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2019 27th European Signal Processing Conference (EUSIPCO)最新文献

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Kernel Based Online Change Point Detection 基于内核的在线变更点检测
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902582
Ikram Bouchikhi, A. Ferrari, C. Richard, Anthony Bourrier, M. Bernot
{"title":"Kernel Based Online Change Point Detection","authors":"Ikram Bouchikhi, A. Ferrari, C. Richard, Anthony Bourrier, M. Bernot","doi":"10.23919/EUSIPCO.2019.8902582","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902582","url":null,"abstract":"Detecting change points in time series data is a challenging problem, in particular when no prior information on the data distribution and the nature of the change is available. In a former work, we introduced an online non-parametric change-point detection framework built upon direct density ratio estimation over two consecutive time segments, rather than modeling densities separately. This algorithm based on the theory of reproducing kernels showed positive and reliable detection results for a variety of problems. To further improve the detection performance of this approach, we propose in this paper to modify the original cost function in order to achieve unbiasedness of the density ratio estimation under the null hypothesis. Theoretical analysis and numerical simulations confirm the improved behavior of this method, as well as its efficiency compared to a state of the art one. Application to sentiment change detection in Twitter data streams is also presented.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115047327","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: 8
Reducing the Bias in DRSS-Based Localization: An Instrumental Variable Approach 减少基于drs的定位偏差:一种工具变量方法
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902818
Jun Li, K. Doğançay, Ngoc Hung Nguyen, Yee Wei Law
{"title":"Reducing the Bias in DRSS-Based Localization: An Instrumental Variable Approach","authors":"Jun Li, K. Doğançay, Ngoc Hung Nguyen, Yee Wei Law","doi":"10.23919/EUSIPCO.2019.8902818","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902818","url":null,"abstract":"This paper proposes a closed-form solution with reduced bias for differential received signal strength (DRSS) localization. During the linearization of DRSS measurement equations, the measurement noise is injected into the measurement data matrix, resulting in a correlation between the measurement noise and measurement data matrix. Existing closed-form solutions do not consider this correlation, which causes biased estimation results. The solution proposed here aims to eliminate the bias by introducing instrument variables (IV), whose role is to mitigate the correlation arising from linearization. Simulation results demonstrate the improved performance of the IV-based estimator over some existing closed-form solutions, in the form of root-mean-squared errors that are close to the Cramér-Rao lower bound, and significantly reduced bias, over a wide range of noise levels.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115178071","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
Scheduling Moldable Parallel Streaming Tasks on Heterogeneous Platforms with Frequency Scaling 基于频率缩放的异构平台可塑并行流任务调度
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8903180
Sebastian Litzinger, J. Keller, C. Kessler
{"title":"Scheduling Moldable Parallel Streaming Tasks on Heterogeneous Platforms with Frequency Scaling","authors":"Sebastian Litzinger, J. Keller, C. Kessler","doi":"10.23919/EUSIPCO.2019.8903180","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903180","url":null,"abstract":"We extend static scheduling of parallelizable tasks to machines with multiple core types, taking differences in performance and power consumption due to task type into account. Next to energy minimization for given deadline, i.e. for given throughput requirement, we consider makespan minimization for given energy or average power budgets. We evaluate our approach by comparing schedules of synthetic task sets for big.LITTLE with other schedulers from literature. We achieve an improvement of up to 33%.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"177 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122437388","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: 10
FDA-MIMO Signal Processing for Mainlobe Jammer Suppression 抑制主瓣干扰的FDA-MIMO信号处理
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902536
Wen-qin Wang, H. So, A. Farina
{"title":"FDA-MIMO Signal Processing for Mainlobe Jammer Suppression","authors":"Wen-qin Wang, H. So, A. Farina","doi":"10.23919/EUSIPCO.2019.8902536","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902536","url":null,"abstract":"Inspired by the fact that frequency diverse array (FDA) generates a time-variant transmit beampattern depending on both range and angle parameters, we propose joint utilization of the FDA and multiple-input multiple-output (MIMO) radar for counteracting mainlobe jamming signals. In doing so, the advantages of FDA and MIMO in range-time-dependent beampattern and increased degrees-of-freedom, respectively, are combined. The proposed algorithm development is based on eigenvalue projection and blocking matrix processing. Theoretical analysis and simulation results for evaluation of the FDA-MIMO framework are provided.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122567487","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: 12
Compression of High-Dimensional Multispectral Image Time Series Using Tensor Decomposition Learning 基于张量分解学习的高维多光谱图像时间序列压缩
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902838
A. Aidini, Grigorios Tsagkatakis, P. Tsakalides
{"title":"Compression of High-Dimensional Multispectral Image Time Series Using Tensor Decomposition Learning","authors":"A. Aidini, Grigorios Tsagkatakis, P. Tsakalides","doi":"10.23919/EUSIPCO.2019.8902838","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902838","url":null,"abstract":"Multispectral imaging is widely used in many fields, such as in medicine and earth observation, as it provides valuable spatial, spectral and temporal information about the scene. It is of paramount importance that the large amount of images collected over time, and organized in multidimensional arrays known as tensors, be efficiently compressed in order to be stored or transmitted. In this paper, we present a compression algorithm which involves a training process and employs a symbol encoding dictionary. During training, we derive specially structured tensors from a given image time sequence using the CANDECOMP/PARAFAC (CP) decomposition. During runtime, every new image time sequence is quantized and encoded into a vector of coefficients corresponding to the learned CP decomposition. Experimental results on sequences of real satellite images demonstrate that we can efficiently handle higher-order tensors and obtain the decompressed data by composing the learned tensors by means of the received vector of coefficients, thus achieving a high compression ratio.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114505437","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: 4
Maximum-likelihood DOA estimation at low SNR in Laplace-like noise 类拉普拉斯噪声下低信噪比下的最大似然DOA估计
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902711
C. Mecklenbräuker, P. Gerstoft
{"title":"Maximum-likelihood DOA estimation at low SNR in Laplace-like noise","authors":"C. Mecklenbräuker, P. Gerstoft","doi":"10.23919/EUSIPCO.2019.8902711","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902711","url":null,"abstract":"We consider the estimation of the direction of arrivals (DOAs) of plane waves hidden in additive, mutually independent, complex circularly symmetric noise at very low signal to noise ratio (SNR). The maximum-likelihood estimator (ML) for the DOAs of deterministic signals carried by plane waves hidden in noise with a Laplace-like distribution is derived. This leads to a DOA estimator based on the Least Absolute Deviation (LAD) criterion. We prove analytically that a weighted phase-only beamformer (which evaluates the scalar product between the steering vector and the complex signum function of the observed array data) is an approximation to a beamformer based on the Least Absolute Deviation (LAD) criterion. The root mean squared error of DOA estimators versus SNR is compared in a simulation study: the conventional beamformer (CBF), the weighted phase-only beamformer, and sparse Bayesian learning (SBL3). This shows show that the ML estimator and weighted phase-only beamformer are well performing DOA estimators at low SNR for additive homoscedastic and heteroscedastic Gaussian noise, as well as Laplace-like noise.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122102733","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
Tensor Network Kalman Filter for LTI Systems LTI系统的张量网络卡尔曼滤波
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902976
Daniel Gedon, P. Piscaer, K. Batselier, Carlas S. Smith, M. Verhaegen
{"title":"Tensor Network Kalman Filter for LTI Systems","authors":"Daniel Gedon, P. Piscaer, K. Batselier, Carlas S. Smith, M. Verhaegen","doi":"10.23919/EUSIPCO.2019.8902976","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902976","url":null,"abstract":"An extension of the Tensor Network (TN) Kalman filter [2], [3] for large scale LTI systems is presented in this paper. The TN Kalman filter can handle exponentially large state vectors without constructing them explicitly. In order to have efficient algebraic operations, a low TN rank is required. We exploit the possibility to approximate the covariance matrix as a TN with a low TN rank. This reduces the computational complexity for general SISO and MIMO LTI systems with TN rank greater than one significantly while obtaining an accurate estimation. Improvements of this method in terms of computational complexity compared to the conventional Kalman filter are demonstrated in numerical simulations for large scale systems.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117074662","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
Bayesian Model Selection for Nonlinear Acoustic Echo Cancellation 非线性声回波抵消的贝叶斯模型选择
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902673
Mhd Modar Halimeh, Andreas Brendel, Walter Kellermann
{"title":"Bayesian Model Selection for Nonlinear Acoustic Echo Cancellation","authors":"Mhd Modar Halimeh, Andreas Brendel, Walter Kellermann","doi":"10.23919/EUSIPCO.2019.8902673","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902673","url":null,"abstract":"In this paper, we introduce a Bayesian framework to perform model selection for nonlinear acoustic echo cancellation. This is especially important for scenarios where the functional form of the underlying nonlinear distortion is time-varying and/or is unknown, e.g., nonlinear distortions that vary with the volume level of the loudspeakers. To this end, the proposed method evaluates the model probabilities, or what is known as the evidence density, in a Bayesian manner. Thus, unlike convex and affine combination schemes of adaptive filters, the proposed method optimizes both the model complexity as well as the model performance by a single criterion. Moreover, by using the significance-aware principle, the proposed framework is realized in a computationally efficient way. The method is validated by three experiments using synthesized time-invariant nonlinearities, synthesized time-varying nonlinearities, and using real recorded nonlinearities.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128519568","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
Reparameterization Gradient Message Passing 重新参数化梯度消息传递
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902930
Semih Akbayrak, B. Vries
{"title":"Reparameterization Gradient Message Passing","authors":"Semih Akbayrak, B. Vries","doi":"10.23919/EUSIPCO.2019.8902930","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902930","url":null,"abstract":"In this paper we consider efficient message passing based inference in a factor graph representation of a probabilistic model. Current message passing methods, such as belief propagation, variational message passing or expectation propagation, rely on analytically pre-computed message update rules. In practical models, it is often not feasible to analytically derive all update rules for all factors in the graph and as a result, efficient message passing-based inference cannot proceed. In related research on (non-message passing-based) inference, a “reparameterization trick” has lead to a considerable extension of the class of models for which automated inference is possible. In this paper, we introduce Reparameterization Gradient Message Passing (RGMP), which is a new message passing method based on the reparameterization gradient. In most models, the large majority of messages can be analytically derived and we resort to RGMP only when necessary. We will argue that this kind of hybrid message passing leads naturally to low-variance gradients.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129083257","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
Recursive LCMVEs with Non-Stationary Constraints and Partially Coherent Signal Sources 具有非平稳约束和部分相干信号源的递推lcmve
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902676
É. Chaumette, J. Vilà‐Valls, F. Vincent, P. Closas
{"title":"Recursive LCMVEs with Non-Stationary Constraints and Partially Coherent Signal Sources","authors":"É. Chaumette, J. Vilà‐Valls, F. Vincent, P. Closas","doi":"10.23919/EUSIPCO.2019.8902676","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902676","url":null,"abstract":"In estimating an unknown parameter vector in a linear regression model, it is common to use linearly constrained minimum variance estimators (LCMVEs). For a long time, LCMVEs were studied in the context of stationary constraints under both stationary or non-stationary environments. Recently, a new family of non-stationary constraints leading to Kalman-like recursive LCMVEs has been introduced for fully coherent signal (FCS) sources. A noteworthy feature of this family is to allow the possibility of, at each new observation, incorporating new constraints. This article extends these results to the case of partially coherent signal (PCS) sources. Indeed, without ad hoc modifications of the Kalman-like recursion, estimation of the amplitudes of PCS sources exhibit a performance breakdown even for a slight loss of coherence. Last but not least, it is shown that PCS sources introduce a lower limit in the achievable performance in the large sample regime.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124600287","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
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