Pub Date : 2016-12-17DOI: 10.1109/GlobalSIP.2016.7906076
T. Getu, W. Ajib, R. Landry
For intentional or unintentional interferens, radio frequency interference (RFI) is being prevalent in both satellite and terrestrial communications. In this regard, efficient RFI excision algorithms would have a paramount importance. Having relied on recent advances in tensor-based signal processing, the paper proposes oversampled multi-linear subspace estimation and projection (o-MLSEP) algorithm for efficient multi-interferer RFI (MI-RFI) excision in single-input multiple-output (SIMO) systems. Simulations corroborate that o-MLSEP significantly improves MLSEP as the oversampling factor gets larger.
{"title":"Oversampling-based algorithm for efficient RF interference excision in SIMO systems","authors":"T. Getu, W. Ajib, R. Landry","doi":"10.1109/GlobalSIP.2016.7906076","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7906076","url":null,"abstract":"For intentional or unintentional interferens, radio frequency interference (RFI) is being prevalent in both satellite and terrestrial communications. In this regard, efficient RFI excision algorithms would have a paramount importance. Having relied on recent advances in tensor-based signal processing, the paper proposes oversampled multi-linear subspace estimation and projection (o-MLSEP) algorithm for efficient multi-interferer RFI (MI-RFI) excision in single-input multiple-output (SIMO) systems. Simulations corroborate that o-MLSEP significantly improves MLSEP as the oversampling factor gets larger.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133259464","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 : 2016-12-16DOI: 10.1109/GlobalSIP.2016.7905903
D. Shutin, Siwei Zhang
The presented work discusses a distributed algorithm for solving a return-to-base problem in swarm robotics. A swarm of cooperative intelligent agents is used to span a phased array and cooperatively detect and estimate the bearing of a navigational beacon placed at an unknown location. Both signal detection and bearing estimation is solved jointly using sparse Bayesian learning with dictionary refinement. In the considered setting, Bayesian sparsity is used to detect the presence of the signal. Once signal is detected, its parameters are estimated using a gradient-based numerical technique, with both the gradient and the cost function value computed using classical average consensus over only 4 scalar values. As such, the scheme is independent of the network topology and is particularly useful for communication links with low communication rate. Synthetic simulations demonstrate the effectiveness of the algorithm.
{"title":"Distributed sparsity-based bearing estimation with a swarm of cooperative agents","authors":"D. Shutin, Siwei Zhang","doi":"10.1109/GlobalSIP.2016.7905903","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905903","url":null,"abstract":"The presented work discusses a distributed algorithm for solving a return-to-base problem in swarm robotics. A swarm of cooperative intelligent agents is used to span a phased array and cooperatively detect and estimate the bearing of a navigational beacon placed at an unknown location. Both signal detection and bearing estimation is solved jointly using sparse Bayesian learning with dictionary refinement. In the considered setting, Bayesian sparsity is used to detect the presence of the signal. Once signal is detected, its parameters are estimated using a gradient-based numerical technique, with both the gradient and the cost function value computed using classical average consensus over only 4 scalar values. As such, the scheme is independent of the network topology and is particularly useful for communication links with low communication rate. Synthetic simulations demonstrate the effectiveness of the algorithm.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134367475","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 : 2016-12-15DOI: 10.1109/GlobalSIP.2016.7905963
Mohammadhafez Bazrafshan, Nikolaos Gatsis
For a single-phase distribution network with constant-power, constant-current, and constant-impedance loads (ZIP loads), sufficient conditions are presented that explicitly define a region where a unique load-flow solution exists. The Z-Bus method is shown to be a contraction mapping iteration, which upon initialization within this region, is guaranteed to converge to the unique load-flow solution. The sufficient conditions for convergence of the Z-Bus method are numerically verified for IEEE distribution test feeders.
{"title":"Convergence of the Z-Bus method and existence of unique solution in single-phase distribution load-flow","authors":"Mohammadhafez Bazrafshan, Nikolaos Gatsis","doi":"10.1109/GlobalSIP.2016.7905963","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905963","url":null,"abstract":"For a single-phase distribution network with constant-power, constant-current, and constant-impedance loads (ZIP loads), sufficient conditions are presented that explicitly define a region where a unique load-flow solution exists. The Z-Bus method is shown to be a contraction mapping iteration, which upon initialization within this region, is guaranteed to converge to the unique load-flow solution. The sufficient conditions for convergence of the Z-Bus method are numerically verified for IEEE distribution test feeders.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123911609","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 : 2016-12-11DOI: 10.1109/GlobalSIP.2016.7905869
Oguzhan Teke, P. Vaidyanathan
Many interesting ideas relating to signal processing on graphs have evolved in recent years. This paper visits some basic properties in linear system theory that have not been addressed in the context of graphs. In classical discrete-time system theory, a linear system is shift-invariant if and only if it can be described using a “polynomial” transfer function H(z) (albeit of infinite order). For such a system the Fourier transform of the output, Y (ejw), at any frequency ω i does not depend on the Fourier transform of the input X(ejw) at other frequencies ωj,· ≠ ωi (alias-free property). For a linear system, this alias-free property is equivalent to shift invariance, which in turn is equivalent to the existence of a “polynomial” description (transfer function). For linear systems on graphs, however, these three properties are in general not equivalent. This paper establishes conditions under which such equivalence holds, and also places in evidence some situations where it does not.
{"title":"Linear systems on graphs","authors":"Oguzhan Teke, P. Vaidyanathan","doi":"10.1109/GlobalSIP.2016.7905869","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905869","url":null,"abstract":"Many interesting ideas relating to signal processing on graphs have evolved in recent years. This paper visits some basic properties in linear system theory that have not been addressed in the context of graphs. In classical discrete-time system theory, a linear system is shift-invariant if and only if it can be described using a “polynomial” transfer function H(z) (albeit of infinite order). For such a system the Fourier transform of the output, Y (ejw), at any frequency ω i does not depend on the Fourier transform of the input X(ejw) at other frequencies ωj,· ≠ ωi (alias-free property). For a linear system, this alias-free property is equivalent to shift invariance, which in turn is equivalent to the existence of a “polynomial” description (transfer function). For linear systems on graphs, however, these three properties are in general not equivalent. This paper establishes conditions under which such equivalence holds, and also places in evidence some situations where it does not.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130563579","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 : 2016-12-11DOI: 10.1109/GlobalSIP.2016.7905999
Michael Wilmanski, C. Kreucher, A. Hero
To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR's complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features. We show a performance leap from 87.30% to 99.21% accuracy on real collected wide-angle SAR data with the use of complex features.
{"title":"Complex input convolutional neural networks for wide angle SAR ATR","authors":"Michael Wilmanski, C. Kreucher, A. Hero","doi":"10.1109/GlobalSIP.2016.7905999","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905999","url":null,"abstract":"To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR's complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features. We show a performance leap from 87.30% to 99.21% accuracy on real collected wide-angle SAR data with the use of complex features.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130663524","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 : 2016-12-10DOI: 10.1109/GlobalSIP.2016.7906063
Santhosh Karnik, Zhihui Zhu, M. Wakin, J. Romberg, M. Davenport
The discrete prolate spheroidal sequences (DPSS's) provide an efficient representation for signals that are perfectly time-limited and nearly bandlimited. Unfortunately, because of the high computational complexity of projecting onto the DPSS basis — also known as the Slepian basis — this representation is often overlooked in favor of the fast Fourier transform (FFT). In this paper, we show that there exist fast constructions for computing approximate projections onto the leading Slepian basis elements. The complexity of the resulting algorithms is comparable to the FFT, and scales favorably as the quality of the desired approximation is increased. We demonstrate how these algorithms allow us to efficiently compute the solution to certain least-squares problems that arise in signal processing.
{"title":"Fast computations for approximation and compression in Slepian spaces","authors":"Santhosh Karnik, Zhihui Zhu, M. Wakin, J. Romberg, M. Davenport","doi":"10.1109/GlobalSIP.2016.7906063","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7906063","url":null,"abstract":"The discrete prolate spheroidal sequences (DPSS's) provide an efficient representation for signals that are perfectly time-limited and nearly bandlimited. Unfortunately, because of the high computational complexity of projecting onto the DPSS basis — also known as the Slepian basis — this representation is often overlooked in favor of the fast Fourier transform (FFT). In this paper, we show that there exist fast constructions for computing approximate projections onto the leading Slepian basis elements. The complexity of the resulting algorithms is comparable to the FFT, and scales favorably as the quality of the desired approximation is increased. We demonstrate how these algorithms allow us to efficiently compute the solution to certain least-squares problems that arise in signal processing.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129366299","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 : 2016-12-09DOI: 10.1109/GlobalSIP.2016.7905975
M. Kisacikoglu, F. Erden, N. Erdogan
This study proposes a new distributed control strategy for the grid integration of plug-in electric vehicles. The proposed strategy consists of two stages: (i) an offline process to determine an aggregated reference charge power level based on mobility estimation and base load profile, and (ii) a real-time operation based on the distributed control approach. The control algorithm manages PEV charge load profiles in order to flatten the residential distribution transformer loading while ensuring the desired state of the charge (SOC) level. The proposed algorithm is tested on real distribution transformer loading data, and compared with heuristic charging scenarios. The numerical results are presented to demonstrate the impact of the proposed algorithm.
{"title":"A distributed smart PEV charging algorithm based on forecasted mobility energy demand","authors":"M. Kisacikoglu, F. Erden, N. Erdogan","doi":"10.1109/GlobalSIP.2016.7905975","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905975","url":null,"abstract":"This study proposes a new distributed control strategy for the grid integration of plug-in electric vehicles. The proposed strategy consists of two stages: (i) an offline process to determine an aggregated reference charge power level based on mobility estimation and base load profile, and (ii) a real-time operation based on the distributed control approach. The control algorithm manages PEV charge load profiles in order to flatten the residential distribution transformer loading while ensuring the desired state of the charge (SOC) level. The proposed algorithm is tested on real distribution transformer loading data, and compared with heuristic charging scenarios. The numerical results are presented to demonstrate the impact of the proposed algorithm.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121167324","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 : 2016-12-09DOI: 10.1109/GlobalSIP.2016.7905934
Kai Yang, Yuanming Shi, Jun Zhang, Z. Ding, K. Letaief
The curse of big data, propelled by the explosive growth of mobile devices, places overwhelming pressures on wireless communications. Network densification is a promising approach to improve the area spectral efficiency, but to acquire massive channel state information (CSI) for effective interference management becomes a formidable task. In this paper, we propose a novel interference management method which only requires the network connectivity information, i.e., the knowledge of the presence of strong links, and statistical information of the weak links. To acquire such mixed network connectivity information incurs significant less overhead than complete CSI, and thus this method is scalable to large network sizes. To maximize the sum-rate with the mixed network connectivity information, we formulate a rank minimization problem to cancel strong interference and suppress weak interference, which is then solved by a Riemannian trust-region algorithm. Such algorithm is robust to initial points and has a fast convergence rate. Simulation result shows that our approach achieves a higher data rate than the state-of-the-art methods.
{"title":"A low-rank approach for interference management in dense wireless networks","authors":"Kai Yang, Yuanming Shi, Jun Zhang, Z. Ding, K. Letaief","doi":"10.1109/GlobalSIP.2016.7905934","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905934","url":null,"abstract":"The curse of big data, propelled by the explosive growth of mobile devices, places overwhelming pressures on wireless communications. Network densification is a promising approach to improve the area spectral efficiency, but to acquire massive channel state information (CSI) for effective interference management becomes a formidable task. In this paper, we propose a novel interference management method which only requires the network connectivity information, i.e., the knowledge of the presence of strong links, and statistical information of the weak links. To acquire such mixed network connectivity information incurs significant less overhead than complete CSI, and thus this method is scalable to large network sizes. To maximize the sum-rate with the mixed network connectivity information, we formulate a rank minimization problem to cancel strong interference and suppress weak interference, which is then solved by a Riemannian trust-region algorithm. Such algorithm is robust to initial points and has a fast convergence rate. Simulation result shows that our approach achieves a higher data rate than the state-of-the-art methods.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"32 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129542493","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 : 2016-12-09DOI: 10.1109/GlobalSIP.2016.7905976
Sarthak Gupta, V. Kekatos
Energy storage systems are becoming a key component in smart grids with increasing renewable penetration. Storage technologies feature diverse capacity, charging, and response specifications. Investment and degradation costs may require charging batteries at multiple timescales, potentially matching the control periods at which grids are dispatched. To this end, a microgrid equipped with slow- and fast-responding batteries is considered here. Energy management decisions are taken at two stages. Slow-responding batteries are dispatched at an hourly resolution with decisions remaining invariant over multiple fast control slots. Building on Lyapunov optimization, slow- and fast-responding batteries are charged based on real-time and data-dependent with quantifiable sub-optimality bounds. Numerical tests using real data demonstrate the advantage of operating heterogeneous batteries.
{"title":"Real-time operation of heterogeneous energy storage units","authors":"Sarthak Gupta, V. Kekatos","doi":"10.1109/GlobalSIP.2016.7905976","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905976","url":null,"abstract":"Energy storage systems are becoming a key component in smart grids with increasing renewable penetration. Storage technologies feature diverse capacity, charging, and response specifications. Investment and degradation costs may require charging batteries at multiple timescales, potentially matching the control periods at which grids are dispatched. To this end, a microgrid equipped with slow- and fast-responding batteries is considered here. Energy management decisions are taken at two stages. Slow-responding batteries are dispatched at an hourly resolution with decisions remaining invariant over multiple fast control slots. Building on Lyapunov optimization, slow- and fast-responding batteries are charged based on real-time and data-dependent with quantifiable sub-optimality bounds. Numerical tests using real data demonstrate the advantage of operating heterogeneous batteries.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127425035","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 : 2016-12-09DOI: 10.1109/GlobalSIP.2016.7905967
Chaofeng Pan, Xianbin Cao, D. Wu
Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our proposed method operates in three steps: 1) extract edge features of power lines from a testing image, 2) employ a CNN classifier to remove the background noise, 3) use a Hough-Transform (HT) based fine-selection module to locate power lines. Comprehensive experiments demonstrate the superiority of the proposed method, compared to the state-of-the-art methods.
{"title":"Power line detection via background noise removal","authors":"Chaofeng Pan, Xianbin Cao, D. Wu","doi":"10.1109/GlobalSIP.2016.7905967","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2016.7905967","url":null,"abstract":"Tiny target detections, especially power line detection, have received great attention due to its critical role in ensuring the flight safety of low-flying unmanned aerial vehicles (UAVs). In this paper, an accurate and robust power line detection method is proposed, wherein background noise is mitigated by an embedded convolution neural network (CNN) classifier before conducting the final power line extractions. Our proposed method operates in three steps: 1) extract edge features of power lines from a testing image, 2) employ a CNN classifier to remove the background noise, 3) use a Hough-Transform (HT) based fine-selection module to locate power lines. Comprehensive experiments demonstrate the superiority of the proposed method, compared to the state-of-the-art methods.","PeriodicalId":407134,"journal":{"name":"2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126359482","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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