Windows 7 activation uninstall other cracks: The ultimate solution for your Windows 7 problems

I have a PC which has windows 7 license but I installed windows from an image I downloaded and it is already activated. For validating genuine Microsoft, I need to enter my own product key but the necessary activation tools do not exist in my windows folder. What should I do?

windows 7 activation uninstall other cracks

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It is likely the that version of windows that you installed does not contain the infrastructure needed to activate properly. There is nothing you can do other than get a real version of windows, and get a fresh install.

I accidentally deleted my windows 10 genuine product key. My friend told me to use elite previlidges in cmd and type "slmgr/upk" he said that my ram will be boosted but it uninstalled the key. I also suggest that microsoft should give a warning rhat the consequences of the command are not good or at least what will it do. My pc came with windows 10 pre installed and there is no sticker of product key on it. Now my friend is pressing on using cracks to activate my windows . But I think they are illegal . What shall I do? Please help as I cannot activate my windows now. Can anyone tell me what are the disadvantages of unactivated windows over activated windows also?

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

Recently, with the development of machine learning classified as deep learning inspired by structure of the brain called artificial neural networks (ANN) [45], many algorithms have been proposed to perform object detection and image classification tasks. ANN is employed to solve many civil engineering problems [46,47,48,49,50]. Gao and Mosalam in [51] applied the transfer learning to detect damage images with structural method, and this method can reduce the computational cost by using the pre-trained neural network model. Meanwhile, the author needs to fine the neural network to perform the crack detection. Local patch information was employed to inspect crack information by convolutional neural networks (CNN) in [52]. In CrackNet [53], the algorithm improved pixel-perfect accuracy based on CNN by discarding pooling layers. In CrackNet-R [54], a recurrent neural network (RNN) is deployed to perform automatic crack detection on asphalt road. Cha et al. [55] adopted a sliding windows based on CNN to scan and detect road crack. Fan et al. in [56] proposed a structured prediction method to detect crack pixels with CNN. The small structured pixel images (27 27 pixels) was input into the neural network, which may generate overload for the computer memory. Ensemble network is proposed to perform crack detection and measure pavement cracks generated in road pavement [57]. Maeda et al. on [58] adopted object detection network architecture to detect crack images, and the network architecture can be transferred to a smartphone to perform road crack detection. Cha et al. used the Faster-RCNN to inspect road cracks [59]. Yang et al. in [60] adopted a fully convolutional network (FCN) to inspect road pavement cracks at pixel level, which can perform crack detection by end-to-end training. Li et al. in [61] employed the you-only-look-once v3 (YOLOv3)-Lite method to inspect the aircraft structures, and the depth wise separable convolution and feature pyramid were adopted to design the network architecture and joined the low- and high-resolution for crack detection. Jenkins et al. presented an encoder-decoder architecture to perform road crack detection, and the function of the encoder and decoder layers are used to reduce the size of input image to generate lower level feature maps, and obtain the resolution of the input data with up-sampling, respectively [62]. Tisuchiya et al. proposed a data augmentation method based on YOLOv3 to perform crack detection, which can increase the accuracy effectively [63]. 2ff7e9595c