The Experts below are selected from a list of 19188 Experts worldwide ranked by ideXlab platform

Huaiyu Dai - One of the best experts on this subject based on the ideXlab platform.

  • distributed detection in wireless sensor networks using a multiple access channel
    IEEE Transactions on Signal Processing, 2007
    Co-Authors: Huaiyu Dai
    Abstract:

    Distributed detection in a one-dimensional (1-D) sensor network with correlated sensor observations, as exemplified by two problems-detection of a Deterministic Signal in correlated Gaussian noise and detection of a first-order autoregressive [AR(1)] Signal in independent Gaussian noise, is studied in this paper. In contrast with the traditional approach where a bank of dedicated parallel access channels (PAC) is used for transmitting the sensor observations to the fusion center, we explore the possibility of employing a shared multiple access channel (MAC), which significantly reduces the bandwidth requirement or detection delay. We assume that local observations are mapped according to a certain function subject to a power constraint. Using the large deviation approach, we demonstrate that for the Deterministic Signal in correlated noise problem, with a specially chosen mapping rule, MAC fusion achieves the same asymptotic performance as centralized detection under the average power constraint (APC), while there is always a loss in error exponents associated with PAC fusion. Under the total power constraint (TPC), MAC fusion still results in exponential decay in error exponents with the number of sensors, while PAC fusion does not. For the AR Signal problem, we propose a suboptimal MAC mapping rule which performs closely to centralized detection for weakly correlated Signals at almost all Signal-to-noise ratio (SNR) values, and for heavily correlated Signals when SNR is either high or low. Finally, we show that although the lack of MAC synchronization always causes a degradation in error exponents, such degradation is negligible when the phase mismatch among sensors is sufficiently small

  • distributed detection of a Deterministic Signal in correlated gaussian noise over mac
    International Symposium on Information Theory, 2006
    Co-Authors: Huaiyu Dai
    Abstract:

    Distributed detection of a Deterministic Signal in correlated Gaussian noise in a one-dimensional sensor network is studied in this paper. In contrast to the traditional approach where a bank of dedicated parallel access channels (PAC) is used for transmitting the sensor observations to the fusion center, we explore the possibility of employing a shared multiple access channel (MAC), which significantly reduces the bandwidth requirement or detection delay. We assume that local observations are mapped according to a certain function subject to a power constraint and transmitted simultaneously to the fusion center. Using a large deviation approach, we demonstrate that with a specially-chosen mapping rule, MAC fusion achieves the same asymptotic performance as centralized detection under the average power constraint (APC), while there is always a loss in error exponents associated with PAC fusion. Under the total power constraint (TPC), MAC fusion still results in exponential decay in error exponents with the number of sensors, while PAC fusion does not. Finally, we derive an upper bound on the performance loss due to the lack of perfect synchronization over MAC, and show that the performance degradation is negligible when the phase mismatch among sensors is sufficiently small.

Youcef Ferdi - One of the best experts on this subject based on the ideXlab platform.

  • computation of fractional order derivative and integral via power series expansion and Signal modelling
    Nonlinear Dynamics, 2006
    Co-Authors: Youcef Ferdi
    Abstract:

    The three techniques of s-to-z transform, power series expansion (PSE) and Signal modelling are combined to develop a new procedure for efficiently computing the fractional order derivatives and integrals of discrete-time Signals. A mapping function between the s-plane and the z-plane is first chosen, and then a PSE of this mapping function raised to fractional order is performed to get the desired infinite impulse response of the ideal digital fractional operator. Finally, the desired impulse response is modelled as the impulse response of a linear invariant system whose rational transfer function is determined using Deterministic Signal modelling techniques. Three non-iterative techniques, namely Pade, Prony and Shanks’ methods have been considered in this paper. Using Al-Alaoui’s rule as s-to-z transform, computation examples show that both Prony and Shanks’ method can achieve more accurate fractional differentiation and integration than Pade method which is equivalent to continued fraction expansion technique.

Jerome Antoni - One of the best experts on this subject based on the ideXlab platform.

  • extraction of angle Deterministic Signals in the presence of stationary speed fluctuations with cyclostationary blind source separation
    Mechanical Systems and Signal Processing, 2012
    Co-Authors: Simone Delvecchio, Jerome Antoni
    Abstract:

    This paper addresses the use of a cyclostationary blind source separation algorithm (namely RRCR) to extract angle Deterministic Signals from mechanical rotating machines in presence of stationary speed fluctuations. This means that only phase fluctuations while machine is running in steady-state conditions are considered while run-up or run-down speed variations are not taken into account. The machine is also supposed to run in idle conditions so non-stationary phenomena due to the load are not considered. It is theoretically assessed that in such operating conditions the Deterministic (periodic) Signal in the angle domain becomes cyclostationary at first and second orders in the time domain. This fact justifies the use of the RRCR algorithm, which is able to directly extract the angle Deterministic Signal from the time domain without performing any kind of interpolation. This is particularly valuable when angular resampling fails because of uncontrolled speed fluctuations. The capability of the proposed approach is verified by means of simulated and actual vibration Signals captured on a pneumatic screwdriver handle. In this particular case not only the extraction of the angle Deterministic part can be performed but also the separation of the main sources of excitation (i.e. motor shaft imbalance, epyciloidal gear meshing and air pressure forces) affecting the user hand during operations.

V. I. Kostylev - One of the best experts on this subject based on the ideXlab platform.

  • energy detection characteristics of the quasi Deterministic Signal with the nakagami amplitude
    International Conference Frontiers Signal Processing, 2018
    Co-Authors: Oleg V Chernoyarov, V. I. Kostylev, Alexandra V Salnikova, Alexander N Faulgaber
    Abstract:

    We studied the possibility of applying power method for the radio Signal detection against Gaussian white noise. It is presupposed that the Signal amplitude is random and distributed by the Nakagami law. For this case, we found the distribution of the decision statistics of the energy detector. We obtained the expressions for the probability of correct detection under the discrete processing of the observable data realization within the limited time interval. We also analyzed the influence of the average power Signal-to-noise ratio value and the time-bandwidth product upon the detection characteristics.

  • energy detection of a Signal with random amplitude
    International Conference on Communications, 2002
    Co-Authors: V. I. Kostylev
    Abstract:

    Urkowitz (1967) has discussed the detection of a Deterministic Signal of unknown structure in the presence of band-limited Gaussian noise. That analysis is developed to the case of a Signal with random (Rayleigh, Rice, Nakagami, and other) amplitude. For such amplitude the distribution of a decision statistic of an energy detector is retrieved and expressions for detection probability are obtained.

  • Characteristics of Energy Detection of QuasiDeterministic Radio Signals
    Radiophysics and Quantum Electronics, 2001
    Co-Authors: V. I. Kostylev
    Abstract:

    We determine the decision-statistic distribution of an energy detector for the case of receiving an additive mixture of the Gaussian quasi-Deterministic Signal and Gaussian white noise. Exact and approximate expressions for true detection probability are obtained.

Quan Ding - One of the best experts on this subject based on the ideXlab platform.