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Network Framework
Published in Yi Qiu, Puxiang Xiong, Tianlong Zhu, The Design and Implementation of the RT-Thread Operating System, 2020
Yi Qiu, Puxiang Xiong, Tianlong Zhu
The subnet mask (also called netmask and address mask) is used to indicate which bits of an IP address identify the subnet where the host is located and which bits are identified as the bit mask of the host. The subnet mask cannot exist alone; it must be used in conjunction with an IP address. The subnet mask has only one effect, which is to divide an IP address into two parts: network address and host address. The subnet mask is the bit of 1, the IP address is the network address, the subnet mask is the bit of 0, and the IP address is the host address. Taking the IP address 192.168.1.10 and the subnet mask 255.255.255.0 as an example, the first 24 bits of the subnet mask (converting decimal to binary) is 1, so the first 24 bits of the IP address 192.168.1 represent the network address. The remaining 0 is the host address.
Configuring TCP/IP on a Windows NT Computer
Published in Steven F. Blanding, Enterprise Operations Management, 2020
The subnet mask represents a sequence of set bits that is logically ANDed with the IP address to determine the extended network address. Because the first or first two bit positions of an IP address indicates the type of address, it also indicates the length of the network portion of the address prior to subnetting. By subtracting the length of the IP address from the ANDed length, the device can determine the length of the subnet portion of the address and the value in the subnet portion. For example, the subnet mask of 255.255.255.0 shown in Exhibit 28.2 when ANDed with the IP address of 205.131.175.97 results in a 24-bit address. However, because the network address of 205.131.175.0 represents a Class C address that consists of a 3-byte network address and 1-byte host address, this indicates that no subnetting occurred. Thus, a subnet mask of 255.255.255.0 represents a nonsubnetted Class C address. Similarly, a subnet mask of 255.255.0 would indicate a nonsubnetted Class B address, while a subnet mask of 255.0.0.0 would represent a nonsubnetted Class A network address.
Third-Generation Cellular Communications: An Air Interface Overview
Published in Jerry D. Gibson, Mobile Communications Handbook, 2017
An important property of PN sequences is that a time-shifted version of any PN sequence can be generated by forming a modulo-2 addition of the PN sequence with a delayed version of itself, that is, the “delay-and-add” property. The delay-and-add property of PN sequences implies that temporal shifting of the PN sequence can be formed with simple logic operations involving a shift register. This leads to the concept of a PN mask. A mask is a binary value with the same number of bits as there are delay elements in the shift register used to generate the PN sequence. The mask is AND'ed bitwise with the contents of each corresponding delay element, and a modulo-2 addition is performed on the results of these AND operations. The resulting sequence is a delayed version of the original PN sequence.
Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews
Published in Systems Science & Control Engineering, 2022
Hong Fang, Guangjie Jiang, Desheng Li
The original pre-trained model BERT was published by Google (Devlin et al., 2018) in 2018. In the tasks of SA, BERT achieves better performance than other previous 1-D or shallow bidirectional word vector models on more complex, unfamiliar sentences (Arora et al., 2020). And soon after, Google provided the BERT-Base-Chinese model for the Chinese language, which uses character-based tokenization. In 2021, Yiming Cui et al. created a series of models of Chinese BERT with the whole word masking (WWM) technology proposed by Google based on the BERT-Base-Chinese model (Cui et al., 2021), Chinese-BERT-wwm is one of them. In WWM, if the part of a term is replaced by Mask, the corresponding and rest of the term are replaced by Mask at once. Mask includes replacement with [MASK] label and keeps the original word, which is replaced with another word randomly. Therefore, the model's ability to capture boundary relationships between words is better.
A video painterly stylization using semantic segmentation
Published in Journal of the Chinese Institute of Engineers, 2022
Der-Lor Way, Rong-Jie Chang, Chin-Chen Chang, Zen-Chung Shih
Figure 3 illustrates the process of GrabCut with dynamic bounding boxes. In the initial stage, a cluster map is obtained by the flood fill algorithm and is used to initialize an array of four coordinates for expanding the bounding box (Figure 3(a)). The expanding coordinates are the upper left, upper right, lower left, and lower right corners of the box. If a component in the cluster map belongs to the foreground, the bounding box around the component is initialized according to the contour outlines and expanding coordinates. The GrabCut mask is set as follows (Figure 3(b)): A region outside the bounding box is set as the background. A region inside the bounding box and covered by the component is set as ‘perhaps foreground;’ otherwise it is set as ‘perhaps background.’ A label of ‘perhaps’ indicates that the label may change from the original prediction after the GrabCut method has been applied. By using this image and mask, the GrabCut method can acquire a better segmentation C’ for component C.
Estimation of articulated angle in six-wheeled dump trucks using multiple GNSS receivers for autonomous driving
Published in Advanced Robotics, 2021
Taro Suzuki, Kazunori Ohno, Shotaro Kojima, Naoto Miyamoto, Takahiro Suzuki, Tomohiro Komatsu, Yukinori Shibata, Kimitaka Asano, Keiji Nagatani
As shown in Figure 5, the driving environment is an open-sky environment, but actual construction sites are often located in mountainous areas, and there is a concern that GNSS satellites may be shielded and GNSS positioning performance may be degraded. In this study, we evaluate the robustness and accuracy of the proposed method by simulating the shielding of GNSS satellites by adding a pseudo GNSS satellite elevation masking process. Specifically, we evaluated the proposed method under three conditions: (a) no satellite elevation mask, (b) satellite elevation mask: 35, and (c) satellite elevation mask: 45. The proposed method is compared with the conventional RTK-GNSS method for dump truck position estimation [6] and with the LiDAR method for articulated angle estimation.