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Novel Sensing Mechanism for Full-Duplex Secondary Users in Cognitive Radio 认知无线电全双工辅助用户的新型感知机制
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902697
M. Mortada, A. Nasser, A. Mansour, K. Yao
{"title":"Novel Sensing Mechanism for Full-Duplex Secondary Users in Cognitive Radio","authors":"M. Mortada, A. Nasser, A. Mansour, K. Yao","doi":"10.23919/EUSIPCO.2019.8902697","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902697","url":null,"abstract":"In this paper, we present a new Transmitting-Receiving-Sensing (TRS) mechanism for full-duplex cognitive radio. Our proposed mechanism permits Secondary Users (SUs) to establish a bidirectional communication over the same frequency band while keeping aware of the Primary User (PU) activity status. The activity period of SU in our proposed mechanism is composed of two stages: In the first stage SU communicates in bidirectional way with his peer SU. At the second stage, one of the SUs becomes silent in order to do not disturb his peer, which performs a spectrum sensing and remains active at the same time using self-interference cancellation technique. The probability of collision related to our mechanism is derived as well as the probability of waste and the average throughput. Our simulation results show that the proposed mechanism can significantly decrease the probability of collision at low SNRp (Signal to Noise Ratio of PU at SU).","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"159 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":"115263860","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
Rank-one Tensor Approximation with Beta-divergence Cost Functions 散度代价函数的秩一张量近似
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902937
Michiel Vandecappelle, Nico Vervliet, L. D. Lathauwer
{"title":"Rank-one Tensor Approximation with Beta-divergence Cost Functions","authors":"Michiel Vandecappelle, Nico Vervliet, L. D. Lathauwer","doi":"10.23919/EUSIPCO.2019.8902937","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902937","url":null,"abstract":"$beta $-divergence cost functions generalize three popular cost functions for low-rank tensor approximation by interpolating between them: the least-squares (LS) distance, the Kullback-Leibler (KL) divergence and the Itakura-Saito (IS) divergence. For certain types of data and specific noise distributions, beta-divergence cost functions can lead to more meaningful low-rank approximations than those obtained with the LS cost function. Unfortunately, much of the low-rank structure that is heavily exploited in existing second-order LS methods, is no longer exploitable when moving to general $beta $-divergences. In this paper, we show that, unlike in the general rank-$R$ case, rank-1 structure can still be exploited. We therefore propose an efficient method that uses second-order information to compute nonnegative rank-l approximations of tensors for general $beta -$ divergence cost functions.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"46 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":"115760467","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
A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW 认知电子战雷达脉冲探测与调制分类的完整框架
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8903045
E. Yar, M. B. Kocamis, A. Orduyilmaz, Mahmut Serin, M. Efe
{"title":"A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW","authors":"E. Yar, M. B. Kocamis, A. Orduyilmaz, Mahmut Serin, M. Efe","doi":"10.23919/EUSIPCO.2019.8903045","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903045","url":null,"abstract":"In this paper, we consider automatic radar pulse detection and intra-pulse modulation classification for cognitive electronic warfare applications. In this manner, we introduce an end-to-end framework for detection and classification of radar pulses. Our approach is complete, i.e., we provide raw radar signal at the input side and produce categorical output at the output. We use short time Fourier transform to obtain time-frequency image of the signal. Hough transform is used to detect pulses in time-frequency images and pulses are represented with a single line. Then, convolutional neural networks are used for pulse classification. In experiments, we provide classification results at different SNR levels.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"14 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":"125237946","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
Realtime 2-D DOA Estimation using Phase-Difference Projection (PDP) 基于相位差投影的实时二维DOA估计
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902804
Hui Chen, Tarig Ballal, Xing Liu, T. Al-Naffouri
{"title":"Realtime 2-D DOA Estimation using Phase-Difference Projection (PDP)","authors":"Hui Chen, Tarig Ballal, Xing Liu, T. Al-Naffouri","doi":"10.23919/EUSIPCO.2019.8902804","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902804","url":null,"abstract":"Estimating the direction of arrival (DOA) information of a signal is important for communications, localization and navigation systems. Time-delay based methods are popular DOA algorithms that can estimate DOA with a minimal number of receivers. Time delay can be measured with subsample accuracy using phase-difference based methods. Phase-wrapping represents a major challenge for time delay estimation that occurs when inter-sensor spacing is large. Several methods exist for phase-unwrapping; the most successful methods are those search methods, which are time-consuming and do not lend themselves to theoretical analysis. In this paper, we present a phase-difference projection (PDP) method for DOA estimation which is capable of delivering more accurate results with reduced computational complexity. The proposed method has been tested and compared with several benchmark algorithms in both simulations and experiments. The results show that, at a signal-to-noise ratio (SNR) of -18 dB, using the proposed PDP algorithm, the percentage of the DOA estimates with errors smaller than <5° is 54%, and it reaches 100% at SNR =-7dB. This performance is not matched by the benchmark methods. For the utility test, we implemented this algorithm to realize an ultrasound-based air-mouse and it achieves satisfactory user experiences when using Google Maps, or playing some interactive games.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"7 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":"121089249","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
A Composite Discriminator for Generative Adversarial Network based Video Super-Resolution 基于生成对抗网络的视频超分辨率综合判别器
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8903072
Xijun Wang, Alice Lucas, S. Tapia, Xinyi Wu, R. Molina, A. Katsaggelos
{"title":"A Composite Discriminator for Generative Adversarial Network based Video Super-Resolution","authors":"Xijun Wang, Alice Lucas, S. Tapia, Xinyi Wu, R. Molina, A. Katsaggelos","doi":"10.23919/EUSIPCO.2019.8903072","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8903072","url":null,"abstract":"Generative Adversarial Networks (GANs) have been used for solving the video super-resolution problem. So far, video super-resolution GAN-based methods use the traditional GAN framework which consists of a single generator and a single discriminator that are trained against each other. In this work we propose a new framework which incorporates two collaborative discriminators whose aim is to jointly improve the quality of the reconstructed video sequence. While one discriminator concentrates on general properties of the images, the second one specializes on obtaining realistically reconstructed features, such as, edges. Experiments results demonstrate that the learned model outperforms current state of the art models and obtains super-resolved frames, with fine details, sharp edges, and fewer artifacts.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"14 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":"127178359","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
fMRI BOLD signal decomposition using a multivariate low-rank model 基于多元低秩模型的fMRI BOLD信号分解
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902660
Hamza Cherkaoui, T. Moreau, Abderrahim Halimi, P. Ciuciu
{"title":"fMRI BOLD signal decomposition using a multivariate low-rank model","authors":"Hamza Cherkaoui, T. Moreau, Abderrahim Halimi, P. Ciuciu","doi":"10.23919/EUSIPCO.2019.8902660","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902660","url":null,"abstract":"Standard methodologies for functional Magnetic Resonance Imaging (fMRI) data analysis decompose the observed Blood Oxygenation Level Dependent (BOLD) signals using voxelwise linear model and perform maximum likelihood estimation to get the parameters associated with the regressors. In task fMRI, the latter are usually defined from the experimental paradigm and some confounds whereas in resting-state acquisitions, a seedvoxel time-course may be used as predictor. Nowadays, most fMRI datasets offer resting-state acquisitions, requiring multivariate approaches (e.g., PCA, ICA, etc) to extract meaningful information in a data-driven manner. Here, we propose a novel low-rank model of fMRI BOLD data but instead of considering a dimension reduction in space as in ICA, our model relies on convolutional sparse coding between the hemodynamic system and a few temporal atoms which code for the neural activity inducing signals. A rank -1 constraint is also associated with each temporal atom to spatially map its influence in the brain. Within a variational framework, the joint estimation of the neural signals and the associated spatial maps is formulated as a nonconvex optimization problem. A local minimizer is computed using an efficient alternate minimization algorithm. The proposed approach is first validated on simulations and then applied to task fMRI data for illustration purpose. Its comparison to a state-of-the-art approach suggests that our method is competitive regarding the uncovered neural fingerprints while offering a richer decomposition in time and space.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"12 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":"124929336","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
Efficient Distributed Processing for Large Scale MIMO Detection 大规模MIMO检测的高效分布式处理
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902787
M. Ahmed-Ouameur, D. Massicotte
{"title":"Efficient Distributed Processing for Large Scale MIMO Detection","authors":"M. Ahmed-Ouameur, D. Massicotte","doi":"10.23919/EUSIPCO.2019.8902787","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902787","url":null,"abstract":"In large scale multiple-input multiple-output (MIMO), high spectral and energy efficiencies comes at the expense of a high computational complexity baseband processing. Many contributions have been proposed to reduce such complexity using matrix inversion approximation techniques for instance. On the other hand, to reduce the constraint on the interconnects’ bandwidth, fewer decentralized processing techniques have emerged. Here, we propose a computationally efficient technique based on embedding one single Gauss-Seidel iteration within every ADMM based detection iteration. The simulations are performed using an LTE-like TDD-OFDM frame structure and waveform, under perfect and non-perfect channel state information (CSI). Early results reveal that the proposed ADMM-GS algorithm can outperform the centralised GS based technique processing in a high SNR region and high load regime. In addition ADMM-GS’ performance exhibits relatively less sensitivity to channel estimation error; a characteristic inherited from the centralised GS technique.","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":"123755160","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
Data Preprocessing for ANN-based Industrial Time-Series Forecasting with Imbalanced Data 非平衡数据下基于人工神经网络的工业时序预测的数据预处理
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902682
Ivan Pisa, I. Santín, J. Vicario, A. Morell, R. Vilanova
{"title":"Data Preprocessing for ANN-based Industrial Time-Series Forecasting with Imbalanced Data","authors":"Ivan Pisa, I. Santín, J. Vicario, A. Morell, R. Vilanova","doi":"10.23919/EUSIPCO.2019.8902682","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902682","url":null,"abstract":"The evolution of Industry towards the 4.0 paradigm has motivated the adoption of Artificial Neural Networks (ANNs) to deal with applications where predictive and maintenance tasks are performed. These tasks become difficult to carry out when rare events are present due to the imbalance of data. This is because training of ANN can be biased. Conventional techniques addressing this problem are mainly based on resampling-based approaches. However, these are not always feasible when dealing with time-series forecasting tasks in industrial scenarios. For that reason, this work proposes the application of data preprocessing techniques especially designed to face this scenario, a problem which has not been covered enough in the state-of-the-art. Considered techniques are applied over time-series data coming from Wastewater Treatment Plants (WWTPs). Our proposal significantly outperforms current strategies showing a 68% of improvement in terms of RMSE when rare events are addressed.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"11 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":"123806933","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
On the Relation Between DOA-Vector Eigenbeam ESPRIT and Subspace Pseudointensity-Vector doa向量特征束ESPRIT与子空间伪强度向量关系的研究
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902715
A. Herzog, Emanuël Habets
{"title":"On the Relation Between DOA-Vector Eigenbeam ESPRIT and Subspace Pseudointensity-Vector","authors":"A. Herzog, Emanuël Habets","doi":"10.23919/EUSIPCO.2019.8902715","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902715","url":null,"abstract":"Eigenbeam ESPRIT (EB-ESPRIT) is a subspace based method to estimate directions-of-arrival (DOAs) of sound sources from a spherical microphone array recording in the spherical harmonics domain (SHD). In recent works, nonsingular EB-ESPRIT methods have been proposed which can estimate the source DOA-vectors without ambiguities. In another recent publication, a subspace based pseudointensity-vector method (SS-PIV) has been proposed for DOA estimation. In this work, we derive the mathematical relation between the DOA-vector EB-ESPRIT and the SS-PIV method. We show that the SS-PIV can be seen as a special case of the DOA-vector EB-ESPRIT. Using this relation, we propose a novel DOA-estimator denoted as extended pseudointensity-vector (PIV). In the evaluation, we compare the DOA-vector EB-ESPRIT and the extended PIV with the SS-PIV and PIV under noisy and reverberant conditions.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"24 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":"125558729","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
Training Variational Autoencoders with Discrete Latent Variables Using Importance Sampling 用重要性抽样训练离散潜变量变分自编码器
2019 27th European Signal Processing Conference (EUSIPCO) Pub Date : 2019-09-01 DOI: 10.23919/EUSIPCO.2019.8902811
Alexander Bartler, Felix Wiewel, Lukas Mauch, Binh Yang
{"title":"Training Variational Autoencoders with Discrete Latent Variables Using Importance Sampling","authors":"Alexander Bartler, Felix Wiewel, Lukas Mauch, Binh Yang","doi":"10.23919/EUSIPCO.2019.8902811","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2019.8902811","url":null,"abstract":"The Variational Autoencoder (VAE) is a popular generative latent variable model that is often used for representation learning. Standard VAEs assume continuous-valued latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if we want to train VAEs with discrete-valued latent variables, since reparametrized sampling is not possible. In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and categorically distributed latent representations on two different benchmark datasets.","PeriodicalId":319233,"journal":{"name":"2019 27th European Signal Processing Conference (EUSIPCO)","volume":"42 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":"126727543","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: 2
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