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Propagation Prediction for Urban Systems
Published in Lal Chand Godara, Handbook of Antennas in Wireless Communications, 2018
Henry L. Bertoni, Saúl A. Torrico
Transmission paths from an elevated base station are depicted in Fig. 3.15 for three classes of subscriber locations in a metropolitan area built on rolling terrain. To model the path gain for these cases, the rows of buildings are assumed to be oriented perpendicular to the plane of curvature of the hills, as shown in the side view in Fig. 3.15, which is also assumed to be the plane of propagation [8, 19]. At location A in Fig. 3.15, the rooftops are within LOS of the rooftops near the mobile. In this case the path loss is determined using the flat terrain approach discussed in Section 3.3, with the difference that the angle α of Eq. (3.9) is replace by αA when computing PG1. This approach is equivalent to replacing the base station antenna height by the effective value heff, as proposed by Lee [46]. At location B in Fig. 3.15, the path gain is calculated by adding to the three terms in Eq. (3.1) a term that accounts for diffraction over the intervening hill, and computing PG1 using the glancing angle αB. When the hill is rounded, it may be necessary to model the diffraction by a creeping ray, as discussed later. At location C in Fig. 3.15, reduction of the rooftop fields near the mobile cannot be separated from diffraction loss at the houses on top of the hill. Instead, propagation involves multiple diffraction past the buildings at the top of the hill down to the buildings near the mobile, and may be modeled by a creeping ray that goes over the hill.
The Keystroke-Level Model
Published in Stuart K. Card, Thomas P. Moran, Allen Newell, The Psychology of Human-Computer Interaction, 2018
Stuart K. Card, Thomas P. Moran, Allen Newell
The correspondence between the user’s mental operations and the model’s predicted M operations is directly exhibited in Figure 8.10. The figure shows histograms of the user’s mental time for the trials graphed in Figure 8.9. A trial consists of seven command executions. Each command has a command-invocation keystroke, such as D for the Delete command or R for the Replace command, indicated in Figure 8.8 by the underlined K’s. The model predicts an M operation immediately preceding each command-invocation keystroke. The command-invocation keystrokes thus provide reference points in the execution by which to compare the locations of mental time. The horizontal axis in Figure 8.10 represents the time preceding the recorded command-invocation keystrokes, normalized to be at time 0. The model’s predicted M operations occur uniformly between the times −1.50 sec and −.15 sec, as is shown by the lightly-shaded histogram at the top of Figure 8.10. The histogram is rectangular, since it represents seven identical M operations stacked on top of each other. The distributions of the user’s actual mental times are shown as the darkly-shaded histograms, which are superimposed over copies of the predicted mental time histogram for comparison. As predicted, most of the user’s actual mental time does occur in the two or three seconds preceding the command-invocation keystrokes. The figure also gives for each trial the percentage ratio of the user’s actual mental time to the predicted mental time.
Dec Vax
Published in Paul W. Ross, The Handbook of Software for Engineers and Scientists, 2018
R. L. Davis, L. A. Knox, T. E. Mertz
The REPLACE command combines the DELETE and INSERT functions in one command. You can use REPLACE when you need to delete a block of text and want to type new text in that location. The syntax for the REPLACE command is *REPLACE range <Return> text <Control/Z> The following REPLACE command deletes the current line and shifts to insert mode: *REPLACE <Return> Type the text and then press <Control/Z> to save the inserted text and get out of Insert mode.
Towards More Direct Text Editing With Handwriting Interfaces
Published in International Journal of Human–Computer Interaction, 2023
We observed that most text editing started with a text selection when participants used the indirect-writing text editor. In addition, we observed that participants used the in-word correction control more often than deleting and re-entering text when they use the direct-editing text editor. These observations suggest that an option for replacing selected text is important, and the direct-writing text editor should provide an operation for replacing selected text. An essential aspect of the “replace” operation is to allow users to see the original text while entering new text. We observed that most participants read the original text while paraphrasing it in the writing panel. A simple approach may be to add “replace” command to the context menu of a text selection. When the command is invoked, a writing block may appear before or after the currently selected text. When an input to the writing block is finished, the new text in the writing block may replace the selected text.
Spectral efficiency of adaptive OFDM systems over high mobility Nakagami-m fading channels
Published in Systems Science & Control Engineering, 2020
Xiuyan Zhang, Qiming Sun, Yan Gao
In this paper, RPA has been studied to maximize ASE with square MQAM subject to instantaneous BER and average power constraints over NMF channels, and the ASE of RPA and RA have been compared. Especially, we obtained closed-form expressions for the capacity when these adaptation schemes were used in NMF channels with existence ICI at the high mobility. By comparing different speeds, the ASE of RSBA and RA at the same average SNR, the simulation results indicate that RSBA is more suitable for high-speed communication environment, because RSBA could reduce the impact of ICI by increasing the subcarriers bandwidth. Therefore, we can conclude that RSBA is more suitable for scenes with frequent speed changes. But RSBA can only be used in scenarios with few users, because it increases ASE by reducing the number of subcarriers. For the case of constant speed, we can replace RPA with RA to reduce computing complexity. And we can increase the number of subcarriers to increase the number of users in the low-speed or flat fading channels. Similarly, we can decrease number of subcarriers to keep high ASE at the high-speed. For this paper, we not study the influence of imperfect CSI and multi-user adaptation, but we will continue to work in this direction.
Affinity propagation clustering algorithm based on large-scale data-set
Published in International Journal of Computers and Applications, 2018
Limin Wang, Kaiyue Zheng, Xing Tao, Xuming Han
The main innovation points of this paper:In this paper, the idea of grid clustering was used to divide the large-scale data-sets into several small-scale subsets suitable for the operation of the AP algorithm, which reduced the computational complexity and saved the time cost.Computing the distance of the clustering center by introducing structure similarity, and replace the calculation method of the Euclidean distance of DP algorithm.This paper made use of the DP algorithm to quickly cluster the cluster centers after division, and then completed the clustering of the whole large-scale data-set.The experimental results showed that the improved algorithm was better than the original algorithm in the clustering effect and computation speed.