2022 30th European Signal Processing Conference (EUSIPCO)最新文献_第10页

Speech Enhancement in Distributed Microphone Arrays Using Polynomial Eigenvalue Decomposition 基于多项式特征值分解的分布式麦克风阵列语音增强

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909555

Emilie D'Olne, Vincent W. Neo, P. Naylor

引用次数: 2

A Minimum Variance Distortionless Response Spectral Estimator with Kronecker Product Filters 基于Kronecker积滤波器的最小方差无失真响应谱估计

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909584

Xianrui Wang, J. Benesty, Gongping Huang, Jingdong Chen

引用次数: 0

Algorithmic Advances for the Adjacency Spectral Embedding 邻接谱嵌入算法研究进展

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909610

Marcelo Fiori, Bernardo Marenco, Federico Larroca, P. Bermolen, G. Mateos

{"title":"Algorithmic Advances for the Adjacency Spectral Embedding","authors":"Marcelo Fiori, Bernardo Marenco, Federico Larroca, P. Bermolen, G. Mateos","doi":"10.23919/eusipco55093.2022.9909610","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909610","url":null,"abstract":"The Random Dot Product Graph (RDPG) is a popular generative graph model for relational data. RDPGs postulate there exist latent positions for each node, and specifies the edge formation probabilities via the inner product of the corresponding latent vectors. The embedding task of estimating these latent positions from observed graphs is usually posed as a non-convex matrix factorization problem. The workhorse Adjacency Spectral Embedding offers an approximate solution obtained via the eigendecomposition of the adjacency matrix, which enjoys solid statistical guarantees but can be computationally intensive and is formally solving a surrogate problem. In this paper, we bring to bear recent non-convex optimization advances and demonstrate their impact to RDPG inference. We develop first-order gradient descent methods to better solve the original optimization problem, and to accommodate broader network embedding applications in an organic way. The effectiveness of the resulting graph representation learning framework is demonstrated on both synthetic and real data. We show the algorithms are scalable, robust to missing network data, and can track the latent positions over time when the graphs are acquired in a streaming fashion.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127981128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Automatic Detection of the Retina in Optical Coherence Tomography using Deep Q Learning 基于深度Q学习的光学相干断层扫描视网膜自动检测

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909830

Alex Cazañas-Gordón, Luís A. da Silva Cruz

引用次数: 0

Design of Single Unimodular Waveform With Good Correlation Level Via Phase Optimizations 通过相位优化设计具有良好相关电平的单模波形

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909969

Xiaohan Zhao, Yongzhe Li, R. Tao

{"title":"Design of Single Unimodular Waveform With Good Correlation Level Via Phase Optimizations","authors":"Xiaohan Zhao, Yongzhe Li, R. Tao","doi":"10.23919/eusipco55093.2022.9909969","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909969","url":null,"abstract":"In this paper, we focus on the unimodular waveform design with good correlation property, i.e., with low integrated sidelobe level (ISL). In contrast to existing approaches that commonly involve constraints on the moduli of waveform elements, we come up with the idea of designing the waveform via directly optimizing its phase values. Using this idea, the standard ISL-minimization based waveform design is converted as an unconstrained optimization problem with respect to the phase values of waveform elements, which avoids the repetitive procedure of projecting non-unimodular complex values into the best approximations of constant magnitudes. To this end, we first reformulate the ISL metric into a function of the phase values to be obtained for the waveform, and then solve the new unconstrained ISL-minimization-based waveform design using majorization-minimization techniques. The first-order gradient of the reformulated objective function is derived, by which the majorant of the objective is elaborated. Based on this, we finally tackle the design via iterations, at each of which we obtain a closed-form solution with fast implementations. An algorithm is proposed, with whose simpleness and effectiveness are verified by simulations.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conditional Variational Graph Autoencoder for Air Quality Forecasting 空气质量预报的条件变分图自编码器

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909725

Esther Rodrigo Bonet, T. Do, Xuening Qin, J. Hofman, V. Manna, Wilfried Philips, Nikos Deligiannis

引用次数: 1

Group equivariant networks for leakage detection in vacuum bagging 真空装袋泄漏检测的群等变网络

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909715

Christoph Brauer, D. Lorenz, Lionel Tondji

{"title":"Group equivariant networks for leakage detection in vacuum bagging","authors":"Christoph Brauer, D. Lorenz, Lionel Tondji","doi":"10.23919/eusipco55093.2022.9909715","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909715","url":null,"abstract":"The incorporation of prior knowledge into the ma-chine learning pipeline is subject of informed machine learning. Spatial invariances constitute a class of prior knowledge that can be taken into account especially in the design of model architectures or through virtual training examples. In this contribution, we investigate fully connected neural network architectures that are equivariant with respect to the dihedral group of order eight. This is practically motivated by the application of leakage detection in vacuum bagging which plays an important role in the manufacturing of fiber composite components. Our approach for the derivation of an equivariant architecture is constructive and transferable to other symmetry groups. It starts from a standard network architecture and results in a specific kind of weight sharing in each layer. In numerical experiments, we compare equivariant and standard networks on a novel leakage detection dataset. Our results indicate that group equivariant networks can capture the application specific prior knowledge much better than standard networks, even if the latter are trained on augmented data.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Tensor Tracking With Missing Data Under Tensor-Train Format 缺失数据下的鲁棒张量跟踪

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909702

Thanh Trung LE, K. Abed-Meraim, N. Trung, A. Hafiane

引用次数: 3

Real-time Vehicle Localization and Pose Tracking in High-Resolution 3D Maps 高分辨率3D地图中的实时车辆定位和姿态跟踪

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909654

Orkény Zováthi, Balázs Pálffy, C. Benedek

{"title":"Real-time Vehicle Localization and Pose Tracking in High-Resolution 3D Maps","authors":"Orkény Zováthi, Balázs Pálffy, C. Benedek","doi":"10.23919/eusipco55093.2022.9909654","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909654","url":null,"abstract":"In this paper we introduce a novel approach for accurate self-localization and pose tracking for Lidar and GPS-equipped autonomous vehicles (AVs) in high-density (more than 5000 points/m2) 3D localization maps obtained through Mobile Laser Scanning (MLS). Our solution consist of two main steps: First, starting from a poor GPS-based initial position, we estimate the 3DoF pose (planar position and yaw orientation) of the ego vehicle by aligning its sparse (50-500 points/m2) Lidar point cloud measurements to the MLS prior map, using a novel approach of matching static landmark objects of the scene. Second, to effectively deal with the lack of pairable objects in certain time frames (e.g. due to scene segments occluded by a large moving tram), we track the estimated 3DoF pose of the AVs by a Kalman filter. Comperative test are provided on roads with heavy traffic in downtown city areas with large (5-10 meters) GPS positioning errors. The proposed approach is able to reduce the location error of the vehicle by one order of magnitude and keep the yaw angle error around 1° during its whole trajectory without considerable drift, while running in real-time (20-25 Hz).","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131089240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

2022 30th European Signal Processing Conference (EUSIPCO) Pub Date : 2022-08-29 DOI: 10.23919/eusipco55093.2022.9909566

Yang Li, G. Mateos

{"title":"Learning Similarity-Preserving Representations of Brain Structure-Function Coupling","authors":"Yang Li, G. Mateos","doi":"10.23919/eusipco55093.2022.9909566","DOIUrl":"https://doi.org/10.23919/eusipco55093.2022.9909566","url":null,"abstract":"Advances in graph signal processing for network neuroscience offer a unique pathway to integrate brain structure and function, with the goal of revealing some of the brain's organizing principles at the system level. In this direction, we develop a supervised graph representation learning framework to model the relationship between brain structural connectivity (SC) and functional connectivity (FC) via a graph encoder-decoder system. Specifically, we propose a Siamese network architecture equipped with graph convolutional encoders to learn graph (i.e., subject)-level embeddings that preserve application-dependent similarity measures between brain networks. This way, we effectively increase the number of training samples and bring in the flexibility to incorporate additional prior information via the prescribed target graph-level distance. While information on the brain structure-function coupling is implicitly distilled via reconstruction of brain FC from SC, our model also manages to learn representations that preserve the similarity between input graphs. The superior discriminative power of the learnt representations is demonstrated in downstream tasks including subject classification and visualization. All in all, this work advocates the prospect of leveraging learnt graph-level, similarity-preserving embeddings for brain network analysis, by bringing to bear standard tools of metric data analysis.","PeriodicalId":231263,"journal":{"name":"2022 30th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1