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Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)最新文献

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Estimation of Consecutively Missed Samples in Fetal Heart Rate Recordings. 胎儿心率记录中连续遗漏样本的估计。
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2020-01-01 Epub Date: 2020-12-18 DOI: 10.23919/eusipco47968.2020.9287490
Guanchao Feng, J Gerald Quirk, Cassandra Heiselman, Petar M Djurić
{"title":"Estimation of Consecutively Missed Samples in Fetal Heart Rate Recordings.","authors":"Guanchao Feng, J Gerald Quirk, Cassandra Heiselman, Petar M Djurić","doi":"10.23919/eusipco47968.2020.9287490","DOIUrl":"10.23919/eusipco47968.2020.9287490","url":null,"abstract":"<p><p>During labor, fetal heart rate (FHR) is monitored externally using Doppler ultrasound. This is done continuously, but for various reasons (e.g., fetal or maternal movements) the system does not record any samples for varying periods of time. In many settings, it would be quite beneficial to estimate the missing samples. In this paper, we propose a (deep) Gaussian process-based approach for estimation of consecutively missing samples in FHR recordings. The method relies on similarities in the state space and on exploiting the concept of attractor manifolds. The proposed approach was tested on a short segment of real FHR recordings. The experimental results indicate that the proposed approach is able to provide more reliable results in comparison to several interpolation methods that are commonly applied for processing of FHR signals.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"2020 ","pages":"1080-1084"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887835/pdf/nihms-1670258.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25387690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points. 具有时变参数和诱导点的高斯过程状态空间模型
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2020-01-01 DOI: 10.23919/Eusipco47968.2020.9287481
Yuhao Liu, Petar M Djurić
{"title":"Gaussian Process State-Space Models with Time-Varying Parameters and Inducing Points.","authors":"Yuhao Liu, Petar M Djurić","doi":"10.23919/Eusipco47968.2020.9287481","DOIUrl":"10.23919/Eusipco47968.2020.9287481","url":null,"abstract":"<p><p>We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time. The models have the ability to estimate time-varying functions and thereby increase flexibility to extract information from observed data. The proposed inference approach makes use of time-varying inducing points to adapt to changes of the function, and it exploits hierarchical importance sampling. The experimental results show that the approach has better performance than that of the standard Gaussian process.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"2020 ","pages":"1462-1466"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890411/pdf/nihms-1670261.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"25391248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T 1 Mapping. 改进的正则化重建多层心脏MRI t1同步定位。
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2019-09-01 Epub Date: 2019-11-18 DOI: 10.23919/EUSIPCO.2019.8903058
Ömer Burak Demirel, Sebastian Weingärtner, Steen Moeller, Mehmet Akçakaya
{"title":"Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI <i>T</i> <sub>1</sub> Mapping.","authors":"Ömer Burak Demirel,&nbsp;Sebastian Weingärtner,&nbsp;Steen Moeller,&nbsp;Mehmet Akçakaya","doi":"10.23919/EUSIPCO.2019.8903058","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903058","url":null,"abstract":"<p><p>Myocardial <i>T</i> <sub>1</sub> mapping is a quantitative MRI technique that has found great clinical utility in the detection of various heart disease. These acquisitions typically require three breath-holds, leading to long scan durations and patient discomfort. Simultaneous multi-slice (SMS) imaging has been shown to reduce the scan time of myocardial <i>T</i> <sub>1</sub> mapping to a single breath-hold without sacrificing coverage, albeit at reduced precision. In this work, we propose a new reconstruction strategy for SMS imaging that combines the advantages of two different k-space interpolation strategies, while allowing for regularization, in order to improve the precision of accelerated mycordial <i>T</i> <sub>1</sub> mapping.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"2019 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/EUSIPCO.2019.8903058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37505024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Numerical stability of spline-based Gabor-like systems. 基于样条的类gabor系统的数值稳定性。
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2018-12-03 DOI: 10.23919/EUSIPCO.2018.8552927
Darian M Onchis, Simone Zappalà, Pedro Real, Codruta Istin
{"title":"Numerical stability of spline-based Gabor-like systems.","authors":"Darian M Onchis, Simone Zappalà, Pedro Real, Codruta Istin","doi":"10.23919/EUSIPCO.2018.8552927","DOIUrl":"10.23919/EUSIPCO.2018.8552927","url":null,"abstract":"<p><p>The paper provides a theorem for the characterization of numerical stability of spline-type systems. These systems are generated through shifted copies of a given atom over a time lattice. Also, we reformulate the well known Gabor systems via modulated spline-type systems and we apply the corresponding numerical stability to these systems. The numerical stability is tested for consistency against deformations.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"26 ","pages":"1337-1341"},"PeriodicalIF":0.0,"publicationDate":"2018-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6292493/pdf/emss-80803.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36787383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dictionary Learning for Spontaneous Neural Activity Modeling. 自发神经活动建模的词典学习
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2017-08-01 Epub Date: 2017-10-26 DOI: 10.23919/EUSIPCO.2017.8081475
Eirini Troullinou, Grigorios Tsagkatakis, Ganna Palagina, Maria Papadopouli, Stelios Manolis Smirnakis, Panagiotis Tsakalides
{"title":"Dictionary Learning for Spontaneous Neural Activity Modeling.","authors":"Eirini Troullinou, Grigorios Tsagkatakis, Ganna Palagina, Maria Papadopouli, Stelios Manolis Smirnakis, Panagiotis Tsakalides","doi":"10.23919/EUSIPCO.2017.8081475","DOIUrl":"10.23919/EUSIPCO.2017.8081475","url":null,"abstract":"<p><p>Modeling the activity of an ensemble of neurons can provide critical insights into the workings of the brain. In this work we examine if learning based signal modeling can contribute to a high quality modeling of neuronal signal data. To that end, we employ the sparse coding and dictionary learning schemes for capturing the behavior of neuronal responses into a small number of representative prototypical signals. Performance is measured by the reconstruction quality of clean and noisy test signals, which serves as an indicator of the generalization and discrimination capabilities of the learned dictionaries. To validate the merits of the proposed approach, a novel dataset of the actual recordings from 183 neurons from the primary visual cortex of a mouse in early postnatal development was developed and investigated. The results demonstrate that high quality modeling of testing data can be achieved from a small number of training examples and that the learned dictionaries exhibit significant specificity when introducing noise.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"2017 ","pages":"1579-1583"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7485749/pdf/nihms-1625649.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38472121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
FOOD TEXTURE DESCRIPTORS BASED ON FRACTAL AND LOCAL GRADIENT INFORMATION. 基于分形和局部梯度信息的食物纹理描述符。
Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) Pub Date : 2011-08-01 Epub Date: 2015-04-02
Marc Bosch, Fengqing Zhu, Nitin Khanna, Carol J Boushey, Edward J Delp
{"title":"FOOD TEXTURE DESCRIPTORS BASED ON FRACTAL AND LOCAL GRADIENT INFORMATION.","authors":"Marc Bosch,&nbsp;Fengqing Zhu,&nbsp;Nitin Khanna,&nbsp;Carol J Boushey,&nbsp;Edward J Delp","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>This work is motivated by the desire to use image analysis methods to identify and characterize images of food items to aid in dietary assessment. This paper introduces three texture descriptors for texture classification that can be used to classify images of food. Two are based on the multifractal analysis, namely, entropy-based categorization and fractal dimension estimation (EFD), and a Gabor-based image decomposition and fractal dimension estimation (GFD). Our third texture descriptor is based on the spatial relationship of gradient orientations (GOSDM), by obtaining the occurrence rate of pairs of gradient orientations at different neighborhood scales. The proposed methods are evaluated in texture classification and food categorization tasks using the entire Brodatz database and a customized food dataset with a wide variety of textures. Results show that for food categorization our methods consistently outperform several widely used techniques for both texture and object categorization.</p>","PeriodicalId":87340,"journal":{"name":"Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)","volume":"2011 ","pages":"764-768"},"PeriodicalIF":0.0,"publicationDate":"2011-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5448986/pdf/nihms824777.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35052360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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