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Spectrum - Springer Nature

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Last Updated: 09 September 2022

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Investigation of Using Log-Spectrum Averaging (Cepstral Averaging) for Blind Reconstruction of an Unknown Impact Input Force

Then, who estimated the unknown impact loading profile based on response vibrations, is a difficult problem. If the impact location is also unknown, traditional inverse problem solutions are ineffective in reconstructing the ILP. Since there is no need to know the source of the input force, producing a blind ILP estimate is appealing. In addition, identifying individuals from their footfall-induced floor vibration can reveal crucial details about the excitation source, such as, for example, identifying individuals from their footfall-induced floor vibration. Averaging of the structural response at several locations is used to build the blind input reconstruction.

Source link: https://doi.org/10.1007/978-3-031-05405-1_8


A New Tracking Measurement Method Based on QPSK Spread Spectrum Communication Signal

The QPSK scatter spectrum communication signal, which can be used to perform signal integrity testing, is used by the Hongyan satellite. However, the Hongyan QPSK signal's I and Q branches are different from traditional satellite navigation signals because they do not explicitly use the new satellite navigation signal tracking system. This paper, therefore, recommends a tracking system based on QPSK spread spectrum communication signals. Based on FFT and m sequence, the data symbol estimation scheme is developed.

Source link: https://doi.org/10.1007/978-981-19-3387-5_29


Automatic Jammer Signal Classification Using Deep Learning in the Spectrum of AI-Enabled CR-IoT

Given the concept of cognitive radio CR-IoT network, it is expected to integrate self-awareness capabilities into the IoT devices to make the entire network more accessible and efficient. CR-IoT is vulnerable to a variety of abnormal attacks, as does any wireless networks. However, it has become possible to correctly detect and classify malicious signals present in the signal transmission due to the advancement of deep learning systems. We developed deep learning algorithms to classify jammer signals present in a CR-IoT network, using quick Fourier transform and continuous wavelet transform capabilities derived from the received orthogonal frequency division multiplexing signal spectrum in this work.

Source link: https://doi.org/10.1007/978-981-19-1610-6_36


Development of GUI Based Tool for the Visualization of the FBG Spectrum Subjected to Guided Waves

Fiber optical grating sensors have many advantages, including tiny size, ability to be embedded, and insensitivity to magnetic and electric fields, and are therefore commonly used for monitoring in a variety of industries. Because of the small mesh size required for recording wave propagation, numerical experiments with optical fibers are computationally costly. In addition, the change of the strain profile from the numerical model to the FBG sensor's spectral response is a non-trivial process that necessitates complex matrix manipulations. A method for measuring the FBG spectrum change related to GW is also provided in this paper.

Source link: https://doi.org/10.1007/978-3-031-07322-9_18


Comparative Study of SVM and KNN Machine Learning Algorithm for Spectrum Sensing in Cognitive Radio

This paper discusses the development of a machine learning algorithm for cognitive radio. Based on results with respect to false alarm rate, the two machine learning algorithms are developed in a way that can be used to analyze spectrum sensing better than SVM. When secondary users are used, the ROC curve is also plotted for inspecting the spectrum.

Source link: https://doi.org/10.1007/978-981-19-1844-5_41


Response Spectrum Method for Vehicle-Induced Bridge Vibration Serviceability Design

Several negative factors, including bridge fatigue, driving safety, and pedestrianu2019s comfort, among other things, are chiefly caused by the dynamic portion of the load. This paper discusses the development of seismic engineering's response spectrum.

Source link: https://doi.org/10.1007/978-3-030-93236-7_40


Cross-Spectrum-Based Synchronization of Structural Health Monitoring Data

The bulk of synchronization problems mainly concern current wireless SHM schemes; however, synchronization discrepancies may also arise in cable-based networks, particularly if multiple data acquisition units without global clock management are used. With structural response data from real buildings, the success of a cross spectrum synchronization strategy introduced in a recent study, which expands on cross-spectral response measurement angle angles of structural response data converted to the frequency domain, is investigated. The validation test findings reveal that the cross spectrum synchronization scheme is capable of complementing traditional clock synchronization protocols, thus raising the precision of SHM results.

Source link: https://doi.org/10.1007/978-3-031-07258-1_93


Improved Single-Sensor-Based Modal Identification Using Singular Spectrum Analysis

The present research aims to demonstrate the capabilities of singular spectrum analysis as a filter bank that may be integrated into a modal identification framework using single-sensor output data. The original time series is recreated by SSA using main components from the signal subspace, thus reducing noise components entirelyu2014u2014and yields a narrowed indicator that finds its use in modal identification. The findings reveal that SSA can be used as a valuable tool for the analysis of vibratory behavior of structures with well-separated spectral components.

Source link: https://doi.org/10.1007/978-981-19-1862-9_56

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions