Building a Distribution: Openharmony and Openmandriva
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Plasma on Mobile Devices
Plasma on Mobile devices Application Ecosystem MC Bhushan Shah KDE Developer Agenda ● Initial development of the Plasma Mobile ● Basic architecture details ● Advantages to KDE community ● Application ecosystem and development ● Future for Plasma Mobile ● Challenges Introduction ● KDE developer and sysadmin ● Plasma Mobile maintainer and lead developer ● Employed by Bluesystems GmbH ● From Vadodara, India KDE ● Previously known as the K Desktop Environment ● Now community, which creates free software for end users ● Several products including Plasma, KDE Frameworks, KDE applications. Plasma Mobile ● Announced in the July 2015 ● Vision of providing completely free and open-source mobile platform which respects user’s privacy and freedom. ● Initial prototype on the LG Nexus 5. Initial Development ● LGE Nexus 5 as reference device ● Ubuntu Touch 15.04 (vivid) as base system ● Makes use of the Android binary blobs / drivers ● Can also run on the desktop system for development Basic architecture details ● KWin wayland as compositor ● DRM/GBM or hwcomposer backends ● plasmashell and mobile shell package ● QtQuickControls2 and Kirigami for application development Advantages to KDE community ● Several performance improvements ● Better touch input support in applications and shell ● Improvements in Wayland support ● More modular and re-usable user interfaces Application ecosystem and development ● QtQuickControls2 and Kirigami as toolkit ● CMake/QMake as a buildsystem ● Various bundle formats as well as native distribution packaging for the distribution -
User Manual - S.USV Solutions Compatible with Raspberry Pi, up Board and Tinker Board Revision 2.2 | Date 07.06.2018
User Manual - S.USV solutions Compatible with Raspberry Pi, UP Board and Tinker Board Revision 2.2 | Date 07.06.2018 User Manual - S.USV solutions / Revision 2.0 Table of Contents 1 Functions .............................................................................................................................................. 3 2 Technical Specification ........................................................................................................................ 4 2.1 Overview ....................................................................................................................................... 5 2.2 Performance .................................................................................................................................. 6 2.3 Lighting Indicators ......................................................................................................................... 6 3 Installation Guide................................................................................................................................. 7 3.1 Hardware ...................................................................................................................................... 7 3.1.1 Commissioning S.USV ............................................................................................................ 7 3.1.2 Connecting the battery .......................................................................................................... 8 3.1.3 Connecting the external power supply ................................................................................. -
A Low-Cost Deep Neural Network-Based Autonomous Car
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car Michael G. Bechtely, Elise McEllhineyy, Minje Kim?, Heechul Yuny y University of Kansas, USA. fmbechtel, elisemmc, [email protected] ? Indiana University, USA. [email protected] Abstract—We present DeepPicar, a low-cost deep neural net- task may be directly linked to the safety of the vehicle. This work based autonomous car platform. DeepPicar is a small scale requires a high computing capacity as well as the means to replication of a real self-driving car called DAVE-2 by NVIDIA. guaranteeing the timings. On the other hand, the computing DAVE-2 uses a deep convolutional neural network (CNN), which takes images from a front-facing camera as input and produces hardware platform must also satisfy cost, size, weight, and car steering angles as output. DeepPicar uses the same net- power constraints, which require a highly efficient computing work architecture—9 layers, 27 million connections and 250K platform. These two conflicting requirements complicate the parameters—and can drive itself in real-time using a web camera platform selection process as observed in [25]. and a Raspberry Pi 3 quad-core platform. Using DeepPicar, we To understand what kind of computing hardware is needed analyze the Pi 3’s computing capabilities to support end-to-end deep learning based real-time control of autonomous vehicles. for AI workloads, we need a testbed and realistic workloads. We also systematically compare other contemporary embedded While using a real car-based testbed would be most ideal, it computing platforms using the DeepPicar’s CNN-based real-time is not only highly expensive, but also poses serious safety control workload. -
Pinephone User Manual - Quick Start Guide (En)
PINEPHONE USER MANUAL - QUICK START GUIDE (EN) 1 Package contents ● User Manual - Quick Start Guide (x1) ● PinePhone (x1) ● USB-C power cable (x1) 2 Safety precautions and recycling 2.1 Cautions Before using the device please read this manual carefully. Notes for safe operation: ● The PinePhone should be charged using a 15W (5V 3A) USB-PD power adapter. Charging at a higher voltage may result in damage to the device. ● The PinePhone will only operate when its internal temperature is between 5°C and 65°C. It should never be operated with an external temperature lower than -20°C or higher than 40°C. ● Do not puncture, disassemble, strike or squeeze the battery. Old batteries need to be disposed of in accordance with local regulations (see section 2.2). ● Do not expose the device to direct sunlight, water or high levels of humidity. ● In the event of overheating, power off the PinePhone and let it cool for 15 minutes. ● Comply with local regulation pertaining to using mobile devices. This extends to and includes use of the device in public spaces, when operating motor vehicles and heavy machinery. 2.2 Recycling of components and batteries Recycling any PinePhone components should be done according to local regulation. This may require you to dispose of the phone or its parts at a local recycling centre or at a designated container. Please consult local legislation for details. Batteries should never, under any circumstances, be disposed of with general household waste. The end user is legally obliged to return used batteries. Batteries can be returned to us to be disposed of. -
D I E B Ü C H S E D E R P a N D O R a Seite 1
D i e B ü c h s e d e r P a n d o r a Infos zur griechischen Mythologie siehe: http://de.wikipedia.org/wiki/B%C3%BCchse_der_Pandora ______________________________________________________________________________________________________________________________________________________ „Das Öffnen der Büchse ist absolute Pflicht, um die Hoffnung in die Welt zu lassen!“ Nachdruck und/oder Vervielfältigungen, dieser Dokumentation ist ausdrücklich gestattet. Quellenangabe: Der Tux mit dem Notebook stammt von der Webseite: http://www.openclipart.org/detail/60343 1. Auflage , © 9-2012 by Dieter Broichhagen | Humboldtstraße 106 | 51145 Köln. e-Mail: [email protected] | Web: http://www.broichhagen.de | http://www.illugen-collasionen.com Satz, Layout, Abbildungen (wenn nichts anderes angegeben ist): Dieter Broichhagen Alle Rechte dieser Beschreibung liegen beim Autor Gesamtherstellung: Dieter Broichhagen Irrtum und Änderungen vorbehalten! Seite 1 - 12 D i e B ü c h s e d e r P a n d o r a Infos zur griechischen Mythologie siehe: http://de.wikipedia.org/wiki/B%C3%BCchse_der_Pandora ______________________________________________________________________________________________________________________________________________________ Hinweis: Weitere Abbildungen von meiner „Büchse der Pandora“ finden Sie unter http://broichhagen.bplaced.net/ilco3/images/Pandora-X1.pdf Bezeichnungen • SheevaPlug ist kein Katzenfutter und kein Ausschlag • DreamPlug ist kein Auslöser für Alpträume • Raspberry Pi ist kein Himbeer-Kuchen • Pandaboard ist kein Servbrett für Pandabären • Die „Büchse der Pandora“ ist keine Legende Was haben SheevaPlug, DreamPlug, Raspberry Pi und Pandaboard gemeinsam? Sie haben einen ARM und sind allesamt Minicomputer. Wer einen ARM hat, ist nicht arm dran. Spaß bei Seite oder vielmehr, jetzt geht der Spaß erst richtig los. Am Anfang war der ... SheevaPlug Das Interesse wurde durch Matthias Fröhlich geweckt, der einen SheevaPlug- Minicomputer vor 2 Jahren im Linux- Workshop vorstellte. -
Latest Kali Linux Version Download Kali Linux 2021.2 Released – Download DVD ISO Images
latest kali linux version download Kali Linux 2021.2 Released – Download DVD ISO Images. Kali Linux 2021.2 is the latest Kali rolling release that arrived a few days ago with Linux Kernel 5.10 LTS, default Xfce 4.16.2 desktop, new important tools, and tons of other enhancements. New Features in Kali Linux 2021.2. Let’s discuss some core important features of the Kali Linux 2021.2 release. Kaboxer to Package App In Container. Kali 2021.2 is the first version to feature a home-baked new Kali Applications Boxer v1.0 , in short Kaboxer . Packaging programs becomes very difficult when a program has complex dependencies or legacy libraries that are no longer available in the host system. Here comes Kaboxer , a Linux application that aims to help you package up programs properly using container technology. Kali has also shipped three new applications packaged using Kaboxer tools such as Covenant framework , Firefox (Developer Edition) web browser, and Zenmap official graphical user interface (GUI) for the Nmap Security Scanner. If you want to know more about Kaboxer , read its introduction blog post or documentation to learn packaging applications with Kaboxer . Kali-Tweaks to Easily Configure OS. Next, Kali Linux 2020.2 brings another brand new app called Kali-Tweaks v1.0 . This app aims to help you customize Kali Linux easily as per your need. As of now, you can use Kali-Tweaks for the following purposes: Installing or removing group of tools. Configuring network repositories for APT sources i.e. “ bleeding-edge ” and “ experimental ” branches. Managing the shell and command prompt. -
End-To-End Learning for Autonomous Driving Robots
End-to-End Learning for Autonomous Driving Robots Anthony Ryan 21713293 Supervisor: Professor Thomas Bräunl GENG5511/GENG5512 Engineering Research Project Submitted: 25th May 2020 Word Count: 8078 Faculty of Engineering and Mathematical Sciences School of Electrical, Electronic and Computer Engineering 1 Abstract This thesis presents the development of a high-speed, low cost, end-to-end deep learning based autonomous robot driving system called ModelCar-2. This project builds on a previous project undertaken at the University of Western Australia called ModelCar, where a robotics driving system was first developed with a single LIDAR scanner as its only sensory input as a baseline for future research into autonomous vehicle capabilities with lower cost sensors. ModelCar-2 is comprised of a Traxxas Stampede RC Car, Wide-Angle Camera, Raspberry Pi 4 running UWA’s RoBIOS software and a Hokuyo URG-04LX LIDAR Scanner. ModelCar-2 aims to demonstrate how the cost of producing autonomous driving robots can be reduced by replacing expensive sensors such as LIDAR with digital cameras and combining them with end-to-end deep learning methods and Convolutional Neural Networks to achieve the same level of autonomy and performance. ModelCar-2 is a small-scale application of PilotNet, developed by NVIDIA and used in the DAVE-2 system. ModelCar-2 uses TensorFlow and Keras to recreate the same neural network architecture as PilotNet which features 9 layers, 27,000,000 connections and 252,230 parameters. The Convolutional Neural Network was trained to map the raw pixels from images taken with a single inexpensive front-facing camera to predict the speed and steering angle commands to control the servo and drive the robot. -
Raspberry Pi Market Research
Raspberry Pi Market Research Contents MARKET ................................................................................................................................................... 3 CONSUMERS ............................................................................................................................................ 8 COMPETITORS ....................................................................................................................................... 12 Element14 ......................................................................................................................................... 12 Gumstix- Geppetto Design ............................................................................................................... 14 Display Module .................................................................................................................................. 17 CoMo Booster For Raspberry Pi Compute Module (Geekroo Technologies ) ................................... 18 2 MARKET When the first Raspberry PI (Pi) was released in February 2012 it made a big impact that extended well beyond the education world for which it was touted. The Pi became a staple amongst the hobbyist and professional maker communities and was used for building everything from media centers, home automation systems, remote sensing devices and forming the brains of home made robots. It has recently been announced that over 5m Raspberry Pi’s have been sold since its inception, making it the best selling -
Gumstix Edge AI Series for NVIDIA Jetson Nano
Gumstix Edge AI Series for NVIDIA Jetson Nano TensorFlow pre-integration for machine learning and neural networking development. ® Fremont, CA. March 31, 2020— Gumstix , Inc., a leader in computing hardware for intelligent embedded applications, announced the release of four Edge AI devices designed to meet the demands of machine-learning applications moving massive data from the networks edge. Powered by the NVIDIA Jetson Nano running a Quad-core ARM A57, the Gumstix AI development boards feature built in TensorFlow support to make edge AI prototyping more accessible for software engineers and provide a rapid turnkey solution for deployment. Gumstix AI devices streamline the software-hardware integration and provide users the following benefits: ● Easy to customize modular designs available in the Geppetto app. ● GPU enabled TensorFlow support. ● Customizable OS (Yocto) for rapid and simple deployment. “Ever since launching our first products in 2003- running a full Linux implementation in the size of a stick of gum - we have sought to make the world of tiny devices more and more powerful.” says W.Gordon Kruberg, M.D., Head of Modular Hardware, Altium, Inc., “This new AI series of expansion boards, featuring the SnapShot, and our support of the NVIDIA Nano, grows from our belief in the power and primacy of neural-network algorithms.” The Gumstix Snapshot with up to 16 video streams along with the 3 other new boards, are now available for purchase at www.gumstix.com. In addition, users are able to also easily customize the form, fit and function of these modular designs online and receive a customized board in 15 days. -
最近の事と来年の目標 in 2020 1 Recently My Activity and the Next Year’S Goal in 2020
最近の事と来年の目標 in 2020 1 Recently my activity and the next year’s goal in 2020 FZ-G1+Lubuntu20.04 in128GB USB memory 1、 自己紹介 Self introduction 2、Recently my activities 3、Non eMMC Windows Tablet? 4、Install Linux in The third Mobile OSs 5、Install Linux in UMPC 6、Install Linux in Macbook,PowerbookG4 7、USB Wifi and USB HDMI capture 8、Recently my activity 詳しい話はSlideshareで公開中 This Presentation: @kapper1224 Slideshare & PDF files publication of my HP http://kapper1224.sakura.ne.jp 鹿児島らぐ 2020年12月 Gadget Hacking 2020 年12月26日 14:00~ User Group Place: Online Speaker:Kapper 自己紹介 Self Introduction 2 ● My name: Kapper ● Twitter account:@kapper1224 ● HP:http://kapper1224.sakura.ne.jp ● Slideshare: http://www.slideshare.net/kapper1224 ● Mastodon:https://pawoo.net/@kapper1224/ ● Facebook:https://www.facebook.com/kapper1224/ ● My Hobby:Linux、*BSD、and Mobile Devices ● My favorite words:The records are the more important than the experiment. ● Test Model:Netwalker(PC-Z1,T1)、Nokia N900、DynabookAZ、RaspberryPi Nexus7(2012、2013)、Nexus5、Chromebook、Fx0(FirefoxOS)、 無敵CD-920、CD-928,GPD-WIN、GPD-Pocket、Macbook NANOTE、Windows Tablet、SailfishOS、UBPorts、postmarketOS ● Recent my Activity: Hacking Linux on Windows10 Tablet (Intel Atom) and Android Smartphone. Hacking NetBSD and OpenBSD on UEFI and Windows Tablet. I have been exhibiting in NT Nagoya, NT Kanazawa, Oogaki Mini MakerFaire. I have over 200 Windows Tablet and 120 ARM Android, and test it now. 後、最近小説家になろうで異世界で製造業と産業革命の小説書いていますなう。 Recently my activities 3 NetBSD、OpenBSD、FreeBSD SailfishOS on UBPorts on Nexus5 MaruOS Some Linux on Windows Tablet Nexus5 Nexus7 2013 on Nexus5X postmarketOS on Install Linux and *BSD Nexus5, Nexus7 2012 In USB memory Activities on NT, MakerFaire,Taiwan 異世界転生小説を書いています。 4 This is my novels 「The otherworldy 」 ● 中世〜近世で製造業で産業革命するネタ。 It is 「Industrial Revolutions」 in the early modern period in my novels. -
2014-Kein Titel-Gerrit Grunwald
SENSOR NETWORKS JAVA RASPBERRYEMBEDDED PI ABOUTME. CANOOENGINEERING Gerrit Grunwald | Developer Leader of JUG Münster JavaFX & IoT community Co-Lead Java Champion, JavaOne RockStar JAVA ONE 2013 canoo canoo canoo canoo MONITORING canoo IDEAS canoo ๏ Measure multiple locations ๏ Store data for analysis ๏ Access it from everywhere ๏ Use Java where possible ๏ Visualize the data on Pi canoo ๏ Create mobile clients ๏ Display current weather condition ๏ Measure local environmental data ๏ Use only embedded devices canoo QUESTIONS ???canoo ๏ How to do the measurement ? ๏ Where to store the data ? ๏ Which technologies to use ? ๏ What is affordable ? ๏ Which hardware to use ? canoo TODO canoo 1 2 MONITOR STORE DATA 9 ROOMS 3 4 VISUALIZE ON OTHER RASPBERRY PI CLIENTS canoo MONITOR 9 ROOMS canoo BUT HOW canoo SENSOR NETWORK canoo SENSOR NETWORK ๏ Built of sensor nodes ๏ Star or mesh topology ๏ Pass data to coordinator ๏ Sensor nodes running on batteries canoo POSSIBLE SENSOR NODES canoo RASPBERRY PI canoo RASPBERRY PI ๏ Cheaper ๏ Power supply ๏ Support ๏ Size ๏ Flexible ๏ Overkill ๏ Connectivity ๏ limited I/O's canoo ARDUINO YUN canoo ARDUINO YUN ๏ Cheaper ๏ Power supply ๏ Support ๏ Oversized ๏ Flexible ๏ I/O's ๏ No Java ๏ Connectivity canoo ARDUINO + XBEE canoo ARDUINO + XBEE ๏ Cheap ๏ Power supply ๏ Support ๏ Oversized ๏ Flexible ๏ I/O's ๏ No Java ๏ Connectivity canoo XBEE canoo XBEE ๏ Cheap ๏ No Java ๏ Support ๏ Flexible canoo XBEE canoo XBEE ๏ 2.4 GHz at 2mW ๏ Indoor range up to 40m ๏ Outdoor range up to 120m ๏ 2.8 - 3.6 Volt ๏ -40 - 85°C operat. -
Genode Operating System Framework Platforms
GENODE Operating System Framework 21.05 Platforms Norman Feske Contents Contents 1 Introduction3 2 Porting Genode to a new SoC4 2.1 Preparatory steps................................9 2.1.1 Licensing considerations........................9 2.1.2 Selecting a suitable SoC........................ 10 2.1.3 Start by taking the known-good path................ 11 2.1.4 Setting up an efficient development workflow........... 12 2.2 Getting acquainted with the target platform................. 14 2.2.1 Getting a first impression....................... 15 2.2.2 The U-Boot boot loader........................ 19 2.3 Bare-metal serial output............................ 24 2.4 Kernel skeleton................................. 34 2.4.1 A tour through the code base..................... 34 2.4.2 A new home for the board support.................. 41 2.4.3 Getting to grips using meaningful numbers............. 48 2.4.4 A first life sign of the kernel...................... 55 2.5 Low-level debugging.............................. 57 2.5.1 Option 1: Walking the source code.................. 58 2.5.2 Option 2: One step of ground truth at a time............ 60 2.5.3 Option 3: Backtraces.......................... 62 2.6 Excursion to the user land........................... 64 2.7 Device access from the user level....................... 73 2.7.1 Using a GPIO pin for sensing a digital signal............ 74 2.7.2 Driving an LED via a GPIO pin.................... 81 2.7.3 Responding to device interrupts................... 84 2.8 One Platform driver to rule them all..................... 90 2.8.1 Platform driver............................. 90 2.8.2 Session interfaces for accessing pins................. 95 2.8.3 PIO device driver............................ 96 2.8.4 Dynamic configuration testing...................