Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081640
Maria Kyrarini, Sameer Naeem, Xingchen Wang, A. Gräser
Commonly used path planning techniques for object manipulation are computationally expensive and time-consuming. In this paper, a novel framework called Skill Robot Library (SRL), which has competence to store only the keypoints of a path rather than complete, is presented. The path can be computed with path planner or taught by a human using kinesthetic teaching. Additionally, when the environment is static and only the requested new start and goal positions are changed with respect to the start and goal positions of the stored path, the SRL can retrieve and modify the stored path. The SRL forwards the final path to the robot for reproduction. Experimental results achieved with a six degrees of freedom robotic arm are presented together with performance evaluation of the SRL and the path planner is demonstrated via a series of experiments.
{"title":"Skill robot library: Intelligent path planning framework for object manipulation","authors":"Maria Kyrarini, Sameer Naeem, Xingchen Wang, A. Gräser","doi":"10.23919/EUSIPCO.2017.8081640","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081640","url":null,"abstract":"Commonly used path planning techniques for object manipulation are computationally expensive and time-consuming. In this paper, a novel framework called Skill Robot Library (SRL), which has competence to store only the keypoints of a path rather than complete, is presented. The path can be computed with path planner or taught by a human using kinesthetic teaching. Additionally, when the environment is static and only the requested new start and goal positions are changed with respect to the start and goal positions of the stored path, the SRL can retrieve and modify the stored path. The SRL forwards the final path to the robot for reproduction. Experimental results achieved with a six degrees of freedom robotic arm are presented together with performance evaluation of the SRL and the path planner is demonstrated via a series of experiments.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129838621","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081292
Nara Hahn, F. Winter, S. Spors
Wave Field Synthesis (WFS) is a spatial sound reproduction technique aiming at a physically accurate reconstruction of a desired sound field within an extended listening area. It was shown in a recent study that the accuracy of the synthesized sound field can be improved in a local area by applying a spatial band-limitation to the driving function. However, the computational complexity of the frequency-domain driving function is demanding because of the involved Bessel functions. In this paper, a time-domain WFS driving function is introduced for the synthesis of a spatially band-limited plane wave. The driving function is obtained based on a time-domain representation of the sound field which is given as a superposition of plane waves with time-varying direction and amplitude. The performance of the proposed approach is evaluated by numerical simulations. Practical issues regarding the discretization of the analytic driving function and dynamic range control are discussed.
{"title":"Synthesis of a spatially band-limited plane wave in the time-domain using wave field synthesis","authors":"Nara Hahn, F. Winter, S. Spors","doi":"10.23919/EUSIPCO.2017.8081292","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081292","url":null,"abstract":"Wave Field Synthesis (WFS) is a spatial sound reproduction technique aiming at a physically accurate reconstruction of a desired sound field within an extended listening area. It was shown in a recent study that the accuracy of the synthesized sound field can be improved in a local area by applying a spatial band-limitation to the driving function. However, the computational complexity of the frequency-domain driving function is demanding because of the involved Bessel functions. In this paper, a time-domain WFS driving function is introduced for the synthesis of a spatially band-limited plane wave. The driving function is obtained based on a time-domain representation of the sound field which is given as a superposition of plane waves with time-varying direction and amplitude. The performance of the proposed approach is evaluated by numerical simulations. Practical issues regarding the discretization of the analytic driving function and dynamic range control are discussed.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294053","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081298
Ganapati Hegde, M. Pesavento, M. Pfetsch
In this paper, we consider a wireless system with a central station equipped with a large number of antennas surveilling a multitude of single antenna devices. The devices become active and transmit blocks of symbols sporadically. Our objective is to blindly identify the active devices and detect the transmit symbols. To this end, we exploit the sporadic nature of the device to station communication and formulate a sparse optimization problem as an integer program. Furthermore, we employ the convex relaxation of the discrete optimization variables in the problem in order reduce its computational complexity. A procedure to further lower the symbol detection errors is also discussed. Finally, the influence of system parameters on the performance of the proposed techniques is analysed using simulation results.
{"title":"Joint active device identification and symbol detection using sparse constraints in massive MIMO systems","authors":"Ganapati Hegde, M. Pesavento, M. Pfetsch","doi":"10.23919/EUSIPCO.2017.8081298","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081298","url":null,"abstract":"In this paper, we consider a wireless system with a central station equipped with a large number of antennas surveilling a multitude of single antenna devices. The devices become active and transmit blocks of symbols sporadically. Our objective is to blindly identify the active devices and detect the transmit symbols. To this end, we exploit the sporadic nature of the device to station communication and formulate a sparse optimization problem as an integer program. Furthermore, we employ the convex relaxation of the discrete optimization variables in the problem in order reduce its computational complexity. A procedure to further lower the symbol detection errors is also discussed. Finally, the influence of system parameters on the performance of the proposed techniques is analysed using simulation results.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129992933","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081488
S. Fortunati
In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
{"title":"Misspecified Cramér-rao bounds for complex unconstrained and constrained parameters","authors":"S. Fortunati","doi":"10.23919/EUSIPCO.2017.8081488","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081488","url":null,"abstract":"In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131056119","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081603
N. Petrov, F. L. Chevalier, N. Bogdanović, A. Yarovoy
The problem of range-migrating target detection in a compound-Gaussian clutter is studied here. We assume a target to have a range-walk of a few range cells during the coherent processing interval, when observed by wideband radar with high range resolution. Two CFAR detectors are proposed assuming different correlation properties of clutter over range. The detectors' performance is studied via numerical simulations and a significant improvement over existing techniques is demonstrated.
{"title":"Range migrating target detection in correlated compound-Gaussian clutter","authors":"N. Petrov, F. L. Chevalier, N. Bogdanović, A. Yarovoy","doi":"10.23919/EUSIPCO.2017.8081603","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081603","url":null,"abstract":"The problem of range-migrating target detection in a compound-Gaussian clutter is studied here. We assume a target to have a range-walk of a few range cells during the coherent processing interval, when observed by wideband radar with high range resolution. Two CFAR detectors are proposed assuming different correlation properties of clutter over range. The detectors' performance is studied via numerical simulations and a significant improvement over existing techniques is demonstrated.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122382916","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081203
H. Schepker, L. Tran, S. Nordholm, S. Doclo
Commonly adaptive filters are used to reduce the acoustic feedback in hearing aids. While theoretically allowing for perfect cancellation of the feedback signal, in practice the adaptive filter solution is typically biased due to the closed-loop hearing aid system. In contrast to conventional behind-the-ear hearing aids, in this paper we consider an earpiece with multiple integrated microphones. For such an earpiece it has previously been proposed to use a fixed null-steering beamformer to reduce the acoustic feedback in the microphones. In this paper we propose to combine the fixed null-steering beamformer with an additional adaptive filter to cancel the residual feedback component in the beamformer output. We compare the combination of the fixed null-steering beamformer and different adaptive filtering algorithms including subband adaptive filtering and the prediction-error-method based fullband adaptive filtering with using either of the two approaches alone. Experimental results using measured acoustic feedback show the benefit of using the combined approach compared to using either of the two approaches to cancel the acoustic feedback.
{"title":"Combining null-steering and adaptive filtering for acoustic feedback cancellation in a multi-microphone earpiece","authors":"H. Schepker, L. Tran, S. Nordholm, S. Doclo","doi":"10.23919/EUSIPCO.2017.8081203","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081203","url":null,"abstract":"Commonly adaptive filters are used to reduce the acoustic feedback in hearing aids. While theoretically allowing for perfect cancellation of the feedback signal, in practice the adaptive filter solution is typically biased due to the closed-loop hearing aid system. In contrast to conventional behind-the-ear hearing aids, in this paper we consider an earpiece with multiple integrated microphones. For such an earpiece it has previously been proposed to use a fixed null-steering beamformer to reduce the acoustic feedback in the microphones. In this paper we propose to combine the fixed null-steering beamformer with an additional adaptive filter to cancel the residual feedback component in the beamformer output. We compare the combination of the fixed null-steering beamformer and different adaptive filtering algorithms including subband adaptive filtering and the prediction-error-method based fullband adaptive filtering with using either of the two approaches alone. Experimental results using measured acoustic feedback show the benefit of using the combined approach compared to using either of the two approaches to cancel the acoustic feedback.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131087000","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081644
Anthony Griffin, N. Pradel, Brody Radford, David I. Wilson, A. Ensor
In this paper we present our end-to-end model of the imaging pipeline in the Square Kilometre Array. Our Sky Generator models the signals that are received by the Central Signal Processor (CSP), our CSP Correlator model then processes those signals to generate visibilities to pass to the Science Data Processor (SDP). Our SDP Imaging model then grids the visibilities and inverse Fourier transforms them to produce a dirty image of the sky. Our modelling allows us to investigate the error that is introduced due to reduced numerical precision, and we then propose techniques to mitigate this error, and thus reduce the required amount of computational hardware.
{"title":"Precision analysis of the imaging pipeline in the square kilometre array","authors":"Anthony Griffin, N. Pradel, Brody Radford, David I. Wilson, A. Ensor","doi":"10.23919/EUSIPCO.2017.8081644","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081644","url":null,"abstract":"In this paper we present our end-to-end model of the imaging pipeline in the Square Kilometre Array. Our Sky Generator models the signals that are received by the Central Signal Processor (CSP), our CSP Correlator model then processes those signals to generate visibilities to pass to the Science Data Processor (SDP). Our SDP Imaging model then grids the visibilities and inverse Fourier transforms them to produce a dirty image of the sky. Our modelling allows us to investigate the error that is introduced due to reduced numerical precision, and we then propose techniques to mitigate this error, and thus reduce the required amount of computational hardware.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123715687","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081580
Ted Kronvall, Stefan Ingi Adalbjornsson, Santhosh Nadig, A. Jakobsson
In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.
{"title":"Online group-sparse estimation using the covariance fitting criterion","authors":"Ted Kronvall, Stefan Ingi Adalbjornsson, Santhosh Nadig, A. Jakobsson","doi":"10.23919/EUSIPCO.2017.8081580","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081580","url":null,"abstract":"In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123739069","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081538
O. Meur, A. Coutrot, Zhi Liu, P. Rama, Adrien Le Roch, A. Helo
Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6–10 year-old, 4–6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% of the time. The use of predicted salience does not further improve the classification's accuracy.
{"title":"Your gaze betrays your age","authors":"O. Meur, A. Coutrot, Zhi Liu, P. Rama, Adrien Le Roch, A. Helo","doi":"10.23919/EUSIPCO.2017.8081538","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081538","url":null,"abstract":"Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict the age of an observer. Eye movements of 101 participants split into 4 age groups (adults, 6–10 year-old, 4–6 year-old and 2 year-old) were recorded while exploring static images. The analysis of observers' gaze provides evidence of age-related differences in viewing patterns. Therefore, we extract from the scanpaths several features, including fixation durations and saccade amplitudes, and learn a direct mapping from those features to age using Gentle AdaBoost classifiers. Experimental results show that the proposed image-blind method succeeds in predicting the age of the observer up to 92% of the time. The use of predicted salience does not further improve the classification's accuracy.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132333472","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081194
S. Dias, Marcelo G. S. Bruno
This paper introduces a methodology for numerical computation of the Posterior Cramér-Rao Lower Bound (PCRLB) for the position estimate mean-square error when a moving emitter is tracked by a network of received-signal-strength (RSS) sensors using a distributed, random exchange diffusion filter. The square root of the PCRLB is compared to the empirical root-mean-square error curve for a particle filter implementation of the diffusion filter, referred to as RndEx-PF, and to the square root of the PCRLB for the optimal centralized filter that assimilates all network measurements at each time instant. In addition, we also compare the proposed RndEx-PF algorithm to three alternative distributed trackers based on Kullback-Leibler fusion using both iterative consensus and non-iterative diffusion strategies.
{"title":"Performance bounds for cooperative RSS emitter tracking using diffusion particle filters","authors":"S. Dias, Marcelo G. S. Bruno","doi":"10.23919/EUSIPCO.2017.8081194","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081194","url":null,"abstract":"This paper introduces a methodology for numerical computation of the Posterior Cramér-Rao Lower Bound (PCRLB) for the position estimate mean-square error when a moving emitter is tracked by a network of received-signal-strength (RSS) sensors using a distributed, random exchange diffusion filter. The square root of the PCRLB is compared to the empirical root-mean-square error curve for a particle filter implementation of the diffusion filter, referred to as RndEx-PF, and to the square root of the PCRLB for the optimal centralized filter that assimilates all network measurements at each time instant. In addition, we also compare the proposed RndEx-PF algorithm to three alternative distributed trackers based on Kullback-Leibler fusion using both iterative consensus and non-iterative diffusion strategies.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130212244","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}
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