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Data
Published in Aditi Majumder, M. Gopi, Introduction to Visual Computing, 2018
A sample is a value (or a set of values) of a continuous function f(t) at a specified value of the independent variable t. Sampling is a process by which one or more samples are extracted from a continuous signal f(t) thereby reducing it to a discrete function f^(t) $ \mathop {\hat{f}}\limits^{{}} (t) $ . The samples can be extracted at equal intervals of the independent variable. This is termed as uniform sampling. Note that the density of sampling can be changed by changing the interval at which the function is sampled. If the samples are extracted at unequal intervals, then it is termed as non‐uniform sampling. These are illustrated in Figure 1.3.
Analytical Techniques for Ultra-Wideband Signals
Published in James D. Taylor, Introduction to Ultra-Wideband Radar Systems, 2020
Muriladhar Rangaswamy, Tapan K. Sarkar
Almost all electromagnetic systems deal with decaying signals and time limited signals. The time limited signals are not bandlimited so the classical sampling theorem does not hold. Moreover, the data gathering capability is limited by the number of data storage spaces and adequate signal representations may require nonuniform sampling.
Digital Filtering with Nonuniformly Sampled Data: From the Algorithm to the Implementation
Published in Marek Miskowicz, Event-Based Control and Signal Processing, 2018
Laurent Fesquet, Brigitte Bidégaray-Fesquet
Nonuniform sampling is especially efficient for sporadic signals. We, therefore, use here an electrocardiogram (ECG) record displayed in Figure 22.2. Its time duration is 14.2735 s. It has been recorded using 28,548 regular samples with 2000 Hz uniform sampling and displays 22 heartbeats.
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes
Published in Technometrics, 2023
In particular, three important spatial sampling schemes (i.e., network layout) are considered: irregular, nonuniform, and shifted uniform sampling. Note that, (i) the irregular sampling (Figure 2(a)) is the general scenario that includes the nonuniform, shifted uniform, and uniform sampling as its special cases; (ii) the two special cases, that is, nonuniform and shifted uniform sampling (Figure 2(b) and (c)), are also investigated because computationally efficient solutions are available in the spectral domain for the two special schemes. In practice, nonuniform sampling is often used to minimize acquisition time, sensor installation cost and power consumption, and is particularly useful for monitoring low-activity signals (Venkataramani and Bresler 2001; Beyrouthy, Fesquet, and Rolland 2015). Shifted uniform sampling (also known as the nested array or difference co-array) involves two nested uniform sensing networks, and significantly increases the degrees of freedom of linear arrays. By nesting two or more uniform linear arrays, shifted uniform sampling can provide degrees of freedom using only M physical sensors, and thus mitigate the issue of spectral aliasing in spectral analysis (Pal and Vaidyanathan 2010; Qin and Amin 2021).
Nonuniform sampling theorems for random signals in the linear canonical transform domain
Published in International Journal of Electronics, 2018
Xu Shuiqing, Jiang Congmei, Chai Yi, Hu Youqiang, Huang Lei
However, all of the abovementioned results have not taken the nonuniform sampling theorems for random signals related to the LCT into consideration. In addition, as far as we know, there are also no research papers that considered the reconstruction of the random signals from various nonuniform samples associated with the LCT. Meanwhile, the nonuniform sampling for random signals has various practical applications. For example, nonuniform sampling can be found in many data acquisition systems because of poor sampling timebase. The errors it produces are often dominating, which could not be neglected in precision instruments (Jenq, 1988; Wei et al., 2011; Zayed, 1993). Therefore, it is worthwhile and practically useful to derive nonuniform sampling theorems for random signals related to the LCT.