented ... -
Distributed Ridge Regression with Feature Partitioning
(Chapter, 2019)We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of ... -
A DNN Based Speech Enhancement Approach to Noise Robust Acoustic-to-Articulatory Inversion
(Chapter, 2021)In this work, we investigate the problem of speaker independent acoustic-to-articulatory inversion (AAI) in noisy condition within the deep neural network (DNN) framework. We claim that DNN vector-to-vector regression for ... -
Dynamic Graph Topology Learning with Non-Convex Penalties
(Chapter, 2022)This paper presents a majorization-minimization-based framework for learning time-varying graphs from spatial-temporal measurements with non-convex penalties. The proposed approach infers time-varying graphs by using the ... -
Experimental phantom-based evaluation of Physical Layer Security for Future Leadless Cardiac Pacemaker
(Chapter, 2018)Next generation of cardiac pacemakers are expected to be completely wireless bringing along new security threats. Thus, it is critical to secure the pacemaker transmissions between legitimate nodes from a third party or ... -
Extracellular Vesicle Propagation in Acidic Tumor Microenvironment
(Chapter, 2022) -
Extraction of Interface Wave Dispersion Curves from Ocean Ambient Noise
(Chapter, 2019)