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A Glimpse of LabVIEW
Published in Jivan Shrikrishna Parab, Ingrid Anne Nazareth, Rajendra S. Gad, Gourish Naik, Learning by Doing with National Instruments Development Boards, 2020
Jivan Shrikrishna Parab, Ingrid Anne Nazareth, Rajendra S. Gad, Gourish Naik
An array is a simple data structure consisting of a group of similar elements of a particular data type. An array contains a sequence of data elements of the same data type and size placed in adjacent memory locations which can be referred individually. Therefore arrays store many values below the same name. Each element is accessed by the individual position in the array given by an index also known as a subscript. The index often uses a successive range of integers. The arrays can be one, two or multi dimensional. Arrays are suitable to store data from graphs, waveforms, data collected from multiple sensors or data generated in loops. LabVIEW uses data structures and arrays for this purpose. In LabVIEW, elements of a similar type are grouped in arrays, consisting of dimensions and elements. A dimension is the depth, height or length of an array whereas an element is data that form an array.
Starting with MATLAB and Exploring Its Graphics Capabilities
Published in Jamal T. Manassah, Elementary Mathematical and Computational Tools For Electrical and Computer Engineers Using Matlab®, 2017
In this case, the number of repetitions is controlled by the index variable m, which takes on the values m=1 to 10 in intervals of 1. Therefore, 10 assignments are made. What the above loop is doing is sequentially assigning the different values of m^2 (i.e., m2) for each element of the “x-array.” An array is just a data structure that can hold multiple entries. An array can be 1-D such as in a vector or 2-D such as in a matrix. More will be said about vectors and matrices in subsequent chapters. At this time, assume 1-D and 2-D arrays as pigeonholes with numbers or ordered pair of numbers respectively assigned to them.
Data Structures and Manipulation
Published in Richard J. Roiger, Just Enough R!, 2020
Arrays beyond three dimensions are difficult to visualize. Like matrices, the default is to fill the arrays in column first order. Arrays are useful for storing large amounts of related information. However, from a machine learning perspective, the requirement that all elements of an array must be of the same data type makes them of limited use. It’s time to proceed to multimodal data types where we find the data structures most often applicable for machine learning applications.
Functional verification of a sigma-delta ADC real number model
Published in International Journal of Electronics, 2022
Nikolaos Georgoulopoulos, Prof. Alkis Hatzopoulos
The presented verification architecture based on UVM for the sigma-delta ADC real number model is illustrated in Figure 2. The top-level module of the testbench launches the required test, while it also declares the DUT instance and the interface. Moving down the architecture’s hierarchy, each test exercises a part of the whole functionality of the DUT and it instantiates the verification environment accordingly. Now, the environment class includes an agent, an environment monitor, a golden reference model, which represents a simpler version of the sigma-delta ADC model, and an environment configuration object, that contains crucial information about the agent. The presented agent consists of a configuration object, a driver, a sequencer and a monitor. In the beginning, the sequencer controls the sequences flow to the driver. The basic sequence class has a 32-bit real-type array that represents a lookup table, whose elements are randomly constrained, as it is described in Table 1. The range of values for each array element is picked in this way, in order to ultimately assist in the creation of a smooth sine wave input. Additionally, it creates an
Space vector PWM of three-phase inverter with MPPT for photovoltaic system
Published in Australian Journal of Electrical and Electronics Engineering, 2021
Mostafa Wageh, Sherif M. Dabour, R. M. Mostafa, M. A. Ghalib
Various methods of MPPT control technology have been studied, such as fuzzy control algorithm was discussed in (Irfan and Amin 2021; Ismailou et al. 2021), incremental conductance (IC) method (Lee, Bae, and Cho 2006; Kwon, Nam, and Kwon 2006; Libo, Zhengming, and Jianzheng 2007), fractional open-circuit voltage, the lookup table method (Hiyama, Kouzuma, and Imakubo 1995; Hiyama and Kitabayashi 1997), and neural system controller, perturb and observe (P&O) method (Osram and Chapman 2007; Yedukondalu, Krishna, and Mohan 2021; Khaehintung, Wiangtong, and Sirisuk 2006) and so on. PV array characteristics must be known previously in the lookup table method. At the same time, the characteristics of the photoelectric matrix depend on many complex factors, such as the possible breakdown of individual cells and temperature. Therefore, it is difficult to store and record all possible system conditions (Hiyama, Kouzuma, and Imakubo 1995; Hiyama and Kitabayashi 1997). In contrast, it is not required to know the characteristics of solar panels in the P&O and IC methods (Egiziano et al., 2006). By using a DC/DC converter between the photoelectric generator and the load, peak power from the solar panels is reached by setting its duty cycle via the algorithm MPPT used to change the duty cycle of the DC/DC converter.
A hybrid 3D feature recognition method based on rule and graph
Published in International Journal of Computer Integrated Manufacturing, 2021
Liang Guo, Ming Zhou, Yuqian Lu, Tao Yang, Fan Yang
Graph is a data structure, just like array and linked list. Node and edge form the simplest graph structure. A graph’s information includes two parts: one is node information in graph, the other one is information of describing relationship of nodes(edge), also is topological relationship between nodes and edges, using G(F, E) to represent. In this paper F(G) and E(G) represent collection of faces and collection of edges, respectively. The collection of faces is F(G) = {f1,f2,fn}. In order to represent the collection of edges conveniently, any adjacency edge (fi,fj) is represented by ek, As shown in Figure 4(a) e1 represents (f1,f2), so E(G) = {e1,e2, em}. Undirected graph of Figure 6(a)’s shaft part is shown in Figure 4(aFigure 5).