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Show new changes starting from 21:54, 18 October 2024
 
 
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17 October 2024

N    16:09  Category:X-LINUX-RT archives diffhist +265 Registered User (Created page with "This category groups together all articles related to the former versions of X-LINUX-RT Expansion Package for the STM32 Arm® Cortex® MPUs ecosystems. The last version of X-LINUX-RT Expansion package is available in this article :X-LINUX-RT expansion package") Tag: 2017 source edit
     11:39  X-LINUX-RT expansion package - v5.1.0 diffhist +40 Registered User Tag: 2017 source edit
N    10:54  X-LINUX-RT expansion package diffhist +608 Registered User (Created page with "<noinclude>{{ApplicableFor |MPUs list=STM32MP13x, STM32MP15x, STM32MP25x |MPUs checklist=STM32MP13x,STM32MP15x, STM32MP25x }}</noinclude> {{:X-LINUX-RT_expansion_package_-_v5.1.0}} ==Archives 25px|link=== {| class="st-table" ! X-LINUX-RT release !! Release note |- | X-LINUX-RT v5.0.0 ||{{EcosystemFlow/Archives | custom=Basic | flow=v5 | page=X-LINUX-RT_expansion_package_-_v5.0.0}} |} <br> ==References== <noinclude> {{PublicationRequestId | 26...") Tag: 2017 source edit
N    10:44  X-LINUX-RT expansion package - v5.0.0‎‎ 3 changes history +9,882 [Registered User‎ (3×)]
     
10:44 (cur | prev) −10,138 Registered User Tag: 2017 source edit
     
10:39 (cur | prev) +1 Registered User (Page created from X-LINUX-RT_expansion_package) Tag: 2017 source edit
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10:37 (cur | prev) +20,019 Registered User (Created page with "<noinclude>{{ApplicableFor |MPUs list=STM32MP13x, STM32MP15x, STM32MP25x |MPUs checklist=STM32MP13x,STM32MP15x, STM32MP25x }}</noinclude> ==Article purpose== Purpose of this article is to: * introduce the X-LINUX-RT expansion package * define the hardware & software deliverables to use the X-LINUX-RT package, * describe all steps to integrate the X-LINUX-RT package and associated expected results * give some use case examples enabled by the X-LINUX-RT package <div clas...")

16 October 2024

N    15:23  X-LINUX-Azure Expansion Package diffhist +528 Registered User (Created page with "This category groups together all articles related to software expansion package about '''Microsoft<sup>®</sup> Azure<sup>®</sup> IoT Edge'''<ref>[https://azure.microsoft.com/en-us/products/iot-edge// Microsoft<sup>®</sup> Azure<sup>®</sup> IoT Edge]</ref> software. It is recommended to first read the X-LINUX-AZURE Expansion Package article. <noinclude> Category:Software expansion packages {{PublicationRequestId | Auto | 2024-05-13 | Automatic approval bas...") Tag: 2017 source edit

14 October 2024

N    16:18  X-LINUX-QT Qt6.5.3 - 3rdParties licenses diffhist +7,347 Registered User (New license file for Qt6 v6.5.3) Tag: 2017 source edit
N    16:15  LegalInformation:X-LINUX-QT licenses - v2.0.0 diffhist +32,984 Registered User (Add license page for the new release 2.0.0) Tag: 2017 source edit
     12:59  STM32MP25 V4L2 camera overview diffhist −511 Registered User (→‎Grab a raw-bayer frame: remove conversion + display because GStreamer not able to handle raw 10 bits...) Tag: 2017 source edit

10 October 2024

     17:41  STM32MP25 V4L2 camera overview‎‎ 2 changes history +672 [Registered User‎ (2×)]
     
17:41 (cur | prev) 0 Registered User (Fixes main => dump_capture_dev) Tag: 2017 source edit
     
17:31 (cur | prev) +672 Registered User (de-hardcode /dev/video2 + remove space after {{Board}}) Tag: 2017 source edit
N    17:16  MB1605 diffhist +4,645 Registered User (Created page with "<noinclude>{{ApplicableFor |MPUs list=STM32MP25x |MPUs checklist=STM32MP13x,STM32MP15x, STM32MP25x }} MB1936 board overview. == Board overview == </noinclude> '''Main board MB1936''', revision C-01: part of the {{Board | type=257x-EV1}}. {| class="st-table" |+ ! Position !! Description !! Position !! Description |- | 1 (<span id{{=}}"MB1936-LED1">'''LED1'''</span>) || User LED (blue) <ref group="*" name="User LEDs">LD2, LD3, LD4, LD5 (MB1936): some user LEDs are used t...")

9 October 2024

N    09:35  STM32MP257x-DKx - board connections diffhist +1,621 Registered User (Created page with "<noinclude> This article presents the recommended connections to start with the '''STM32MP157x-DKx''' Discovery kits. It is valid for the {{Board | type=157A-DK1 | name=short}}, {{Board | type=157D-DK1 | name=short}}, {{Board | type=157C-DK2 | name=short}} and {{Board | type=157F-DK2 | name=short}} Discovery kits: the part numbers are specified in the STM32MP15 microprocessor part numbers article. {{Warning|To success...")
N    09:23  STM32MP257x-DKx - board assembly diffhist +2,199 Registered User (Created page with "<noinclude> This article explains how to assemble the '''STM32MP157x-DKx''' Discovery kits. It is valid for the {{Board | type=157A-DK1 | name=short}}, {{Board | type=157D-DK1 | name=short}}, {{Board | type=157C-DK2 | name=short}} and {{Board | type=157F-DK2 | name=short}} Discovery kits: the part numbers are specified in the STM32MP15 microprocessor part numbers article. {{Warning|To successfully start the board, it...")

27 September 2024

     15:56  Simple ISP preview diffhist +3,360 Registered User (First draft of Simple ISP Preview article) Tag: 2017 source edit
     14:12  System build troubleshooting grid diffhist +25 Registered User

25 September 2024

     17:19  Example of directory structure for Packages diffhist +25 Registered User

23 September 2024

N    10:44  How to retrain a NN model using ONNXRuntime on STM32MP2x diffhist +678 Registered User (Created page with "{{ApplicableFor |MPUs list=STM32MP13x, STM32MP15x, STM32MP25x |MPUs checklist=STM32MP13x, STM32MP15x, STM32MP25x }} <noinclude></noinclude> == Article purpose == This article describes how to run a simple training session on the STM32MP2x using the ONNXRuntime™ training package. It is an example based on an image classification application which aims to retrain a {{Highlight|mobilenetV2}} model in floating point format on the Cortex-A of the STM32MP2x. {{Information...") Tag: 2017 source edit

20 September 2024

N    14:39  On-device learning overview diffhist +1,081 Registered User (Created page with "== What is On-device Learning ? == On-device training refers to the process of training a machine learning model directly on an edge device where the data is being collected without relying on cloud services or external servers. This is in contrast to training a model on a server or a cloud. This approach is becoming increasingly popular and has several advantages: * '''Enhancing data confidentiality''' and personal privacy protection for the sensitive data that cannot...") Tag: 2017 source edit