Normalizing transducer signals: An overview of a proposed standard

Monte, Gustavo; Liu, Zheng; Abate, Francesco; Paciello, Vincenzo; Pietrosanto, Antonio; Huang, Victor

doi:10.1109/i2mtc.2014.6860817

Cited by 24 publications

(3 citation statements)

References 9 publications

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“…Among the algorithms with low computational load, there is one that provides a segmentation and labeling of the raw data samples [15]. This latter technique has been considered for the definition of the new standard of the IEEE 1451 [16], [17]. This paper reports the comparison of these two algorithms.…”

Section: Introductionmentioning

confidence: 97%

A Comparison Between Sensor Signal Preprocessing Techniques

Abate

Huang

Monte³

et al. 2015

IEEE Sensors J.

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The need for the use of sensors networks in ever more efficient manner drives research methods for better information management. It would be useful to decrease the amount of managed data. Often we are interested in few noteworthy information of a signal (for example period, amplitude, time constant, steady state value, etc…) not in the whole waveform. The idea is to take less data, but acquire the same information. In a highly oversampled signal, each single sample does not carry a lot of information. From this point, two different algorithms are compared, in which only few samples are stored or transferred. This paper describes these two algorithms: the first one is the segmentation and labeling algorithm, also proposed for the definition of the new standard of the IEEE 1451, and the second one is based on compressive sensing theory. These two algorithms are compared, the simulations results are shown, and it is discussed which case could be more suitable for

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Section: Introductionmentioning

confidence: 97%

A Comparison Between Sensor Signal Preprocessing Techniques

Abate

Huang

Monte³

et al. 2015

IEEE Sensors J.

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“…The paper introduces a novel approach for seismic signal analysis, utilizing a segmentation and elaboration algorithm [7]. This algorithm is specifically designed to run in real-time with low computational requirements while effectively detecting relevant events for earthquake early warning.…”

Section: Introduction *mentioning

confidence: 99%

An IEEE21451-001 Compliant Smart Sensor for Early Earthquake Detection

Carratù,

Iacono,

Paciello

et al. 2023

IEEE Open J. Instrum. Meas.

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This paper introduces a novel smart sensor that employs an advanced algorithm for earthquake early warning (EEW). The sensor utilizes a smart sampling technique to extract significant signal information, simplifying the process of inferring knowledge. The main objective is to assess the potential destructiveness of an incoming earthquake by analysing the initial moments of the P-wave, and to generate an alert for prompt action, if necessary. The study includes the development and presentation of the proposed method, as well as performance evaluations using real seismic data obtained from freely accessible databases. These evaluations confirm the effectiveness of the proposed method in accurately estimating earthquake magnitudes. Furthermore, the paper includes a comparison with a widely used EEW algorithm. The real-time functionality and interoperability of devices are crucial considerations in earthquake detection applications. The suitability and compatibility of the proposed method with the IEEE1451 family of standards are demonstrated and emphasized in the paper.

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“…This algorithm is also included in the proposal of a new version of the standard IEEE 1451 [8]. Performance is compared with the well-known Fast Fourier Transform algorithm, which is the most popular for the frequency measurement, but it is quite complex.…”

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confidence: 99%

A comparison between FFT and MCT for period measurement with an ARM microcontroller

Guia

Espírito-Santo

Paciellco

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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A comparison between two different algorithms to measure the period of a signal is performed on this work. One of the algorithms is based on the Fast Fourier Transformation (FFT) measurement procedure, whereas the other is based on the Segmentation and Labeling algorithm (MeT). Both algorithms have been prepared for implementation in a micro controller with ARM architecture. The measurement operation of each one of the algorithms is described in detail. Experimental results are reported, with the purpose of demonstrating both performance and output format for signal analysis.

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Normalizing transducer signals: An overview of a proposed standard

Cited by 24 publications

References 9 publications

A Comparison Between Sensor Signal Preprocessing Techniques

A Comparison Between Sensor Signal Preprocessing Techniques

An IEEE21451-001 Compliant Smart Sensor for Early Earthquake Detection

A comparison between FFT and MCT for period measurement with an ARM microcontroller

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