2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)最新文献_第4页

Recurrent generative adversarial neural networks for compressive imaging 用于压缩成像的循环生成对抗神经网络

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313209

M. Mardani, E. Gong, Joseph Y. Cheng, J. Pauly, L. Xing

{"title":"Recurrent generative adversarial neural networks for compressive imaging","authors":"M. Mardani, E. Gong, Joseph Y. Cheng, J. Pauly, L. Xing","doi":"10.1109/CAMSAP.2017.8313209","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313209","url":null,"abstract":"Recovering images from highly undersampled measurements has a wide range of applications across imaging sciences. State-of-the-art analytics however are not aware of the image perceptual quality, and demand iterative algorithms that incur significant computational overhead. To sidestep these hurdles, this paper brings forth a novel compressive imaging framework using deep neural networks that approximates a low-dimensional manifold of images using generative adversarial networks. To ensure the images are consistent with the measurements a recurrent GAN (RGAN) architecture is deployed that consists of multiple alternative blocks of generator networks and affine projection, which is then followed by a discriminator network to score the perceptual quality of the generated images. A deep residual network with skip connections is used for the generator, while the discriminator is a multilayer perceptron. Experiments performed with real-world contrast enhanced MRI data corroborate the diagnostic quality of the retrieved images relative to state-of-the-art CS schemes. In addition, it achieves about two-orders of magnitude faster reconstruction.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124939960","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: 11

A constrained formulation for compressive spectral image reconstruction using linear mixture models 基于线性混合模型的压缩光谱图像重构约束公式

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313122

Jorge Bacca, Héctor Vargas, H. Arguello

{"title":"A constrained formulation for compressive spectral image reconstruction using linear mixture models","authors":"Jorge Bacca, Héctor Vargas, H. Arguello","doi":"10.1109/CAMSAP.2017.8313122","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313122","url":null,"abstract":"Recent hyperspectral imaging systems are constructed on the idea of compressive sensing for efficient acquisition. However, the traditional reconstruction model in compressive hyperspectral imaging has a high computational complexity. In this work, compressive hyperspectral imaging and unmixing are combined for hyperspectral reconstruction in a low-complexity scheme. The compressed hyperspectral measurements are acquired with a single pixel spectrometer. The reconstruction model is represented in a space of lower dimension named linear mixture model. Hyperspectral reconstruction is then formulated as a nonnegative matrix factorization problem with respect to the endmembers and abundances, bypassing high-complexity tasks involving the hyperspectral data cube itself. The nonnegative matrix factorization problem is solved by combining an alternating least-squares based estimation strategy with the alternating direction method of multipliers. The estimated performance of the proposed scheme is illustrated in experiments conducted on a simulated acquisition in real data outperforming in 3dB the state-of-the-art reconstruction algorithms.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114283716","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: 6

On canonical polyadic decomposition of overcomplete tensors of arbitrary even order 任意偶阶过完备张量的正则多进分解

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313191

A. Koochakzadeh, P. Pal

{"title":"On canonical polyadic decomposition of overcomplete tensors of arbitrary even order","authors":"A. Koochakzadeh, P. Pal","doi":"10.1109/CAMSAP.2017.8313191","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313191","url":null,"abstract":"Decomposition of tensors into summation of rank one components, known as Canonical Polyadic (CP) decomposition, has long been studied in the literature. Although the CP-rank of tensors can far exceed their dimensions (in which case they are called overcomplete tensors), there are only a handful of algorithms which consider CP-decomposition of such overcomplete tensors, and most of the CP-decomposition algorithms proposed in literature deal with simpler cases where the rank is of the same order as the dimensions of the tensor. In this paper, we consider symmetric tensors of arbitrary even order whose eigenvalues are assumed to be positive. We show that for a 2dth order tensor with dimension N, under some mild conditions, the problem of CP-decomposition is equivalent to solving a system of quadratic equations, even when the rank is as large as O(Nd). We will develop two different algorithms (one convex, and one nonconvex) to solve this system of quadratic equations. Our simulations show that successful recovery of eigenvectors is possible even if the rank is much larger than the dimension of the tensor.1","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131496883","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: 5

Block term decomposition with rank estimation using group sparsity 利用群稀疏性进行秩估计的块项分解

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313206

Xu Han, L. Albera, A. Kachenoura, H. Shu, L. Senhadji

引用次数: 8

Simultaneous target state and sensor bias estimation: Is more better? 同时目标状态和传感器偏差估计:越多越好?

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313183

M. Kowalski, P. Willett

引用次数: 0

Wideband channel tracking for mmWave MIMO system with hybrid beamforming architecture: (Invited Paper) 基于混合波束形成结构的毫米波MIMO系统宽带信道跟踪研究(特邀论文)

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313185

Han Yan, S. Chaudhari, D. Cabric

{"title":"Wideband channel tracking for mmWave MIMO system with hybrid beamforming architecture: (Invited Paper)","authors":"Han Yan, S. Chaudhari, D. Cabric","doi":"10.1109/CAMSAP.2017.8313185","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313185","url":null,"abstract":"Millimeter-wave (mmWave) systems require a large number of antennas at both base station (BS) and user equipment (UE) for a desirable link budget. Due to time varying channel under UE mobility, up-to-date channel state information (CSI) is important to obtain the beamforming gain. The overhead cost of frequent channel estimation becomes a bottleneck to achieve high throughput. In this paper, we propose the first mmWave frequency selective channel tracking technique for hybrid analog and digital beamforming architecture. During tracking, this technique exploits mmWave channel sparsity and uses only one training symbol to update the CSI. Our simulation study utilizes a dynamic channel simulator that builds on top of recently proposed geometric stochastic approach from mmMAGIC project at 28 GHz. Assuming 10m/s moving speed and 200 deg/s rotation speed at UE, the proposed algorithm maintains the 80% of the spectral efficiency as compared to static environment over a time window of 100 ms. The proposed tracking algorithm reduces the overhead by 3 times as compared to existing channel estimation technique.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134630196","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: 11

A compressive sensing-maximum likelihood approach for off-grid wideband channel estimation at mmWave 毫米波离网宽带信道估计的压缩感知-最大似然方法

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313157

Javier Rodríguez-Fernández, N. G. Prelcic, R. Heath

引用次数: 15

Nonlinear system identification: Finding structure in nonlinear black-box models 非线性系统辨识:在非线性黑箱模型中寻找结构

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313214

P. Dreesen, K. Tiels, Mariya Ishteva, J. Schoukens

{"title":"Nonlinear system identification: Finding structure in nonlinear black-box models","authors":"P. Dreesen, K. Tiels, Mariya Ishteva, J. Schoukens","doi":"10.1109/CAMSAP.2017.8313214","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313214","url":null,"abstract":"The use of black-box models is wide-spread in signal processing and system identification applications. However, often such models possess a large number of parameters, and obfuscate their inner workings, as there are cross-connections between all inputs and all outputs (and possibly all internal states) of the model. Although black-box models have proven their success and wide applicability, there is a need to shed a light on what goes on inside the model. We have developed a tensor-based method that aims at pinpointing the nonlinearities of a given nonlinear model into a small number of univariate nonlinear mappings, with the advantageous side-effect of reducing the parametric complexity. In this contribution we will discuss how the method is conceived, and how it can be applied to the task of finding structure in blackbox models. We have found that the tensor-based decoupling method is able to reconstruct up to high accuracy a given blackbox nonlinear model, while reducing the parametric complexity and revealing some of the inner operation of the model. Due to their universal use, we will focus the presentation on the use of nonlinear state-space models, but the method is also suitable for other model structures. We validate the method on a case study in nonlinear system identification.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122602034","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

Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment 基于耦合SECSI框架的脑电-脑电信号联合分解:对照实验验证

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313193

Kristina Naskovska, S. Lau, Amr Aboughazala, M. Haardt, J. Haueisen

{"title":"Joint MEG-EEG signal decomposition using the coupled SECSI framework: Validation on a controlled experiment","authors":"Kristina Naskovska, S. Lau, Amr Aboughazala, M. Haardt, J. Haueisen","doi":"10.1109/CAMSAP.2017.8313193","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313193","url":null,"abstract":"Simultaneously recorded magnetoencephalography (MEG) and electroencephalography (EEG) signals can benefit from a joint analysis based on coupled Canonical Polyadic (CP) tensor decompositions. The coupled CP decomposition jointly decomposes tensors that have at least one factor matrix in common. The Coupled — Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization framework (C-SECSI) efficiently estimates the factor matrices with adjustable complexity-accuracy trade-offs. Our objective is to decompose simultaneously recorded MEG and EEG signals above intact skull and above two conducting skull defects using C-SECSI in order to determine how such a tissue anomaly of the head is reflected in the tensor rank. The source of the MEG and EEG signals is a miniaturized electric dipole that is implanted into a rabbit's brain. The dipole is shifted along a line under the skull defects, and measurements are taken at regularly spaced points. The coupled SECSI analysis is conducted for MEG and EEG measurement series and ranks 1–3. This coupled decomposition produces meaningful components representing the three characteristic signal topographies for source positions under defect 1 and the positions on either side of defect 1. The rank estimation with respect to the complexity-accuracy trade-off of rank 3 reflects the three characteristic cases well and matches the dimensions spanned by the data set. The intact skull MEG signals show a higher complexity (rank 3) than the corresponding EEG signals (rank 1). The C-SECSI framework is a very promising method for blind signal separation in multidimensional data with coupled modalities, such as simultaneous MEG-EEG.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123989074","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: 6

Under-Determined tensor diagonalization for decomposition of difficult tensors 难张量分解的欠定张量对角化

2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) Pub Date : 2017-12-01 DOI: 10.1109/CAMSAP.2017.8313082

P. Tichavský, A. Phan, A. Cichocki

{"title":"Under-Determined tensor diagonalization for decomposition of difficult tensors","authors":"P. Tichavský, A. Phan, A. Cichocki","doi":"10.1109/CAMSAP.2017.8313082","DOIUrl":"https://doi.org/10.1109/CAMSAP.2017.8313082","url":null,"abstract":"Analysis of multidimensional arrays, usually called tensors, often becomes difficult in cases when the tensor rank (a minimum number of rank-one components) exceeds all the tensor dimensions. Traditional methods of canonical polyadic decomposition of such tensors, namely the alternating least squares, can be used, but a presence of a large number of false local minima can make the problem hard. Usually, multiple random initializations are advised in such cases, but the question is how many such random initializations are sufficient to get a good chance of finding the right solution. It appears that the number of the initializations can be very large. We propose a novel approach to the problem. The given tensor is augmented by some unknown parameters to the shape that admits ordinary tensor diagonalization, i.e., transforming the augmented tensor into an exact or nearly diagonal form through multiplying the tensor by non-orthogonal invertible matrices. Three possible constraints are proposed to make the optimization problem well defined. The method can be modified for an under-determined block-term decomposition.","PeriodicalId":315977,"journal":{"name":"2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126192850","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