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31st European signal processing Conference, EUSIPCO 2023

作者： Behmandpoor, Pourya Patrinos, Panagiotis Moonen, Marc STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (纸本)9789464593600

Decentralized deep learning (DL) based resource allocation (RA) in communication networks guarantees scalability and higher communication bandwidth efficiency compared to centralized RA. Although the RA is decentralized in such approaches, the policies are mostly trained in a centralized manner. In this paper, we investigate a decentralized model-free training approach based on zeroth-order optimization methods. Each user trains its individual policy-possibly with a unique structure-to guarantee the maximum global utility, e.g., sum rate (SR) of users. More importantly, during the training, the users need to share only scalar quantities with their neighbors, avoiding a large communication overhead. The training is also robust against a certain level of asynchrony between the users. The proposed approach relaxes the need for a computationally complex central server and offers the possibility for (re)training in dynamic environments in a model-free manner using the computational power at the edge. Numerical experiments show a competitive performance compared to centralized and federated training approaches. © 2023 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Resource allocation

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A Distributed Adaptive Algorithm for Node-Specific signal Fusion Problems in Wireless Sensor Networks 31

A Distributed Adaptive Algorithm for Node-Specific Signal Fu...

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31st European signal processing Conference, EUSIPCO 2023

作者： Musluoglu, Cem Ates Bertrand, Alexander STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

ISBN: (纸本)9789464593600

The distributed adaptive signal fusion (DASF) framework has been proposed as a generic method to solve spatial filtering and signal fusion problems in a distributed fashion over a wireless sensor network, reducing the communication and energy costs compared to a centralized approach. The DASF framework assumes that there is a common goal across the nodes, i.e., all nodes collaborate to optimize the same network-wide objective function. However, some applications require node-specific objectives, which are linked via a common latent target signal subspace. In this work, we propose the distributed adaptive node-specific signal fusion (DANSF) algorithm which builds upon the DASF framework, and extends it to allow for such node-specific spatial filtering problems. © 2023 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Adaptive algorithms

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EX-DSS: An Explorative Decision Support System for Designing and Deploying Smart Plug Forecasting Pipelines 16

EX-DSS: An Explorative Decision Support System for Designing...

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16th International Conference on Knowledge Management and Information Systems, KMIS 2024 as part of 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2024

作者： Rinaldi, Giulia Botman, Lola Agudelo, Oscar Mauricio De Moor, Bart STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Kasteelpark Arenberg 10 Leuven3001 Belgium

ISBN: (纸本)9789897587160

Artificial Intelligence pipelines are increasingly used to address specific challenges, such as forecasting smart plug loads. Smart plugs, which remotely control various appliances, can significantly reduce energy consumption in commercial buildings by about 20% when effectively scheduled using AI techniques. Designing these AI pipelines involves numerous steps and variables, requiring collaboration and shared knowledge among designers. A Decision Support System (DSS) can facilitate this process. This paper introduces the Explorative Decision Support System (EX-DSS), which extends the classical DSS framework. The EX-DSS integrates an Explorative Management Subsystem to provide project-specific recommendations and a data Quality (DQ) module to validate user inputs, ensuring clarity and enhancing information sharing. The EX-DSS architecture framework was tested through a software prototype designed to create AI pipelines for forecasting smart plug loads. The study found that using the EX-DSS improves the quality of suggestions, making them more problem-specific and resulting in a more personalized and meaningful user experience, with a significant potential to reduce energy consumption in commercial buildings. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Software testing

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A Distributed Adaptive Algorithm for Non-Smooth Spatial Filtering Problems 48

A Distributed Adaptive Algorithm for Non-Smooth Spatial Filt...

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48th IEEE International Conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Hovine, Charles Bertrand, Alexander Leuven Belgium Signal Processing and Data Analytics Stadius Center for Dynamical Systems Leuven Belgium Leuven Belgium

ISBN: (纸本)9781728163277

Computing the optimal solution to a spatial filtering problems in a Wireless Sensor Network can incur large bandwidth and computational requirements if an approach relying on data centralization is used. The so-called distributed adaptive signal fusion (DASF) algorithm solves this problem by having the nodes collaboratively solve low-dimensional versions of the original optimization problem, relying solely on the exchange of compressed views of the sensor data between the nodes. However, the DASF algorithm has only been shown to converge for filtering problems that can be expressed as smooth optimization problems. In this paper, we explore an extension of the DASF algorithm to a family of non-smooth spatial filtering problems, allowing the addition of non-smooth regularizers to the optimization problem, which could for example be used to perform node selection, and eliminate nodes not contributing to the filter objective, therefore further reducing communication costs. We provide a convergence proof of the non-smooth DASF algorithm and validate its convergence via simulations in both a static and adaptive setting. © 2023 IEEE.

关键词： Adaptive algorithms

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PYROADACOUSTICS: A ROAD ACOUSTICS SIMULATOR BASED ON VARIABLE LENGTH DELAY LINES 25

PYROADACOUSTICS: A ROAD ACOUSTICS SIMULATOR BASED ON VARIABL...

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25th International Conference on Digital Audio Effects, DAFx 2022

作者： Damiano, Stefano van Waterschoot, Toon STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium

ISBN: (纸本)9783200085992

In the development of algorithms for sound source detection, identification and localization, having the possibility to generate datasets in a flexible and fast way is of utmost importance. However, most of the available acoustic simulators used for this purpose target indoor applications, and their usefulness is limited when it comes to outdoor environments such as that of a road, involving fast moving sources and long distances travelled by the sound waves. In this paper we present an acoustic propagation simulator specifically designed for road scenarios. In particular, the proposed Python software package enables to simulate the observed sound resulting from a source moving on an arbitrary trajectory relative to the observer, exploiting variable length delay lines to implement sound propagation and Doppler effect. An acoustic model of the road reflection and air absorption properties has been designed and implemented using digital FIR filters. The architecture of the proposed software is flexible and open to extensions, allowing the package to kick-start the implementation of further outdoor acoustic simulation scenarios. Copyright: © 2022 Stefano Damiano et al.

关键词： Roads and streets

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Topology-independent GEVD-based distributed adaptive node-specific signal estimation in ad-hoc wireless acoustic sensor networks

arXiv

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arXiv 2024年

作者： Didier, Paul van Waterschoot, Toon Moonen, Marc STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

A low-rank approximation-based version of the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm is introduced, using a generalized eigenvalue decomposition (GEVD) for application in ad-hoc wireless acoustic sensor networks. This TI-GEVD-DANSE algorithm as well as the original TI-DANSE algorithm exhibit a non-strict convergence, which can lead to numerical instability over time, particularly in scenarios where the estimation of accurate spatial covariance matrices is challenging. An adaptive filter coefficient normalization strategy is proposed to mitigate this issue and enable the stable performance of TI-(GEVD-)DANSE. The method is validated in numerical simulations including dynamic acoustic scenarios, demonstrating the importance of the additional normalization. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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ONE-SHOT DISTRIBUTED NODE-SPECIFIC signal ESTIMATION WITH NON-OVERLAPPING LATENT SUBSPACES IN ACOUSTIC SENSOR NETWORKS

arXiv

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arXiv 2024年

作者： Didier, Paul Behmandpoor, Pourya van Waterschoot, Toon Moonen, Marc STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

A one-shot algorithm called iterationless DANSE (iDANSE) is introduced to perform distributed adaptive node-specific signal estimation (DANSE) in a fully connected wireless acoustic sensor network (WASN) deployed in an environment with non-overlapping latent signal subspaces. The iDANSE algorithm matches the performance of a centralized algorithm in a single processing cycle while devices exchange fused versions of their multichannel local microphone signals. Key advantages of iDANSE over currently available solutions are its iterationless nature, which favors deployment in real-time applications, and the fact that devices can exchange fewer fused signals than the number of latent sources in the environment. The proposed method is validated in numerical simulations including a speech enhancement scenario. © 2024, CC BY.

关键词： Sensor nodes

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Dss4ex: A Decision Support System Framework to Explore Artificial Intelligence Pipelines with an Application in Time Series Forecasting

SSRN

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SSRN 2024年

作者： Rinaldi, Giulia Theodorakos, Konstantinos Garcia, Fernando Crema Agudelo, Oscar Mauricio De Moor, Bart STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Belgium

Understanding complex Artificial Intelligence (AI) pipelines for time series forecasting can be challenging for both experts and non-experts. This work introduces DSS4EX, a Decision Support System (DSS) framework designed to facilitate the exploration and comprehension of AI pipelines. The framework is demonstrated through a software demo specifically tailored for electricity demand forecasting that utilizes the Decomposition-Residuals Deep Neural Network (DR-DNN) pipeline. The dataset used in the software demo, spanning March 18th, 2017, to February 16th, 2021, contains seven hourly time series from an unknown location. It covers 34,360 timesteps of data, including power demand, air pressure, cloud coverage, humidity, temperature, wind direction, and wind speed. The software demo was created to showcase the usability of the DSS4EX framework, allowing users to interactively configure, visualize, and understand the AI pipeline. This interactive approach enhances user engagement and comprehension, promoting informed decision-making. The DSS4EX framework makes complex models more transparent and accessible, bridging the gap between advanced AI models and user understanding. This research demonstrates the potential of DSS4EX in supporting further advancements in AI applications by making sophisticated AI techniques more understandable and usable for a broader audience. © 2024, The Authors. All rights reserved.

关键词： Application programs

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Stimulus-Informed Generalized Canonical Correlation Analysis of Stimulus-Following Brain Responses 31

Stimulus-Informed Generalized Canonical Correlation Analysis...

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31st European signal processing Conference, EUSIPCO 2023

作者： Geirnaert, Simon Francart, Tom Bertrand, Alexander STADIUS Center for Dynamical Systems Signal Processing and Data Analytics Leuven Belgium KU Leuven Department of Neurosciences ExpORL Leuven Belgium Leuven Belgium

ISBN: (纸本)9789464593600

In brain-computer interface or neuroscience applications, generalized canonical correlation analysis (GCCA) is often used to extract correlated signal components in the neural activity of different subjects attending to the same stimulus. This allows quantifying the so-called inter-subject correlation or boosting the signal-to-noise ratio of the stimulus-following brain responses with respect to other (non-)neural activity. GCCA is, however, stimulus-unaware: it does not take the stimulus information into account and does therefore not cope well with lower amounts of data or smaller groups of subjects. We propose a novel stimulus-informed GCCA algorithm based on the MAXVAR-GCCA framework. We show the superiority of the proposed stimulus-informed GCCA method based on the inter-subject correlation between electroencephalography responses of a group of subjects listening to the same speech stimulus, especially for lower amounts of data or smaller groups of subjects. © 2023 European signal processing Conference, EUSIPCO. All rights reserved.

关键词： Electroencephalography

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Least Squares Projection Onto the Behavior for SISO LTI Models ⁎

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IFAC-PapersOnLine 2024年第15期58卷 336-341页

作者： S. Lagauw B. De Moor Center for Dynamical Systems Signal Processing and Data Analytics (STADIUS) Department of Electrical Engineering (ESAT) KU Leuven Belgium

We consider the least squares projection onto the behavior for linear time-invariant (LTI) single-input single-output (SISO) models, in which the observed input-output data are modified in a least squares (LS) sense by subtracting so-called misfits, so that the modified data satisfy a given linear dynamic relation. We show that the LS-criterion of the projection problem induces an orthogonal decomposition of the ambient data space and we characterize this decomposition by means of banded block-Toeplitz matrices, the elements of which are the coefficients of the difference equation describing the SISO LTI dynamics. We thereby generalize earlier results in the literature on autonomous LTI models to the more complicated SISO case. Additionally, we illustrate that the novel characterization is equivalent (up to a change of model representation) to results derived using (isometric) state-space representations in the literature on behavioral systems theory.

关键词： Modeling identification linear systems errors in variables identification

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