The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Luc Vandendorpe - One of the best experts on this subject based on the ideXlab platform.
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USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks
arXiv: Applications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper presents the Universal Software Radio Peripheral (USRP) experimental results of the Max-Min Signal to noise ratio (SNR) Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks which is recently proposed in \cite{BogaMaxMinSNRJournal2013}. Extensive experiments are performed for different set of parameters. In particular, the effects of SNR, number of samples and roll-off factor on the detection performances of the latter algorithms are examined briefly. We have observed that the experimental results fit well with those of the theory. We also confirm that these algorithms are indeed robust against carrier frequency offset, symbol timing offset and noise variance uncertainty.
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ICC - USRP implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks
2014 IEEE International Conference on Communications (ICC), 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper presents the Universal Software Radio Peripheral (USRP) experimental results of the Max-Min Signal to noise ratio (SNR) Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks which is recently proposed in [1]. Extensive experiments are performed for different set of parameters. In particular, the effects of SNR, number of samples and roll-off factor on the detection performances of the latter algorithms are examined briefly. We have observed that the experimental results fit well with those of the theory. We also confirm that these algorithms are indeed robust against carrier frequency offset, symbol timing offset and noise variance uncertainty. © 2014 IEEE.
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Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
IEEE Transactions on Wireless Communications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled Signal of total duration equal to the symbol period is combined linearly. Second, for this combined Signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the Signal Energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm (Pf) and detection (Pd) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better Pd than that of the Eigenvalue decomposition and Energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired Pf(Pd) in the presence of adjacent channel interference Signals.
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CrownCom - Linearly combined Signal Energy based spectrum sensing algorithm for cognitive radio networks with noise variance uncertainty
8th International Conference on Cognitive Radio Oriented Wireless Networks, 2013Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel and simple lineary combined Signal Energy based spectrum sensing algorithm for cognitive radio networks. It is assumed that the transmitter pulse shaping filter is known to the cognitive receiver. And, flat fading channels with synchronous and asynchronous receiver scenarios are considered. For each of these scenarios, the proposed detector is explained as follows: First, by introducing a combiner vector over-sampled Signals with total duration equal to the symbol period are combined linearly. Second, for this combined Signal the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the Energy of the combined Signals corresponding to the maximum and minimum SNRs are proposed as the test statistics For these test statistics, analytical probability of false alarm (Pf) and probability of detection (Pd) expressions are derived for additive white Gaussian noise (AWGN) channel. It is shown that these detectors are robust against noise variance uncertainty Moreover, simulation results demonstrate that the proposed detectors achieve better detection performance compared to tha of the well known Energy detector in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed detectors also guarantee the prescribed Pf(Pd) in the presence of adjacent channel interference Signals.
Tadilo Endeshaw Bogale - One of the best experts on this subject based on the ideXlab platform.
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USRP Implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks
arXiv: Applications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper presents the Universal Software Radio Peripheral (USRP) experimental results of the Max-Min Signal to noise ratio (SNR) Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks which is recently proposed in \cite{BogaMaxMinSNRJournal2013}. Extensive experiments are performed for different set of parameters. In particular, the effects of SNR, number of samples and roll-off factor on the detection performances of the latter algorithms are examined briefly. We have observed that the experimental results fit well with those of the theory. We also confirm that these algorithms are indeed robust against carrier frequency offset, symbol timing offset and noise variance uncertainty.
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ICC - USRP implementation of Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks
2014 IEEE International Conference on Communications (ICC), 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper presents the Universal Software Radio Peripheral (USRP) experimental results of the Max-Min Signal to noise ratio (SNR) Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks which is recently proposed in [1]. Extensive experiments are performed for different set of parameters. In particular, the effects of SNR, number of samples and roll-off factor on the detection performances of the latter algorithms are examined briefly. We have observed that the experimental results fit well with those of the theory. We also confirm that these algorithms are indeed robust against carrier frequency offset, symbol timing offset and noise variance uncertainty. © 2014 IEEE.
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Max-Min SNR Signal Energy Based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty
IEEE Transactions on Wireless Communications, 2014Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled Signal of total duration equal to the symbol period is combined linearly. Second, for this combined Signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the Signal Energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm (Pf) and detection (Pd) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better Pd than that of the Eigenvalue decomposition and Energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired Pf(Pd) in the presence of adjacent channel interference Signals.
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CrownCom - Linearly combined Signal Energy based spectrum sensing algorithm for cognitive radio networks with noise variance uncertainty
8th International Conference on Cognitive Radio Oriented Wireless Networks, 2013Co-Authors: Tadilo Endeshaw Bogale, Luc VandendorpeAbstract:This paper proposes novel and simple lineary combined Signal Energy based spectrum sensing algorithm for cognitive radio networks. It is assumed that the transmitter pulse shaping filter is known to the cognitive receiver. And, flat fading channels with synchronous and asynchronous receiver scenarios are considered. For each of these scenarios, the proposed detector is explained as follows: First, by introducing a combiner vector over-sampled Signals with total duration equal to the symbol period are combined linearly. Second, for this combined Signal the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the Energy of the combined Signals corresponding to the maximum and minimum SNRs are proposed as the test statistics For these test statistics, analytical probability of false alarm (Pf) and probability of detection (Pd) expressions are derived for additive white Gaussian noise (AWGN) channel. It is shown that these detectors are robust against noise variance uncertainty Moreover, simulation results demonstrate that the proposed detectors achieve better detection performance compared to tha of the well known Energy detector in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed detectors also guarantee the prescribed Pf(Pd) in the presence of adjacent channel interference Signals.
S. Soumare - One of the best experts on this subject based on the ideXlab platform.
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A unified approach for estimating transient and long-term stability transfer limits
IEEE Transactions on Power Systems, 1999Co-Authors: R.j. Marceau, S. SoumareAbstract:Until now, the estimation of transient stability transfer limits by means of Signal Energy analysis has been limited to stable time-domain simulations. The paper adapts this analysis to unstable simulations, and extends the method to long-term voltage stability limit estimation as well, thereby proposing a unified approach for both criteria. The method is based on (i) performing transient or long-term time-domain simulations (depending on the type of limit to be found), (ii) extracting the Signal Energy of the time-varying RMS voltage response at appropriate system buses, and (iii) averaging to obtain a "system" Signal Energy. In radial power systems, a more localized average is found to provide slightly better limit-estimating performance (i.e. "corridor" Signal Energy). However, both "system" and "corridor" Signal energies are shown to rise asymptotically and predictably to the limit with increasing power for stable cases, and decreasing power for unstable cases. A simple theory explains this behaviour near the limit as being inversely proportional to power transfer, and test cases performed on a validated model of the 1991 Hydro-Quebec system demonstrate the value of the approach.
Moojun Kim - One of the best experts on this subject based on the ideXlab platform.
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Development and Validation of an NDT Based on Total Sound Signal Energy
Journal of Testing and Evaluation, 2018Co-Authors: Moorak Son, Moojun KimAbstract:This study proposes a new nondestructive testing (NDT) method for assessing the compressive strength of various materials. The method is based on the total sound Signal Energy, which is determined using the sound Signal generated from impacting an object. A device was devised to generate an impact sound with a test object using a rotating, freefalling impact ball and subsequent repetitive impacts from the rebound action. First, to validate the method, soil cement, cement paste, pine wood, shale, and granite specimens were tested to examine the correlation between the direct compressive strength and the total sound Signal Energy. Next, the method was applied to a number of concrete test specimens with various sizes and strengths. The test results of the concrete specimens showed a direct relationship between the direct compressive strength and the total sound Signal Energy, which was dependent on the size of the specimen. Correlation equations between the total sound Signal Energy and the direct compressive strength were determined for different specimen sizes through regression analysis. The equations were then used to estimate the concrete compressive strength and validate the method. Statistical analysis indicated that the estimated compressive strength determined by the total sound Signal Energy quite reliably agreed with the directly measured compressive strength, regardless of the specimen size. It is expected that the new NDT method will play a meaningful role in nondestructively estimating the compressive strength of various materials in the future, including concrete.
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Estimation of the Compressive Strength of Intact Rock Using Non-Destructive Testing Method Based on Total Sound-Signal Energy
Geotechnical Testing Journal, 2017Co-Authors: Moorak Son, Moojun KimAbstract:This study proposes a new non-destructive testing (NDT) method to assess the compressive strength of intact rock. The method is based on total sound-Signal Energy, which is calculated using the sound Signal generated from impacting intact rock. A device was developed to generate an impact sound from a test object using a rotating free-falling impact ball and subsequent repetitive impacts from the rebound action. To validate the method, soil cement, cement paste, pine wood, shale, and granite specimens were first tested to determine the correlation between the direct compressive strength and the total sound-Signal Energy. Next, the method was applied to a number of shale and granite specimens. The results show a direct relationship between the direct compressive strength and the total sound-Signal Energy. Correlation equations between the total sound-Signal Energy and the direct compressive strength were determined for the different rock types, both separately and together through regression analysis. Then, the determined correlation equations were used to estimate the strength of intact rock and validate the method. Statistical analysis indicated that the estimated compressive strength was quite reliable for both rock types. It is expected that the new NDT test method could play a meaningful role in non-destructively estimating the compressive strength of intact rock in the future.
Thomas Heenan Bradley - One of the best experts on this subject based on the ideXlab platform.
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An evaluation of state-of-charge limitations and actuation Signal Energy content on plug-in hybrid electric vehicle, vehicle-to-grid reliability, and economics
IEEE Transactions on Smart Grid, 2012Co-Authors: Casey Quinn, Daniel Zimmerle, Thomas Heenan BradleyAbstract:Researchers have proposed that plug-in hybrid electric vehicles (PHEVs) performing vehicle-to-grid (V2G) ancillary services can accrue significant economic benefits without degrading vehicle performance. However, analyses to date have not evaluated the effect that automatic generator control Signal Energy content and call rate has on V2G ancillary service reliability and value. This research incorporates a new level of detail into the modeling of V2G ancillary services by incorporating probabilistic vehicle travel models, time-series automatic generation control Signals, and time series ancillary services pricing into a non-linear dynamic simulation of the driving and charging behavior of PHEVs. Stochastic results are generated using Monte-Carlo methods. Results show that in order to integrate a V2G system into the existing market and power grid the V2G system will require: 1) an aggregative architecture to meet current industry standard reliability requirements; 2) the construction of low Energy automatic generation control Signals; 3) a lower percent call for V2G even if the pool of contracted ancillary service resources gets smaller; 4) a consideration of vehicle performance degradation due to the potential loss of electrically driven miles; and 5) a high-power home charging capability.