A Cepstrum-Based Spectrum Sensing Approach for Detecting Spread Spectrum Signals

In this manuscript, we introduce a semi-blind spectrum sensing technique based on cepstral analysis for interweave cognitive systems. The misdetection problem of spread spectrum signals leads to erroneous sensing results, which affect the quality-of-service of a legitimate user. The simplicity and accuracy of cepstral analysis approaches make them reliable for signals detection. Therefore, we formulate the averaged autocepstrum detection technique that utilizes the strength of the autocepstral features of spread spectrum signals. The proposed technique is compared with the energy detection and eigenvalue-based detection techniques and shows reliability and efficacy in terms of detection accuracy.

COMPARATIVE STUDY FOR WAVE SPEED ESTIMATION AT A SINGLE AND TWO MEASUREMENT POINTS

Local wave speed is a prognostic detector that allows the analysis of cardiovascular function. Objectives: This study compared wave speed ([Formula: see text] measurements at single-point and two-point techniques. Material and methods: [Formula: see text] were determined from the cepstral analysis of the blood flow velocities, which identified the arrivals times of reflected waves. The blood velocities waveforms were measured by using phase-contrast magnetic resonance (PCMR) for 20 subjects on young and old healthy subjects. Local wave speed was estimated through the arrivals time of reflections waves ([Formula: see text] and the distance separating the measurement site to reflection area ([Formula: see text] or the distance separating the two measurement sites. Results: Our obtained results were in total agreement with reference values reported in the literature. Moreover, the detected results show that there is a high correlation ([Formula: see text]) between the two methods. Conclusion: The analysis of the wave speed variations with advancing age is also achieved out through different regression models.

Breathing crack damage diagnostic strategy using improved MFCC features

In this paper, a new two-stage damage diagnostic technique for breathing crack identification in using improved Mel frequency Cepstral Analysis is proposed for engineering structures. The improvements such as the centre frequencies of Mel-filter bank around the resonant frequencies and the automatic selection of cut-off frequency for frequency conversion (i.e. from Mel-scale to frequency-scale) based on the energy of the response is employed in the present work to customise the estimation of Mel-frequency Cepstral Coefficients (popularly being used for speech signals) for structural vibration responses. In the first stage of the proposed improved Mel-frequency Cepstral Coefficients (MFCC) approach for breathing crack identification, the measured acceleration time history responses are converted into Mel-frequency Cepstral Coefficients using improved Mel frequency Cepstral Analysis. The Mahanabolis distance-based measure between the improved Mel-frequency Cepstral coefficients of the healthy structure and the structure with localized damage is used for confirming the presence of breathing crack using ambient vibration data during online monitoring. In the second stage, the spatial location of breathing crack is established through offline monitoring, by exciting the structure with bitone harmonic excitation. The improved Mel-filter bank energy measured spatially across the structure is used to identify the spatial location(s) of breathing crack. The effectiveness of the proposed approach is verified using the synthetic datasets of the benchmark simply supported beam with a breathing crack, provided by Helsinki Metropolia University of Applied Sciences and a numerically simulated cantilever beam with varied spatial locations and different depths of breathing crack. Finally, experimental investigations have been carried out to demonstrate the practical viability of the proposed MFCC approach. Numerical and experimental studies concluded that the proposed damage diagnostic approach is capable of detecting and localising multiple and also subtle cracks even under varying environmental conditions with noise-contaminated measurements.

Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations

Continuous wheel condition monitoring is indispensable for the early detection of wheel defects. In this paper, we provide an approach based on cepstral analysis of axle-box accelerations (ABA). It is applied to the data in the spatial domain, which is why we introduce a new data representation called navewumber domain. In this domain, the wheel circumference and hence the wear of the wheel can be monitored. Furthermore, the amplitudes of peaks in the navewumber domain indicate the severity of possible wheel defects. We demonstrate our approach on simple synthetic data and real data gathered with an on-board multi-sensor system. The speed information obtained from fusing global navigation satellite system (GNSS) and inertial measurement unit (IMU) data is used to transform the data from time to space. The data acquisition was performed with a measurement train under normal operating conditions in the mainline railway network of Austria. We can show that our approach provides robust features that can be used for on-board wheel condition monitoring. Therefore, it enables further advances in the field of condition based and predictive maintenance of railway wheels.