Field Trip Z GUI - VG Hub
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XOR of source and destination MAC addresses and IP addresses inside a supported tunnel, for example, Virtual Extensible LAN (VXLAN). This mode relies on skb_flow_dissect() function to obtain the header fields
Using QGIS Atlas, select your coverage layer which contains geometries and fields. For each geometry in the coverage layer, a new output will be generated. Fields associated with this geometry can be used within text labels. A page will be generated for each feature.
When you have a time-enabled field, scroll the time slider left-to-right. Watch your data change over time. A little preparation is necessary but nothing too painful. Export as an AVI and impress your boss.
When fields are created in QGIS, you can rename them using the Table Manager plugin. You probably thought in ArcGIS, you have to create a new field and copy the contents over to the new field. But the Alter Field (Data Management) can accomplish this.
Selection by location has been improved with its interactive selection tool. When records are selected, they can be filtered even more so (selected from, added to, removed from). The field calculator supports Python and VB to execute code.
Good article! Both systems ArcGIS and QGIS have advantages and disadvantages, depending on the field of use. However, I think because of the costs associated to licensing, QGIS will dominate the GIS field and probably have more worldwide use. Regarding input data formats, some are standardized in the United States and are often not used in other countries. ArcGIS predominates in the U.S. but QGIS is used in the rest of the world.
Similar to the concept of RAID (levels 4, 5, 6, etc.) where parity is calculated, EC encodes a strip of data blocks on different nodes and calculates parity. In the event of a host and/or disk failure, the parity can be leveraged to calculate any missing data blocks (decoding). In the case of DSF, the data block is an extent group. Based upon the read nature of the data (read cold vs. read hot), the system will determine placement of the blocks in the strip.
Pre-existing EC containers will not immediately change to block aware placement after being upgraded to 5.8. If there are enough blocks (strip size (k+n) + 1) available in the cluster these previously node aware strips will move to block aware. New EC containers will build block aware EC strips.
It is always recommended to have a cluster size which has at least 1 more node (or block for block aware data / parity placement) than the combined strip size (data + parity) to allow for rebuilding of the strips in the event of a node or block failure. This eliminates any computation overhead on reads once the strips have been rebuilt (automated via Curator). For example, a 4/1 strip should have at least 6 nodes in the cluster for a node aware EC strip or 6 blocks for a block aware EC strip. The previous table follows this best practice.
Smaller objects may fit in a chunk of a single region (region id, offset, length), whereas larger objects may get striped across regions. When a large object is striped across multiple regions these regions can be hosted on multiple vDisks allowing multiple Stargates to be leveraged concurrently.
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.
Plant diseases and pests detection is a very important research content in the field of machine vision. It is a technology that uses machine vision equipment to acquire images to judge whether there are diseases and pests in the collected plant images . At present, machine vision-based plant diseases and pests detection equipment has been initially applied in agriculture and has replaced the traditional naked eye identification to some extent.
In recent years, with the successful application of deep learning model represented by convolutional neural network (CNN) in many fields of computer vision (CV, computer-vision), for example, traffic detection , medical Image Recognition , Scenario text detection , expression recognition , face Recognition , etc. Several plant diseases and pests detection methods based on deep learning are applied in real agricultural practice, and some domestic and foreign companies have developed a variety of deep learning-based plant diseases and pests detection Wechat applet and photo recognition APP software. Therefore, plant diseases and pests detection method based on deep learning not only has important academic research value, but also has a very broad market application prospect.
Traditional image classification and recognition methods of manual design features can only extract the underlying features, and it is difficult to extract the deep and complex image feature information . And deep learning method can solve this bottleneck. It can directly conduct unsupervised learning from the original image to obtain multi-level image feature information such as low-level features, intermediate features and high-level semantic features. Traditional plant diseases and pests detection algorithms mainly adopt the image recognition method of manual designed features, which is difficult and depends on experience and luck, and cannot automatically learn and extract features from the original image. On the contrary, deep learning can automatically learn features from large data without manual manipulation. The model is composed of multiple layers, which has good autonomous learning ability and feature expression ability, and can automatically extract image features for image classification and recognition. Therefore, deep learning can play a great role in the field of plant diseases and pests image recognition. At present, deep learning methods have developed many well-known deep neural network models, including deep belief network (DBN), deep Boltzmann machine (DBM), stack de-noising autoencoder (SDAE) and deep convolutional neural network (CNN) . In the area of image recognition, the use of these deep neural network models to realize automate feature extraction from high-dimensional feature space offers significant advantages over traditional manual design feature extraction methods. In addition, as the number of training samples grows and the computational power increases, the characterization power of deep neural networks is being further improved. Nowadays, the boom of deep learning is sweeping both industry and academia, and the performance of deep neural network models are all significantly ahead of traditional models. In recent years, the most popular deep learning framework is deep convolutional neural network.
Convolutional Neural Networks, abbreviated as CNN, has a complex network structure and can perform convolution operations. As shown in Fig. 2, the convolutional neural network model is composed of input layer, convolution layer, pooling layer, full connection layer and output layer. In one model, the convolution layer and the pooling layer alternate several times, and when the neurons of the convolution layer are connected to the neurons of the pooling layer, no full connection is required. CNN is a popular model in the field of deep learning. The reason lies in the huge model capacity and complex information brought about by the basic structural characteristics of CNN, which enables CNN to play an advantage in image recognition. At the same time, the successes of CNN in computer vision tasks have boosted the growing popularity of deep learning.
In the convolution layer, a convolution core is defined first. The convolution core can be considered as a local receptive field, and the local receptive field is the greatest advantage of the convolution neural network. When processing data information, the convolution core slides on the feature map to extract part of the feature information. After the feature extraction of the convolution layer, the neurons are input into the pooling layer to extract the feature again. At present, the commonly used methods of pooling include calculating the mean, maximum and random values of all values in the local receptive field [20, 21]. After the data entering several convolution layers and pooling layers, they enter the full-connection layer, and the neurons in the full-connection layer are fully connected with the neurons in the upper layer. Finally, the data in the full-connection layer can be classified by the softmax method, and then the values are transmitted to the output layer for output results.
This section gives a summary overview of plant diseases and pests detection methods based on deep learning. Since the goal achieved is completely consistent with the computer vision task, plant diseases and pests detection methods based on deep learning can be seen as an application of relevant classical networks in the field of agriculture. As shown in Fig. 3, the network can be further subdivided into classification network, detection network and segmentation network according to the different network structures. As can be seen from Fig. 3, this paper is subdivided into several different sub-methods according to the processing characteristics of each type of methods. 59ce067264