If you are still experiencing problems, or you want to make sure that you have the correct license key, you can install the Trial version or purchase a license.Īutodesk Maya 3dsMax 2012 with UAT. ![]() You can try the software without ever installing anything. The first five characters of the part number should also be the product key for that product.Autodesk MAYA Community Edition 2016, 924H1 Autodesk MAYA with Softimage 2016, 1036H1 Autodesk Inventor 2016, 1588H1. Open this file in notepad and verify that the product name is what you expected it to be.Ĥ. In that folder, look for a file named MID.txt, MID01.txt, MID02.txt or some variation on that name.ģ. Using your installation media, (USB key, DVD, download folder, etc.) navigate to the location of the setup.exe file for your Autodesk product.Ģ. If, for whatever reason, you cannot locate your product key, there is another method:ġ. ![]() If you ordered your product using the online Autodesk store, the serial number and product key will be provided in the “Order Details” confirmation screen after the purchase, as well as the subsequent “Thank You” e-mail that you will receive after the purchase process is complete. If you have already downloaded the product and just need to know the serial number and product key, pick the “Get Serial” button for your product to have it display this information. If you have not already downloaded the product, picking the download button will start the download and will display the products serial number and product key. If you participate in the Autodesk Education Community, you can find this information by logging in and locating the product in question. If you have physical media, you’ll see the serial number and product key printed on the label of the box. Serial Numbers do not appear on software packaging for Autodesk software versions 2014 and newer. If you have physical media (a DVD or USB key) for a 2013 or earlier product, your serial number and product key will be printed on the label of the product packaging. Only an administrator can assign you as a Named User or End User and give you permissions to download and activate the software. If you do not see the software you wish to activate in your Autodesk account or see the message "Contact your admin for serial numbers," you need to contact the contract administrator. You are the account administrator if you purchased a software subscription using your Autodesk Account or were assigned the role of Contract Manager or Software Coordinator by your company. Note about serial number visibility in Autodesk Account: Only account administrators, such as Contract Managers and Software Coordinators, and Named Users with assigned software benefits will see serial numbers in Autodesk Account. The serial number and product key for your Autodesk software can be found in a variety of locations, depending on how you obtained your product.įind Serial Numbers and Product Keys in Autodesk Account: Your Serial Number and Product Key are displayed in your Autodesk Account in the product tray on the Products & Services page and also again in the Software Download window. Entering an incorrect product key will result in activation errors for that product. ![]() Note: Please ensure you are using the correct product key for the Autodesk product and version you are installing. The same version of AutoCAD is in both software packages but the product key differentiates one package from the other. For example, installing AutoCAD 2018 as a point product requires product key 001J1, but installing AutoCAD 2018 from the AutoCAD Design Suite Premium 2018 requires product key 768J1. Product keys are required for installation of Autodesk products and are used to differentiate products that are both sold independently and as part of a product suite.
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This implies that the correlation between the two images obtained from the different X-ray energy is negligible. These observations allowed us to consider flood-field images used in DE NNPS analysis as independent processes. It is worth noting that Equation (15) described the measured DE NNPS. The effects of various wf, t Sn, ξ, and NR filtering on the DE NNPS are shown in Fig. The ACNR filtering showed higher DE MTF performance, with respect to the other NR filtering at the spatial frequencies above 2.3 mm -1 (i.e., high-frequency), as shown in Fig. Hence, these characteristics resulted from the subtraction of two MTFs having different spatial-resolution characteristics, and HE images were filtered by GNR, which reduces noise but degrades spatial resolution. All NR filtering decreased the DE MTF, the GNR filtering showed boost-up characteristics at the spatial frequencies 1.3 mm -1 because of Gaussian filtering of the HE image. 5C, the DE MTF was nearly independent of ξ, except for ξ=0.1. Therefore, it is important to analyze the X-ray interaction through Monte Carlo N-particles (MCNP) simulations (e.g., particle-tracking tally). 5B, DE MTF degraded by increasing Sn-filter thickness could be affected by scatter X-rays and characteristic X-rays. From DE MTF analysis with increasing the t Sn in Fig. As wf increased, the DE MTF was decreased. The DE MTF was largely dependent on wf used for reconstruction as shown in Fig. 4D, the SDNR performance of ACNR was superior when ξ was less than 0.3, whereas the SDNR performance of MNR and GNR was superior when ξ was greater than 0.4.įig. Particularly, reflecting the results of Fig. All the NR filtering suppressed image noise, and improved SDNR performance. Without NR filtering, ξ=0.3 represented the optimal ξ. 4F shows the SDNR performance for ξ used in DE reconstruction. The highest ACNR performance result supports the results of reducing the size of random noise, such as yellow profiles in columns 1 and 4 of Figs. The results are similar to those reported by Warp and Dobbins, who demonstrated a reduction in the noise component at various spatial frequencies for these, and other noise reduction algorithms. The SDNR performance of the ACNR increased the most for t Sn, as shown in Fig. A larger energy separation between the two energies with t Sn enhanced the SDNR, and the performances of GNR and MNR had similar performance with increasing t Sn. ![]() ![]() However, the noise performance of ACNR was superior when ξ was less than 0.3, while the noise performance of MNR and GNR was superior with the fine difference, when ξ was greater than 0.4, as shown in Fig. 3C, the ACNR showed a better noise performance of artificial-nodule-enhanced DE images than the MNR and GNR as a function of the t Sn. Hence, NR filtering maintained the SD value almost constant. The effects of t Sn and ξ on SD performance were nearly negligible, as shown in Fig. 4 shows signal difference (SD), noise, and SDNR calculated from the artificial-nodule-enhanced DE images for various combinations of t Sn and ξ. |
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