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A Proposal Based on Discrete Events for Improvement of the Transmission Channels in Cloud Environments and Big Data
Published in Rashmi Agrawal, Marcin Paprzycki, Neha Gupta, Big Data, IoT, and Machine Learning, 2020
Reinaldo Padilha Franca, Yuzo Iano, Ana Carolina Borges Monteiro, Rangel Arthur, Vania V. Estrela
Entity Sink will be located by SimEvents, where the event-based signal conversion will be performed for a time-based signal, being converted to a specific type that followed the desired output data parameter, an integer, the bit. By means of the Real-World Value (RWV) function, the current value of the input signal was preserved. Then a rounding was performed with the floor function. This function is responsible for rounding the values to the nearest smallest integer. Zero-Order Hold (ZOH) is used for discrete samples at regular intervals, describing the effect of converting a signal to the time domain, causing its reconstruction and maintaining each sample value for a specific time interval. The treatment logic on bits 1 and 0 is shown in Figure 8.3. Subsequently, the signal is modulated with the advanced modulation format DQPSK and is inserted into the AWGN channel, and then demodulated for the purposes of calculating the BER of the signal, as shown in Figure 8.4.
Hardware and Implementation
Published in Naim A. Kheir, Systems Modeling and Computer Simulation, 2018
Sajjan G. Shiva, Mahmoud Mohadjer
An A/D converter usually consists of two main sections: a sample-and-hold section followed by an A/D converter section. The purpose of a sample-and-hold section is to take a sample from the input analog signal and hold it for a short time to allow the A/D converter to complete its function before the next sample is taken. Figure 12.7 shows a typical sample-and-hold device (zero-order hold) using an operational amplifier. During the sampling interval the sampler switch closes momentarily, and the capacitor C charges up and tracks the input analog voltage x(t). When the input signal is disconnected by the sampler, the capacitor continues to hold, for a short time, the input signal value immediately before disconnection. In practice, the output of the hold device is not constant in response to an input signal but rather decays exponentially with a large time constant. The effectiveness of the zero-order hold as a device, to convert the input pulses into a rectangular pulse wave of a certain width, depends on the sampling frequency (Kuo, 1963). The zero-order hold function improves by increasing the sample rate.
Sampling of continuous signals
Published in Alexander D. Poularikas, ®, 2018
The simplest way to construct f(t), for nTs < t < (n + 1)Ts, from f(nTs) is to hold the value of f(nTs) until the arrival of the next sample value f((n + 1)Ts). This process is known as the zero-order hold. If we had connected the values of the samples by a straight line, then we would talk about a first-order hold. The MATLAB function plot performs this process. Figure 6.2.8a shows the sample values of continuous signal. Figure 6.2.8b shows the zero-order hold, and Figure 6.2.8c shows the construction of the continuous signal using linear interpolation (straight line can be described by a polynomial of t of degree 1).
Unknown Resistive Torque Estimation of a Rotary Drilling System Based on Kalman Filter
Published in IETE Journal of Research, 2022
R. Riane, M. Kidouche, R. Illoul, M. Z. Doghmane
The implementation of Kalman filter in continuous time is used in few cases, but since the majority of applications are implemented in digital computers, continuous system models can be approached to a discrete one with discretization. In our study, a discrete-time Kalman filter is obtained from the continuous-time Kalman model using a zero order hold discretization [26], where a continuous system under a state-space formulation can be transformed into a discrete one as given by Equation (17). where ; is the sampling time, ; is nonsingular, and .
Closed-loop delta-operator-based subspace identification for continuous-time systems utilising the parity space
Published in International Journal of Systems Science, 2021
Miao Yu, Ge Guo, Jianchang Liu
In order to solve the identification problems of continuous-time systems, we derive the delta-operator-based state space model approximating systems (1) firstly. Based on a simple antialiasing filter and zero-order hold, the sampled input–output behaviour of systems (1) is obtained by where q is the shift operator with , Δ is the sampling period. The corresponding coefficient matrices of Equation (3) are where ≃ denotes that both sides are nearly equal when Δ is sufficiently small. In addition, and are stochastic disturbances with covariance matrices:
A recursive algorithm for estimating multiple models continuous transfer function with non-uniform sampling
Published in International Journal of Systems Science, 2018
Anísio Rogério Braga, Walmir Caminhas, Carmela Maria Polito Braga
Another interesting feature of the MMRLS algorithm with λ-filtered data is that the lower-order models use only filtered data instead of the actual y(k). For example, in (25), the matrix U results in the following, for a first-order model: Therefore, the λ-operator actually introduces in the estimation algorithm a nice feature to restrict the system transfer function bandwidth by the λ-operator time constant, τ, as more higher-order models are estimated simultaneously. The λ-operator exerts the role of a progressive restricting bandwidth digital filter that prevents the MMRLS algorithm from adapting to the zero mean noise present in the raw data. Thus, the λ-operator filter adds a desirable interpolation with integrating behaviour, which has an analogy to the role of a zero-order hold when converting from digital to analogue in digital control systems (Åstrom & Wittenmark, 1997; Yuz & Goodwin, 2014).