A Statistical Approach to Probe Chaos from Noise in Analog and Mixed Signal Designs

Seghaier, Ibtissem; Zaki, Mohamed H.; Tahar, Sofiène

doi:10.1109/isvlsi.2015.101

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…• Noise reduction by subtracting noise o Active Noise Cancellation [12][13][14][15] • Zero/minimum phase delay filtering o Adaptive and Predictive filters [16,17] o Forward-backward filters [18,19] • Noise characterization o Noise Adaptive Models [13,20] It is also in the scope of this research to use novel or uncommon analysis techniques such as presence of chaos determination [21][22][23] or Cepstral analysis [6]. The creation of supporting software tools is also considered.…”

Section: Adas Functions Utilize Advanced Algorithmsmentioning

confidence: 99%

“…The purpose of the adaptive learning algorithm is to learn the time-variant parameters that charac- For example, on the one hand, in reference [13] it is used a model of the sound in ducts to de- During this research we will apply novel methods to distinguish noise from chaos [21,22] in vehicle related signals and systems. Modeling noise supposes a challenge due to its stochastic nature.…”

Section: Adaptive Learning Modulementioning

confidence: 99%

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Advanced digital signal processing methods for effective noise cancellation of vehicle related signals. State of the Art

Rabadán

2018

DOCTUMH

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Métodos avanzados de procesado digital de la señal para la cancelación eficiente del ruido en las señales características de un vehículo. Estado de la cuestión ISSN: 2530-7320 Vol. 3(1), 2017. Artículo p4 Daniel Carriazo Rabadán ABSTRACTThe current trend of vehicle technology's evolution points towards a further automation of the automobile. The information that such a vehicle processes and handles becomes therefore more important from the performance but also from the safety point of view. The main target of this research is to improve the reliability, accuracy and timing of the information measured by mass produced sensors, whether the sensors are set up in single or array configuration. The scope of the considered sensors covers those related to the automotive sector, both current and potential ones. It is planned to achieve the main target by 1) actively reducing the noise to increase the SNR of the sensor measurements and/or by 2) using advanced processing methods that remove/minimize the delay they introduce in the processing system.Key words: automotive, vehicle, digital signal processing, Active Noise Cancelling, Kalman filter, noise, chaos, filer, zero phase. RESUMENLa actual evolución de la tecnología automovilística apunta hacia una mayor automatización del automóvil. La información que este tipo de vehículo es capaz de manejar y procesar se convierte por tanto en clave, no sólo desde el rendimiento sino también desde el punto de vista de la seguridad. El principal objetivo de esta investigación es mejorar la fiabilidad, la precisión y el retraso de la información medida por sensores producidos de forma masiva, ya estén configurados aislada o matricialmente. El alcance de los sensores considerados abarca desde los actualmente usados en el sector automovilístico hasta los que potencialmente serán usados en dicho sector. Se planea conseguir el objetivo principal mediante 1) la reducción activa del ruido para aumentar la SNR de las medidas de los sensores y/o mediante 2) el uso de métodos avanzados de procesamiento que eliminan/minimizan el retraso introducido en el sistema de procesado.Palabras clave: automoción, vehículo, procesado digital de la señal, Cancelación Activa de Ruido, Kalman, ruido, caos, filtro, fase cero.

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Section: Adas Functions Utilize Advanced Algorithmsmentioning

confidence: 99%

Section: Adaptive Learning Modulementioning

confidence: 99%

Advanced digital signal processing methods for effective noise cancellation of vehicle related signals. State of the Art

Rabadán

2018

DOCTUMH

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Distinguishing chaotic from stochastic dynamics via the complexity of ordinal patterns

Zhang

Chen

et al. 2021

AIP Advances

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Distinguishing between chaotic and stochastic dynamics given an input series is a widely studied topic within the time series analysis due to high demand from the practitioners in various fields. Due to one of the fundamental properties of chaotic systems, namely, being sensitive to parameters and initial conditions, chaotic time series exhibit features also observed in randomly generated signals. In this paper, we introduce distance as a measure of similarity between segments based on the ordinal structure. Furthermore, we introduce a new fuzzy entropy, Fuzzy Permutation Entropy (FPE), which can be used to detect determinism in time series. FPE immunes from repeated equal values in signals to some extent, especially for chaotic series. With specific embedding dimensions, it can be employed to distinguish chaotic signals from noise. We show an example for white Gaussian noise, autoregressive moving-average, continuous or discrete chaotic time series, and test FPE’s performance with additive observational noise. We show an application of FPE on rolling bearings’ fault diagnosis.

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Cross recurrence verification technique for process variation-resilient analog circuits

Seghaier

Zaki

Tahar

2016

2016 IEEE International Symposium on Circuits and Systems (ISCAS)

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A Statistical Approach to Probe Chaos from Noise in Analog and Mixed Signal Designs

Cited by 6 publications

References 11 publications

Advanced digital signal processing methods for effective noise cancellation of vehicle related signals. State of the Art

Advanced digital signal processing methods for effective noise cancellation of vehicle related signals. State of the Art

Distinguishing chaotic from stochastic dynamics via the complexity of ordinal patterns

Cross recurrence verification technique for process variation-resilient analog circuits

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