Pub Date : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646395
G. Nita, A. Keimpema, Z. Paragi
We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain. Thus, we demonstrate the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests - such as pulsar or fast radio bursts (FRB) - in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.
{"title":"STATISTICAL DETECTION AND CLASSIFICATION OF TRANSIENT SIGNALS IN LOW-BIT SAMPLING TIME-DOMAIN SIGNALS","authors":"G. Nita, A. Keimpema, Z. Paragi","doi":"10.1109/GLOBALSIP.2018.8646395","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646395","url":null,"abstract":"We investigate the performance of the generalized Spectral Kurtosis (SK) estimator in detecting and discriminating natural and artificial very short duration transients in the 2-bit sampling time domain Very-Long-Baseline Interferometry (VLBI) data. We demonstrate that, after a 32-bit FFT operation is performed on the 2-bit time domain voltages, these two types of transients become distinguishable from each other in the spectral domain. Thus, we demonstrate the ability of the Spectral Kurtosis estimator to automatically detect bright astronomical transient signals of interests - such as pulsar or fast radio bursts (FRB) - in VLBI data streams that have been severely contaminated by unwanted radio frequency interference.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134147945","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646517
G. Cucho‐Padin, Yue Wang, L. Waldrop, Z. Tian, F. Kamalabadi
In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)’s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.
{"title":"EFFICIENT RFI DETECTION IN RADIO ASTRONOMY BASED ON COMPRESSIVE STATISTICAL SENSING","authors":"G. Cucho‐Padin, Yue Wang, L. Waldrop, Z. Tian, F. Kamalabadi","doi":"10.1109/GlobalSIP.2018.8646517","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646517","url":null,"abstract":"In this paper, we present an efficient method for radio frequency interference (RFI) detection based on cyclic spectrum analysis that relies on compressive statistical sensing to estimate the cyclic spectrum from sub-Nyquist data. We refer to this method as compressive statistical sensing (CSS), since we utilize the statistical autocovariance matrix from the compressed data. We demonstrate the performance of this algorithm by analyzing radio astronomy data acquired from the Arecibo Observatory (AO)’s L-Wide band receiver (~1.3 GHz), which is typically corrupted by active radars for commercial applications located near AO facilities. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the data, which is measured by receiver operating characteristic (ROC) curves. As a result, it allows fast and computationally efficient identification of RFI-free frequency regions in wideband radio astronomy observations.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"38 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531131","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646581
Andrew Mackey, P. Spachos, K. Plataniotis
Indoor positioning systems are used in a variety of applications from shopping malls and museums to subject monitoring and tracking. The reliability and usability of such systems are highly based on their accuracy as well as cost and ease of deployment. Although the Global Positioning System (GPS) is an accurate solution for outdoor use, it can not be used indoors. A popular approach is a wireless navigation system which makes use of Received Signal Strength Indicators (RSSI). However, signal propagation, as well as surrounding noise and a dynamic environment, can affect their performance. Recent advancements in Bluetooth Low Energy (BLE) devices and the introduction of small and inexpensive beacons can alleviate the problem. In this work, we introduce an indoor navigation system with BLE beacons. To measure system accuracy an Android application was developed to collect the signal. Moreover, a Kalman filter was also developed within the application to improve the accuracy. Experimental results showed improvement of systems accuracy in three square topologies. The Kalman filter improved the accuracy up to 78.9%. while the experiments also show a correlation between the overall accuracy and how close BLE beacons are to each other.
{"title":"Enhanced Indoor Navigation System with Beacons and Kalman Filters","authors":"Andrew Mackey, P. Spachos, K. Plataniotis","doi":"10.1109/GlobalSIP.2018.8646581","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646581","url":null,"abstract":"Indoor positioning systems are used in a variety of applications from shopping malls and museums to subject monitoring and tracking. The reliability and usability of such systems are highly based on their accuracy as well as cost and ease of deployment. Although the Global Positioning System (GPS) is an accurate solution for outdoor use, it can not be used indoors. A popular approach is a wireless navigation system which makes use of Received Signal Strength Indicators (RSSI). However, signal propagation, as well as surrounding noise and a dynamic environment, can affect their performance. Recent advancements in Bluetooth Low Energy (BLE) devices and the introduction of small and inexpensive beacons can alleviate the problem. In this work, we introduce an indoor navigation system with BLE beacons. To measure system accuracy an Android application was developed to collect the signal. Moreover, a Kalman filter was also developed within the application to improve the accuracy. Experimental results showed improvement of systems accuracy in three square topologies. The Kalman filter improved the accuracy up to 78.9%. while the experiments also show a correlation between the overall accuracy and how close BLE beacons are to each other.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134535113","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646515
A. Kazemipour, S. Druckmann
In this paper, we introduce Poly-PCA, a nonlinear dimensionality reduction technique which can capture arbitrary nonlinearities in high-dimensional and dynamic data. Instead of optimizing over the space of nonlinear functions of high-dimensional data Poly-PCA models the data as nonlinear functions in the latent variables, leading to relatively fast optimization. Poly-PCA can handle nonlinearities which do not preserve the topology and geometry of the latents. Applying Poly-PCA to a nonlinear dynamical system successfully recovered the phase-space of the latent variables.
{"title":"Nonlinear Dimensionality Reduction Via Polynomial Principal Component Analysis","authors":"A. Kazemipour, S. Druckmann","doi":"10.1109/GlobalSIP.2018.8646515","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646515","url":null,"abstract":"In this paper, we introduce Poly-PCA, a nonlinear dimensionality reduction technique which can capture arbitrary nonlinearities in high-dimensional and dynamic data. Instead of optimizing over the space of nonlinear functions of high-dimensional data Poly-PCA models the data as nonlinear functions in the latent variables, leading to relatively fast optimization. Poly-PCA can handle nonlinearities which do not preserve the topology and geometry of the latents. Applying Poly-PCA to a nonlinear dynamical system successfully recovered the phase-space of the latent variables.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133843614","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646465
Hamed Yazdanpanah, J. A. Apolinário, P. Diniz, Markus V. S. Lima
A class of algorithms known as feature least-mean-square (F-LMS) has been proposed recently to exploit hidden sparsity in adaptive filter parameters. In contrast to common sparsity-aware adaptive filtering algorithms, the F-LMS algorithm detects and exploits sparsity in linear combinations of filter coefficients. Indeed, by applying a feature matrix to the adaptive filter coefficients vector, the F-LMS algorithm can reveal and exploit their hidden sparsity. However, in many cases the unknown plant to be identified contains not only hidden but also plain sparsity and the F-LMS algorithm is unable to exploit it. Therefore, we can incorporate sparsity-promoting techniques into the F-LMS algorithm in order to allow the exploitation of plain sparsity. In this paper, by utilizing the l0-norm, we propose the l0-norm F-LMS (l0-F-LMS) algorithm for sparse lowpass and sparse highpass systems. Numerical results show that the proposed algorithm outperforms the F-LMS algorithm when dealing with hidden sparsity, particularly in highly sparse systems where the convergence rate is sped up significantly.
{"title":"l0-NORM FEATURE LMS ALGORITHMS","authors":"Hamed Yazdanpanah, J. A. Apolinário, P. Diniz, Markus V. S. Lima","doi":"10.1109/GlobalSIP.2018.8646465","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646465","url":null,"abstract":"A class of algorithms known as feature least-mean-square (F-LMS) has been proposed recently to exploit hidden sparsity in adaptive filter parameters. In contrast to common sparsity-aware adaptive filtering algorithms, the F-LMS algorithm detects and exploits sparsity in linear combinations of filter coefficients. Indeed, by applying a feature matrix to the adaptive filter coefficients vector, the F-LMS algorithm can reveal and exploit their hidden sparsity. However, in many cases the unknown plant to be identified contains not only hidden but also plain sparsity and the F-LMS algorithm is unable to exploit it. Therefore, we can incorporate sparsity-promoting techniques into the F-LMS algorithm in order to allow the exploitation of plain sparsity. In this paper, by utilizing the l0-norm, we propose the l0-norm F-LMS (l0-F-LMS) algorithm for sparse lowpass and sparse highpass systems. Numerical results show that the proposed algorithm outperforms the F-LMS algorithm when dealing with hidden sparsity, particularly in highly sparse systems where the convergence rate is sped up significantly.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124929140","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 : 2018-11-01DOI: 10.1109/GLOBALSIP.2018.8646654
Chase P. Dowling, D. Kirschen, Baosen Zhang
A significant portion of a business’ annual electrical payments can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. A business is incentivized to reduce its power consumption, but accurately predicting the timing of peak demand charges is nontrivial. In this paper we present a decision framework based on predicting the day-ahead likelihood of peak demand charges. We train a feed-forward neural net-work to estimate the probability of system demand peaks and show it outperforms conventional forecasting methods using historical load. Using ERCOT demand and weather data from 2010-2017, we show the effectiveness of our framework.
{"title":"COINCIDENT PEAK PREDICTION USING A FEED-FORWARD NEURAL NETWORK","authors":"Chase P. Dowling, D. Kirschen, Baosen Zhang","doi":"10.1109/GLOBALSIP.2018.8646654","DOIUrl":"https://doi.org/10.1109/GLOBALSIP.2018.8646654","url":null,"abstract":"A significant portion of a business’ annual electrical payments can be made up of coincident peak charges: a transmission surcharge for power consumed when the entire system is at peak demand. This charge occurs only a few times annually, but with per-MW prices orders of magnitudes higher than non-peak times. A business is incentivized to reduce its power consumption, but accurately predicting the timing of peak demand charges is nontrivial. In this paper we present a decision framework based on predicting the day-ahead likelihood of peak demand charges. We train a feed-forward neural net-work to estimate the probability of system demand peaks and show it outperforms conventional forecasting methods using historical load. Using ERCOT demand and weather data from 2010-2017, we show the effectiveness of our framework.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131694164","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646519
G. Abreu, Alireza Ghods
We revisit the super multidimensional scaling (SMDS) wireless localization algorithm first proposed a decade ago, recasting it onto the complex-domain1. Under this new formulation, the edge kernel which carries both angle and distance information simultaneously and plays a central role in the SMDS algorithm, becomes a complex-valued rank-one matrix, resulting in a new complex-domain SMDS framework which yields several advantages over the original, including the elimination of redundancy and the enhancement of conditions to handle information erasure.
{"title":"Hybrid Wireless Localization via Complex-domain Isometric Embedding","authors":"G. Abreu, Alireza Ghods","doi":"10.1109/GlobalSIP.2018.8646519","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646519","url":null,"abstract":"We revisit the super multidimensional scaling (SMDS) wireless localization algorithm first proposed a decade ago, recasting it onto the complex-domain1. Under this new formulation, the edge kernel which carries both angle and distance information simultaneously and plays a central role in the SMDS algorithm, becomes a complex-valued rank-one matrix, resulting in a new complex-domain SMDS framework which yields several advantages over the original, including the elimination of redundancy and the enhancement of conditions to handle information erasure.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131730627","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646641
José Clemente, Fangyu Li, Wenzhan Song
In this paper, the problem of how to balance the energy consumption during data processing in networks is investigated using a fog middleware. We first demonstrate that for a fog network with different kind of nodes, balancing the energy relies on a combinatorial optimization that is solved using graph theory. We propose a transformation of the transshipment graph problem to formulate an optimization problem that we solve with linear programming (LP). We show the possibility of balancing and extending the battery life of the whole network based on cooperation between nodes without jeopardizing individual nodes’ battery life. We use both, emulation and real scenarios to test our optimization algorithm. We show we can balance the network energy, keep all nodes alive and active ~95% of the time.
{"title":"OPTIMAL DATA TASK DISTRIBUTION FOR BALANCING ENERGY CONSUMPTION ON COOPERATIVE FOG NETWORKS","authors":"José Clemente, Fangyu Li, Wenzhan Song","doi":"10.1109/GlobalSIP.2018.8646641","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646641","url":null,"abstract":"In this paper, the problem of how to balance the energy consumption during data processing in networks is investigated using a fog middleware. We first demonstrate that for a fog network with different kind of nodes, balancing the energy relies on a combinatorial optimization that is solved using graph theory. We propose a transformation of the transshipment graph problem to formulate an optimization problem that we solve with linear programming (LP). We show the possibility of balancing and extending the battery life of the whole network based on cooperation between nodes without jeopardizing individual nodes’ battery life. We use both, emulation and real scenarios to test our optimization algorithm. We show we can balance the network energy, keep all nodes alive and active ~95% of the time.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134160894","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646444
Di Wang, Mengdi Huai, Jinhui Xu
In this paper, we present the first results on the sparse inverse covariance estimation problem under the differential privacy model. We first gave an ε-differentially private algorithm using output perturbation strategy, which is based on the sensitivity of the optimization problem and the Wishart mechanism. To further improve this result, we then introduce a general covariance perturbation method to achieve both ε-differential privacy and (ε, δ)-differential privacy. For ε-differential privacy, we analyze the performance of Laplacian and Wishart mechanisms, and for (ε, δ)-differential privacy, we examine the performance of Gaussian and Wishart mechanisms. Experiments on both synthetic and benchmark datasets confirm our theoretical analysis.
{"title":"DIFFERENTIALLY PRIVATE SPARSE INVERSE COVARIANCE ESTIMATION","authors":"Di Wang, Mengdi Huai, Jinhui Xu","doi":"10.1109/GlobalSIP.2018.8646444","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646444","url":null,"abstract":"In this paper, we present the first results on the sparse inverse covariance estimation problem under the differential privacy model. We first gave an ε-differentially private algorithm using output perturbation strategy, which is based on the sensitivity of the optimization problem and the Wishart mechanism. To further improve this result, we then introduce a general covariance perturbation method to achieve both ε-differential privacy and (ε, δ)-differential privacy. For ε-differential privacy, we analyze the performance of Laplacian and Wishart mechanisms, and for (ε, δ)-differential privacy, we examine the performance of Gaussian and Wishart mechanisms. Experiments on both synthetic and benchmark datasets confirm our theoretical analysis.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117327912","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 : 2018-11-01DOI: 10.1109/GlobalSIP.2018.8646706
Marwan Alkhweldi, N. Schmid
This paper analyzes the potential of the Spatial Fourier transform (SFT) for detection of a periodic astrophysical signal and for estimation of parameters of the signal. In place of de-dispersing filter bank data for each Dispersion Measure (DM) trial and then integrating over frequency channels to yield a one-dimensional signal, we apply SFT to filter bank data, then detect periodic astrophysical signals and analyze their parameters such as DM and rotational period. This approach allows searching for periodic astrophysical signals in real time. Its complexity is dominated by the complexity of the SFT. The results of our analysis show promise. Using simulated data we demonstrate that it takes about 3 minutes of observation time to detect a pulsar at an S/N value of 8σ. The SFT data also provide information about the rotation of pulsars and lower and upper bounds on their DM value.
{"title":"SPATIAL FOURIER TRANSFORM FOR DETECTION AND ANALYSIS OF PERIODIC ASTROPHYSICAL PULSES","authors":"Marwan Alkhweldi, N. Schmid","doi":"10.1109/GlobalSIP.2018.8646706","DOIUrl":"https://doi.org/10.1109/GlobalSIP.2018.8646706","url":null,"abstract":"This paper analyzes the potential of the Spatial Fourier transform (SFT) for detection of a periodic astrophysical signal and for estimation of parameters of the signal. In place of de-dispersing filter bank data for each Dispersion Measure (DM) trial and then integrating over frequency channels to yield a one-dimensional signal, we apply SFT to filter bank data, then detect periodic astrophysical signals and analyze their parameters such as DM and rotational period. This approach allows searching for periodic astrophysical signals in real time. Its complexity is dominated by the complexity of the SFT. The results of our analysis show promise. Using simulated data we demonstrate that it takes about 3 minutes of observation time to detect a pulsar at an S/N value of 8σ. The SFT data also provide information about the rotation of pulsars and lower and upper bounds on their DM value.","PeriodicalId":119131,"journal":{"name":"2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP)","volume":"71 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100661","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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