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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 ................................................................................. -
Performance Analysis and Optimization Opportunities for NVIDIA Automotive Gpus
Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs Hamid Tabani∗, Fabio Mazzocchetti∗y, Pedro Benedicte∗y, Jaume Abella∗ and Francisco J. Cazorla∗ ∗ Barcelona Supercomputing Center y Universitat Politecnica` de Catalunya Abstract—Advanced Driver Assistance Systems (ADAS) and products such as Renesas R-Car H3 [1], NVIDIA Jetson Autonomous Driving (AD) bring unprecedented performance TX2 [20] and NVIDIA Jetson Xavier [35], [27] have already requirements for automotive systems. Graphic Processing Unit reached the market building upon GPU technology inherited (GPU) based platforms have been deployed with the aim of meeting these requirements, being NVIDIA Jetson TX2 and from the high-performance domain. Automotive GPUs have its high-performance successor, NVIDIA AGX Xavier, relevant inherited designs devised for the high-performance domain representatives. However, to what extent high-performance GPU with the aim of reducing costs in the design, verification and configurations are appropriate for ADAS and AD workloads validation process for chip manufacturers. remains as an open question. Unfortunately, reusability of high-performance hardware This paper analyzes this concern and provides valuable does not consider GPUs efficiency in the automotive domain insights on this question by modeling two recent automotive NVIDIA GPU-based platforms, namely TX2 and AGX Xavier. In and, to the best of our knowledge, the design space for GPUs, particular, our work assesses their microarchitectural parameters where resources are sized with the aim of optimizing specific against relevant benchmarks, identifying GPU setups delivering goals such as performance, has not been yet thoroughly increased performance within a similar cost envelope, or decreas- performed for the automotive domain. ing hardware costs while preserving original performance levels. -
Building a Distribution: Openharmony and Openmandriva
Two different approaches to building a distribution: OpenHarmony and OpenMandriva Bernhard "bero" Rosenkränzer <[email protected]> FOSDEM 2021 -- February 6, 2021 1 MY CONTACT: [email protected], [email protected] Way more important than that, he also feeds LINKEDIN: dogs. https://www.linkedin.com/in/berolinux/ Bernhard "bero" Rosenkränzer I don't usually do "About Me", but since it may be relevant to the topic: Principal Technologist at Open Source Technology Center since November 2020 President of the OpenMandriva Association, Contributor since 2012 - also a contributor to Mandrake back in 1998/1999 2 What is OpenHarmony? ● More than an operating system: Can use multiple different kernels (Linux, Zephyr, ...) ● Key goal: autonomous, cooperative devices -- multiple devices form a distributed virtual bus and can share resources ● Initial target devices: Avenger 96 (32-bit ARMv7 Cortex-A7+-M4), Nitrogen 96 (Cortex-M4) ● Built with OpenEmbedded/Yocto - one command builds the entire OS ● Fully open, developed as an Open Source project instead of an inhouse product from the start. ● For more information, visit Stefan Schmidt's talk in the Embedded devroom, 17.30 and/or talk to us at the Huawei OSTC stand. 3 What is OpenMandriva? ● A more traditional Linux distribution - controlled by the community, continuing where Mandriva left off after the company behind it went out of business in 2012. Its roots go back to the first Mandrake Linux release in 1998. ● Originally targeting only x86 PCs - Support for additional architectures (aarch64, armv7hnl, RISC-V) added later ● Repositiories contain 17618 packages, built and updated individually, assembled into an installable product with omdv-build-iso or os-image-builder. -
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. -
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. -
Procurement of Electric-Electronic
REQUEST FOR QUOTATION (RFQ) Procurement of Electric-Electronic Laboratory Equipment and Supplies for Adana and Mersin Innovation Centers Adana ve Mersin Yenilik Merkezleri için Elektrik-Elektronik Laboratuvarları Ekipman ve Sarf Malzemeleri Alım İhalesi Please enter Name, Address & Contact Details of your Firm DATE: February 10, 2020 Lütfen Firma Adı, Adresi ve İrtibat Bilgilerini buraya giriniz TARİH: 10 Şubat 2020 REFERENCE: UNDP-TUR-RFQ(MC1)-2020/07 Dear Sir / Madam: Sayın İlgili: We kindly request you to submit your Teklife Davet Ek 1’deki Teknik Şartnamede ve Ek quotation for “Procurement of Electric-Electronic 2’de yer alan Malzeme Listesi’nde detayları verilmiş Laboratory Equipment and Supplies for Adana and olan “Adana ve Mersin Yenilik Merkezleri için Mersin Innovation Centers”, as detailed in Technical Elektrik-Elektronik Laboratuvarları Ekipman ve Sarf Specifications and Supplies List provided as Annex Malzemeleri Alım İhalesi” için teklifinizi sunmanızı 1 and Annex 2 (respectively) of this RFQ. When rica ederiz. preparing your quotation, please be guided by the forms attached hereto as ANNEX 1, ANNEX 2 and Teklifinizi lütfen EK 1, EK 2 ve EK 3’de sunulmuş ANNEX 3. formların rehberliğinde hazırlayınız. Quotations shall be submitted on or before 27 Teklifler aşağıda belirtilen adrese, 27 Şubat 2020, February 2020, 14:00 hrs. Turkey Local Time, via Türkiye saati ile 14:00’a kadar elden, e-posta veya hand delivery, e-mail or courier mail to the address kargo yolu ile teslim edilmelidir: below: United Nations Development Programme Birleşmiş Milletler Kalkınma Programı (UNDP) (UNDP) Yıldız Kule, Yukarı Dikmen Mahallesi, Turan Güneş Yıldız Kule, Yukarı Dikmen Mahallesi, Turan Bulvarı, No:106, 06550, Çankaya, Ankara/Türkiye Güneş Bulvarı, No:106, 06550, Çankaya, İlgili Kişi: Ümit ALSAÇ Ankara/Turkey Satın Alma Yetkilisi, UNDP Attn: Mr. -
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. -
NVIDIA Jetson TX2 Module
® ™ NVIDIA JETSON TX2 SUPERCOMPUTER ON A MODULE FOR AI AT THE EDGE TAKE REAL-TIME AI PERFORMANCE FARTHER WITH THE HIGH-PERFORMANCE, LOW-POWER JETSON TX2. The most innovative technology for AI computing and visual computing comes in a supercomputer the size of a credit card. Its small form factor and power envelope make the Jetson TX2 module ideal for intelligent edge devices like robots, drones, smart cameras, and portable medical devices. CONTENTS Jetson TX2 features a variety of standard hardware interfaces that make it easy to integrate it into a wide range of products and form factors. Plus, it comes with the complete Jetpack > NVIDIA Jetson TX2 > Attached Thermal Transfer Plate (TTP) SDK, which includes the BSP, libraries for deep learning, computer vision, GPU computing, multimedia processing, and much more to accelerate your software development. And it’s TECHNICAL SPECIFICATIONS supported by the Jetson developer site, which includes documentation, tutorials, and an FEATURES JETSON TX2 ecosystem of partners and developers. It’s never been easier to get started with AI. Graphics NVIDIA Pascal™, 256 NVIDIA For detailed specifications, design guides, Jetpack, and everything else you need to develop CUDA® cores with Jetson, go to developer.nvidia.com/embedded-computing. CPU HMP Dual Denver 2/2MB L2 + Quad ARM® A57/2MB L2 KEY FEATURES Video 4K x 2K 60 Hz Encode (HEVC) 4K x 2K 60 Hz Decode (12-bit Jetson TX2 Module I/O support) > NVIDIA Pascal™ architecture GPU > USB 3.0 Type A Memory 8 GB 128-bit LPDDR4 > Dual-core Denver 2 64-bit CPU -
Jetson TX2 Developer Kit User Guide
JETSON TX2 DEVELOPER KIT DA_09452-005 | December 17, 2019 ````` User Guide DOCUMENT CHANGE HISTORY DA_09452-005 Version Date Authors Description of Change Revision for Jetson Linux Driver 2.0 March 28, 2019 jsachs, plawrence Package r32.1. Revision for SDK Manager and Jetson 3.0 July 8, 2019 jsachs, plawrence Linux Driver Package r32.2. Revision for Jetson Linux Driver 4.0 December 17, 2019 ssheshadri Package r32.3.1. NOTE Welcome to the NVIDIA Jetson platform! There two key things you should do right away: 1. Sign up for the NVIDIA Developer Program – this enables you to ask questions and contribute on the NVIDIA Jetson Forums, gives access to all documentation and collateral on the Jetson Download Center, and more. 2. Read this User Guide! After that, check out these important links: • Jetson FAQ – Please read the FAQ. • Support Resources – This web page links to important resources, including the Jetson Forum and the Jetson Ecosystem page. • Jetson Linux Driver Package Release Notes – Jetson Linux Driver Package (L4T) is a key component of the Jetson platform, and provides the sample filesystem for your developer kit. Please read the latest release notes. • Thanks, The NVIDIA Jetson team Jetson TX2 Developer Kit DA_09452-005 | ii TABLE OF CONTENTS Note .........................................................................................ii Jetson TX2 Developer Kit ............................................................... 1 Included in the box ............................................................................. 1 Developer -
Debian GNU/Linux Installation Guide
Debian GNU/Linux Installation Guide July 31, 2021 Debian GNU/Linux Installation Guide Copyright © 2004 – 2021 the Debian Installer team This manual is free software; you may redistribute it and/or modify it under the terms of the GNU General Public License. Please refer to the license in Appendix F. Build version of this manual: 20210730. i Contents 1 Welcome to Debian 1 1.1 What is Debian? . 1 1.2 What is GNU/Linux? . 1 1.3 What is Debian GNU/Linux? . 2 1.4 What is the Debian Installer? . 3 1.5 Getting Debian . 3 1.6 Getting the Newest Version of This Document . 3 1.7 Organization of This Document . 3 1.8 About Copyrights and Software Licenses . 4 2 System Requirements 5 2.1 Supported Hardware . 5 2.1.1 Supported Architectures . 5 2.1.2 Three different ARM ports . 6 2.1.3 Variations in ARM CPU designs and support complexity . 6 2.1.4 Platforms supported by Debian/armhf . 6 2.1.5 Platforms no longer supported by Debian/armhf . 8 2.1.6 Multiple Processors . 8 2.1.7 Graphics Hardware Support . 8 2.1.8 Network Connectivity Hardware . 8 2.1.9 Peripherals and Other Hardware . 8 2.2 Devices Requiring Firmware . 8 2.3 Purchasing Hardware Specifically for GNU/Linux . 9 2.3.1 Avoid Proprietary or Closed Hardware . 9 2.4 Installation Media . 9 2.4.1 CD-ROM/DVD-ROM/BD-ROM . 9 2.4.2 Network . 10 2.4.3 Hard Disk . 10 2.4.4 Un*x or GNU system . -
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 -
Raspberry Pi Computer Vision Programming – Second Edition Programming – Second Edition
Raspberry Pi Computer Vision Pajankar Ashwin Edition Second – Programming Vision Computer Pi Raspberry Programming – Second Edition Raspberry Pi is one of the popular on Raspberry Pi, before covering major Raspberry Pi single-board computers of our generation. techniques and algorithms in image All the major image processing and computer processing, manipulation, and computer vision algorithms and operations can be vision. By sequentially working through the implemented easily with OpenCV on steps in each chapter, you'll understand Computer Vision Raspberry Pi. This updated second edition is essential OpenCV features. Later sections packed with cutting-edge examples and new will take you through creating graphical user topics, and covers the latest versions of key interface (GUI) apps with GPIO and OpenCV. technologies such as Python 3, Raspberry Pi, You'll also learn how to use the new computer Programming and OpenCV. This book will equip you with vision library, Mahotas, to perform various the skills required to successfully design and image processing operations. Finally, you'll implement your own OpenCV, Raspberry Pi, explore the Jupyter notebook and how to and Python-based computer vision projects. set up a Windows computer and Ubuntu for computer vision. Second Edition At the start, you'll learn the basics of Python 3, and the fundamentals of single-board By the end of this book, you'll be able to computers and NumPy. Next, you'll discover confi dently build and deploy computer how to install OpenCV 4 for Python 3 vision apps. Design