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The exploitation of cyclostationarity properties of vibratory signals is now more widely used for monitoring rotating machinery and especially for diagnosing bearing defects. The acoustic emission (AE) technology has also emerged ...
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The exploitation of cyclostationarity properties of vibratory signals is now more widely used for monitoring rotating machinery and especially for diagnosing bearing defects. The acoustic emission (AE) technology has also emerged as a reliable tool for preventive maintenance of rotating machines. In this study, we propose an experimental study that characterizes the cyclostationary aspect of acoustic emission (AE) signals recorded from a defective bearing (40 mu m on the outer race) to see its efficiency to detect a defect at its very early stage of degradation. An industrial sensor (UE10 000) is used. An electrical circuit converts the high frequency signal into an audible signal by heterodyning. The cyclic spectral density, which is a tool dedicated that put into evidence the presence of cyclostationarity, is used for characterizing the cyclostationary. Two new indicators based on this cyclostationary technique are proposed and compared for early detection of defective bearings.
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In this paper, single-carrier (SC) block transmission of improper-complex data symbols is considered over a multiple-input multiple-output (MIMO) frequency-selective channel with data-like cochannel interference (CCI). To exploit ...
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In this paper, single-carrier (SC) block transmission of improper-complex data symbols is considered over a multiple-input multiple-output (MIMO) frequency-selective channel with data-like cochannel interference (CCI). To exploit the cyclostationarity and impropriety of the desired and the interfering signals, an equalizer is designed that oversamples the received signal and then processes the sampled block by using widely-linear (WL) feedforward (FF) and noise-predicting feedback (NP-FB) filters. For the given equalizer structure, the minimum mean-squared error (MMSE)-optimal WL FF and NP-FB filters have high computational complexity due to the augmented correlation matrix of the data-like CCI in the received signal. Motivated by the asymptotic property of the correlation matrix, we approximate in the frequency domain the matrix by a block matrix with diagonal blocks. This leads to the low-complexity WL design of a frequency-domain FF filter and a causally noise-predicting FB filter. It is shown that this MIMO SC frequency-domain equalizer with noise prediction is asymptotically optimal in the sense that the average mean-squared error converges to that of the MMSE-optimal equalizer as the block length tends to infinity. Numerical results show that the proposed equalizer performs well even with a moderate block length.
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Rolling element bearing signals are known to exhibit pseudo-cyclostationary properties that limit the efficiency of health monitoring. This paper investigates the restoration of cyclostationarity of train of impulses generated by ...
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Rolling element bearing signals are known to exhibit pseudo-cyclostationary properties that limit the efficiency of health monitoring. This paper investigates the restoration of cyclostationarity of train of impulses generated by bearing faults with the definition of a bearing angle-time relationship. Two main contributions structure the present paper. The first part presents a unifying synthesis of the state of art on the issue. The concept of cycle of reference (CoR) for cyclostationarity is presented. The possible causes of the well-used concept of slippage of the rolling-element bearing are explored to highlight the limitations of the cyclostationary theoretical framework. The influence of different types of jitters is presented to show the effect of using shaft cycle of reference on the spectral properties. CoR, on which bearing signals are phase locked so to restore their cyclostationarity, and a method to estimate the appropriate angle-time relationship are introduced. In a second part, original contributions on demodulation parameters and the possible introduction of unwanted artefacts are thoroughly explained along with the definition of a hypothesis test. The superiority of cyclostationarity restoration for monitoring purposes is illustrated on two industrial cases.
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In this letter, we propose new methods for esti- mating frequency and phase of a complex sinusoid in complex white Gaussian noise. These new estimators use the cyclostation- arity of the sinusoid which is a cyclostationary signal ...
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In this letter, we propose new methods for esti- mating frequency and phase of a complex sinusoid in complex white Gaussian noise. These new estimators use the cyclostation- arity of the sinusoid which is a cyclostationary signal type. Only one component corresponding to a lag of zero of cyclic autocor- relations is used to reduce the computational load. The perfor- mances of our proposed estimators are compared to those of Kay estimator, Cramer-Rao bound (CRB) and maxim-likelihood es- timator (MLE). Simulation results show that our proposed meth- ods can estimate the frequency and phase correctly even in low signal-to-noise ratio (SNR).
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The present bibliography represents a comprehensive list of references on cyclostationarity and its applications. An attempt has been made to make this bibliography complete by listing most of the existing references up to the yea...
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The present bibliography represents a comprehensive list of references on cyclostationarity and its applications. An attempt has been made to make this bibliography complete by listing most of the existing references up to the year 2005 and by providing a detailed classification group.
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In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being pla...
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In this paper, a new method to perform channel estimation is presented. It is shown that accurate estimation can be obtained when a training sequence is actually arithmetically added to the information data as opposed to being placed in a separate empty time slot: hence, the word "implicit." A closed-form solution for the estimation variance is derived, as well as the Cramer-Rao lower bound. Conditions are derived for the training sequences that result in a channel estimation performance that is independent of the channel characteristics. In addition, estimation performance is shown to be independent of the modulation format. A procedure to synthesize optimal training sequences is presented, and the problem of synchronization is solved. The performance of the algorithm is then compared with other methods that use explicit training under GSM-like environmental conditions, and the new algorithm is shown to be competitive with these. Finally, comparisons are also carried out against blind methods over realistic bandlimited channels, and these show that the new method exhibits good performance.
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Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure ...
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Spectrum sensing is a crucial component of opportunistic spectrum access schemes, which aim at improving spectrum utilization by allowing for the reuse of idle licensed spectrum. Sensing a spectral band before using it makes sure the legitimate users are not disturbed. To that end, a number of different spectrum sensing method have been developed in the literature. Cyclostationary detection is a particular sensing approach that takes use of the built-in periodicities characteristic to most man-made signals. It offers a compromise between achievable performance and the amount of prior information needed. However, it often requires a significant amount of data in order to provide a reliable estimate of the cyclic autocorrelation (CA) function. In this work, we take advantage of the inherent sparsity of the cyclic spectrum in order to estimate CA from a low number of linear measurements and enable blind cyclostationary spectrum sensing. Particularly, we propose two compressive spectrum sensing algorithms that exploit further prior information on the CA structure. In the first one, we make use of the joint sparsity of the CA vectors with regard to the time delay, while in the second one, we introduce structure dictionary to enhance the reconstruction performance. Furthermore, we extend a statistical test for cyclostationarity to accommodate sparse cyclic spectra. Our numerical results demonstrate that the new methods achieve a near constant false alarm rate behavior in contrast to earlier approaches from the literature.
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Detection of the directions of arrival (DOA) and the number of impinging signals is an essential function in many array antenna beamforming techniques. A new ap- proach is presented here for estimating the DOA and the number of de...
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Detection of the directions of arrival (DOA) and the number of impinging signals is an essential function in many array antenna beamforming techniques. A new ap- proach is presented here for estimating the DOA and the number of desired signals in array antennas. The new algorithm exploits the cyclostationarity of the modulated signals, which are disturbed by unknown interferences and noises, to suppress undesired signals. In this approach, the outputs of antenna sensors are applied to a linear prediction model and the cyclic correlation is calculated to utilize the cyclostationarity of the desired signals.
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In this paper, a novel approach is presented to the multiuser channel identification . The approach makes use of the modulation-induced Cyclostationarity, capable of identifying individual channels of different users. By means of ...
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In this paper, a novel approach is presented to the multiuser channel identification . The approach makes use of the modulation-induced Cyclostationarity, capable of identifying individual channels of different users. By means of the decomposition of the cyclic spectrum method, the blind estimation of the channel can be achieved . The approach is rather simple, with considerable advantages over existing techniques, and suitable for the multiuser uplink . The identifiabilily condition and its proof are also concluded in the paper. And finally the. simulation of identification algorithm is given.
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Automatic modulation classification (AMC) is an important component for cognitive radio (CR) in order to identify unknown signals and is useful for CRs to fully grasp the condition of the radio environment. In this paper, signal c...
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Automatic modulation classification (AMC) is an important component for cognitive radio (CR) in order to identify unknown signals and is useful for CRs to fully grasp the condition of the radio environment. In this paper, signal cyclostationarity has been utilized for AMC. The spectral correlation density (SCD) is calculated, and the cyclic frequencies of the spectral peaks are collected as features. Taking hardware implementation into consideration, a decision tree is used as a classifier due to lower complexity. The targeted modulations in this paper are AM, BPSK, QPSK, 8PSK, π/2-DBPSK, π/4-DQPSK, OQPSK, MSK, GMSK, 2FSK and 4FSK signals. Simulation results show reasonable classification performance at SNRs of 6 dB and above.
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