The Experts below are selected from a list of 54 Experts worldwide ranked by ideXlab platform

Daniel Massicotte - One of the best experts on this subject based on the ideXlab platform.

  • the jm filter to detect specific frequency in Monitored Signal
    IEEE Transactions on Signal Processing, 2021
    Co-Authors: Marwan A. Jaber, Daniel Massicotte
    Abstract:

    The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a digital Signal processor. It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a Monitored Signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a Monitored Signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete Signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r , where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in Signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm.

  • Fast method to detect specific frequencies in Monitored Signal
    2010 4th International Symposium on Communications Control and Signal Processing (ISCCSP), 2010
    Co-Authors: Marwan A. Jaber, Daniel Massicotte
    Abstract:

    The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing that takes place inside a Digital Signal Processor. It has been known and argued through the literatures that the Fast Fourier Transform (FFT) is useless in detecting a specific frequency in a Monitored Signal because most of the computed results are ignored. In this paper we will present an efficient FFT based method to detect specific frequencies in a Monitored Signal which is compared to the most frequently used method “the Goertzel's Algorithm”. Parallel implementation structure show a fast computation method compared to the Goertzel's algorithm. Computational speedup gains of r using radix-r butterfly are shown.

Marwan A. Jaber - One of the best experts on this subject based on the ideXlab platform.

  • the jm filter to detect specific frequency in Monitored Signal
    IEEE Transactions on Signal Processing, 2021
    Co-Authors: Marwan A. Jaber, Daniel Massicotte
    Abstract:

    The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing inside a digital Signal processor. It has been widely known and argued in relevant literature that the Fast Fourier Transform (FFT) is useless in detecting specific frequencies in a Monitored Signal of length N because most of the computed results are ignored. In this paper, we present an efficient FFT-based method to detect specific frequencies in a Monitored Signal, which will then be compared to the most frequently used method which is the recursive Goertzel algorithm that detects and analyses one selectable frequency component from a discrete Signal. The proposed JM-Filter algorithm presents a reduction of iterations compared to the first and second order Goertzel algorithm by a factor of r , where r represents the radix of the JM-Filter. The obtained results are significant in terms of computational reduction and accuracy in fixed-point implementation. Gains of 15 dB and 19 dB in Signal to quantization noise ratio (SQNR) were respectively observed for the proposed first and second order radix-8 JM-Filter in comparison to Goertzel algorithm.

  • Fast method to detect specific frequencies in Monitored Signal
    2010 4th International Symposium on Communications Control and Signal Processing (ISCCSP), 2010
    Co-Authors: Marwan A. Jaber, Daniel Massicotte
    Abstract:

    The Discrete Fourier Transform (DFT) is a mathematical procedure that stands at the center of the processing that takes place inside a Digital Signal Processor. It has been known and argued through the literatures that the Fast Fourier Transform (FFT) is useless in detecting a specific frequency in a Monitored Signal because most of the computed results are ignored. In this paper we will present an efficient FFT based method to detect specific frequencies in a Monitored Signal which is compared to the most frequently used method “the Goertzel's Algorithm”. Parallel implementation structure show a fast computation method compared to the Goertzel's algorithm. Computational speedup gains of r using radix-r butterfly are shown.

Michele Zorzi - One of the best experts on this subject based on the ideXlab platform.

  • Rate-Distortion Classification for Self-Tuning IoT Networks
    arXiv: Networking and Internet Architecture, 2017
    Co-Authors: Davide Zordan, Michele Rossi, Michele Zorzi
    Abstract:

    Many future wireless sensor networks and the Internet of Things are expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the Signal statistics. New protocols will be then injected as a software as certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the Monitored Signal type or its statistical properties change. We consider a lossy compression scenario, where the application tolerates some distortion of the gathered Signal in return for improved energy efficiency. To reap the full benefits of this paradigm, we discuss an automatic sensor profiling approach where the Signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). We show that this curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples.

Zorzi Michele - One of the best experts on this subject based on the ideXlab platform.

  • Rate-distortion classification for self-tuning IoT networks
    'Institute of Electrical and Electronics Engineers (IEEE)', 2017
    Co-Authors: Zordan Davide, Rossi Michele, Zorzi Michele
    Abstract:

    The Internet of Things, like many future wireless sensor networks, is expected to follow a software defined paradigm, where protocol parameters and behaviors will be dynamically tuned as a function of the Signal statistics. New protocols will be then injected as a software when certain events occur. For instance, new data compressors could be (re)programmed on-the-fly as the Monitored Signal type or its statistical properties change. In this paper, a lossy compression scenario is considered, where the application tolerates some distortion of the gathered Signal in return for improved energy efficiency. To reap the full benefits of this paradigm, an automatic sensor profiling approach is discussed, where the Signal class, and in particular the corresponding rate-distortion curve, is automatically assessed using machine learning tools (namely, support vector machines and neural networks). This curve can be reliably estimated on-the-fly through the computation of a small number (from ten to twenty) of statistical features on time windows of a few hundreds samples

Enrico Zio - One of the best experts on this subject based on the ideXlab platform.

  • Determination of prime implicants by differential evolution for the dynamic reliability analysis of non-coherent nuclear systems
    Annals of Nuclear Energy, 2017
    Co-Authors: Francesco Di Maio, Samuele Baronchelli, Matteo Vagnoli, Enrico Zio
    Abstract:

    We present an original computational method for the identification of prime implicants (PIs) in non-coherent structure functions of dynamic systems. This is a relevant problem for dynamic reliability analysis , when dynamic effects render inadequate the traditional methods of minimal cut-set identification. PIs identification is here transformed into an optimization problem, where we look for the minimum combination of implicants that guarantees the best coverage of all the minterms. For testing the method, an artificial case study has been implemented, regarding a system composed by five components that fail at random times with random magnitudes. The system undergoes a failure if during an accidental scenario a safety-relevant Monitored Signal raises above an upper threshold or decreases below a lower threshold. Truth tables of the two system end-states are used to identify all the minterms. Then, the PIs that best cover all minterms are found by Modified Binary Differential Evolution. Results and performances of the proposed method have been compared with those of a traditional analytical approach known as Quine-McCluskey algorithm and other evolutionary algorithms, such as Genetic Algorithm and Binary Differential Evolution. The capability of the method is confirmed with respect to a dynamic Steam Generator of a Nuclear Power Plant.