DOCSLIB.ORG

Using the WLAN Pros ODROID Performance Testing Device

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Hands-on Labs using the WLAN Pros ODROID Performance Testing Device The goal is to provide Wireless LAN Professionals with a ready-to-use custom device to help provide throughput measurements for network performance. It can be used to test Wired-to-Wired, Wired- to-Wireless, and even Wireless-to-Wireless tests. These tests can assist in establishing baselines, help in troubleshooting, test consistency, as well as measuring network throughput from known end points. Why ODROID? The ODROID C-2 device has much in common with other Single-Board-Computers, like Raspberry Pi and others. We chose this platform for its versatility but mainly because it offers a full Gigabit Ethernet port, so that component should not ever be a bottleneck or be a throttle to network throughput testing. (unlike the Raspberry Pi’s 10/100 Ethernet port). • The specs on the ODROID are: • 1.5GHz 64-bit quad-core single board computer (SBC) • Gigabit Ethernet • eMMC Flash Storage – boots in under 20-seconds • Low power consumption – around 1amp – we power with standard USB battery • Very Versatile • Ability to use Bluetooth or USB keyboard/mice • HDMI out capability • We use an attached screen to do much of what we need • Can be used in a ‘headless’ environment Linux Performance Testing Apps We are using Debian Linux with the following services running upon boot up so they are always ready: Application Version Port Iperf3 3.16 5202 Iperf2 2.0.9 5001 Ruckus ZAP 1.83 Ekahau eperf 3.x 5201 HTML5 Tests 6 versions 80 on Different URLs Client Applications (see other instructions on how to install all these client-side testing apps) Application Version MacOS Windows Android iOS Iperf3 3.1.6 X X Iperf2 2.0.9 X X ZAP 1.83 X X Ekahau ESS X X WiFiPerf (Demo) 1.9 X X X X Ruckus Speedflex 2.0.7 X X Hurricane Electric 1.5.0.289 X X Aruba Utilities X Note: All the files for client-side applications are provided either on the included USB drive or via Internet while doing automatic installations. It is recommended you do the Client installs before starting on the ODROID performance testing. The installation instructions are in the appendix at the end of this document. Powering On/Off Your ODROID The installed USB Battery from KORAL can be turned on with the power button on the side of the battery. Alternatively – the ODROID can be powered with any 5v/2a power source. Powering Off the KORAL battery by pressing the power button TWICE! Connect your client devices to either the 2.4GHz or 5GHz SSID’s. Note what IP addresses each received via WIRELESS connection. • MacOS ___.____.____.____ • Windows OS ___.____.____.____ • Android ___.____.____.____ • iOS ___.____.____.____ Plug your ODROID into a wired port. Note what IP address it received. • ODROID ___.____.____.____ Login and configure your ODROID • SSH to the ODROID using an SSH Client • Open Terminal (MacOS) or Putty (Windows) $ SSH [email protected] Default password = wlanpro Change root password: # passwd You should now have a remote session to your ODROID via SSH. Performance Testing with the ODROID device Task 1 – Basic Performance Test using iPerf Execute an iPerf test with iPerf2 $ iperf –c A.A.A.A Execute an iPerf3 test $ iperf3 –c A.A.A.A –p 5202 -C specifies client mode A.A.A.A is the ODROID IP address -p specifies the port to use Task 2 – Test Consistency using ZAP tool Using the Ruckus ZAP tool to measure the consistency and throughput of a network connection. • Start zapd (daemon) Note: The starting of the ZAP daemon on the ODROID is by default at startup – this is the ZAP Daemon on the client side. We will be running these ZAP tests from the client’s perspective. Open a different terminal (MacOS) or Command Prompt (Windows) where we can control the ODROID. Run ZAP test to ODROID $ zap –sA.A.A.A –dB.B.B.B -s specifies the source IP A.A.A.A is the ODROID IP Address -d specifies the destination IP B.B.B.B is your test device’s IP Address Note: Do NOT put a space after the –s or –d Note: This will run a long time… by default it will run 1,000 unique throughput tests. The results show not only average, but break down the results by percentile. Task 3 – Network Performance Graph using WiFiPerf • Start WiFiPerf (MacOS) • Configure WiFiPerf settings Target Server Address: A.A.A.A Server Port: 5202 • Run Test A.A.A.A is the ODROID’s IP address This tool is also using the ZAP application and showing a visual result of the statistics. Task 4 – Mobile Performance Testing using Speedflex • Start SpeedFlex app on iOS or Android • Configure SpeedFlex settings Destination Address: A.A.A.A • Run Test A.A.A.A is the ODROID’s IP address Task 5 – Mobile Performance Testing using Hurricane Electric Network Utilities iPerf2 and iPerf3 Use H/E Network Tools to perform an iperf2/3 measurement from iOS • Start H/E Network Tools • Select iperf from the list of tools • Configure iPerf settings Select: iperf2 iperf Server: A.A.A.A Interval: 2 Bytes: 500M 4. Select field at top with address and click Go Note: Just click in the field with the server address then click enter *To use iperf3, select iperf3 and specify port 5202 Example: A.A.A.A -p 5202 Task 6 – Mobile Performance Testing using Aruba Network Utilities Use Aruba Utilities to run an iPerf test from an Android device • Start Aruba Utilities (Android) • Swipe to the left to select iPerf page (Perhaps even multiple swipes) • Configure iPerf settings -c A.A.A.A -i 2 -t 10 -c connect to an iPerf server at specified IP -i sets the reporting interval time in seconds -t time in seconds to run test for • Run Task 7 - Remote test between 2 devices Use Zap to remotely measure the network performance between two devices. • Start Zapd or Ruckus SpeedFlex on any two devices Example: iPhone running SpeedFlex and Odroid running zapd • Run a remote zap test from Windows or Mac $ zap -sA.A.A.A -dB.B.B.B -s specifies source IP A.A.A.A = IP of Device 1 -d specifies destination IP B.B.B.B = IP of Device 2 *do not put a space after -s or -d Task 8 – Web Browser Speed Tests In this test, we will be using HTML5 code on the ODROID – there is one specific set of code for the default on port 80… but to get additional tests – one of which you might prefer for ease of use, readability, etc. We’ve added 6 more versions for you to choose from. Just type in the appropriate URL. • Start a Browser of choice • Head over to A.A.A.A - your ODROID’s IP Address • Make choices on the check-boxes to what you’d like to view • Click Start For additional tests – add one of these options to the end, like A.A.A.A/exampleX.html Turn your ODROID into a Wireless Access Point • Insert the enclosed USB Wi-Fi Adapter • Configure your Access Point settings by editing the AP config file: /boot/ap.txt from an SSH session to the ODROID • SSH to the ODROID using an SSH Client • Open Terminal (MacOS) or Putty (Windows) $ SSH [email protected] Default password = wlanpro • #nano /boot/ap.txt • Modify the following settings from the WLANPros image: SSID WLAN_PRO wpa_passphrase changeme channel 36 • Press Button 3 on the ODROID to start/stop the Access Point • Turn on the access point, and then associate with your client. Check your IP address. • Connect your client devices to the SSID’s provided by the ODROID Access Point. Note what IP addresses each received. • MacOS ___.____.____.____ • Windows OS ___.____.____.____ • Android ___.____.____.____ • iOS ___.____.____.____ • Connect to the ODROID via SSH like above, or run any of the throughput tests. In this case you’ll be testing the Wi-Fi USB device’s capabilities… By default it should be handing out 192.168.42.xxx IP’s from the ODROID’s DHCP pool. Use ODROID as a Remote Sensor for Wi-Fi Explorer Pro On the ODROID: • Press button #2 to enable and disable the Remote Sensor service • Note: SSH command line # service wifiexplorer-sensor start/stop On the MacOS Client: • Start WiFi Explorer Pro Add a remote sensor You are now feeding information from the ODROID’s attached USB Wi-Fi device directly to the running copy of Wi-Fi Explorer and can do remote analysis. (Think of having the ODROID shipped to a remote spot, plugged into an Ethernet port the attached remotely using Wi-Fi Explorer for gathering detailed information.) Appendix Client Installations for ODROID Testing Macintosh Install iPerf3 on Mac OS Install Xcode Launch Terminal Application $ xcode-select --install Install iperf3 $ sudo git clone https://github.com/esnet/iperf.git $ cd iperf $ sudo ./configure $ sudo make $ sudo make install Test if it is working $ iperf3 –v Start an iperf 3 server $ iperf3 –s Run an iperf3 client $ iperf3 –c x.x.x.x (IP address of iperf3 server) Install iperf2 on Mac OS First, download and extract the latest iperf2 source code from here: https://sourceforge.net/projects/iperf2/?source=typ_redirect Unzip and Save to your desktop. Open Terminal application and change directories to the location of the extracted iperf2 files, for example: $ cd ~/Desktop/iperf-2.0.9 $ ./configure $ sudo make $ sudo make install Test if iperf2 is installed: $ iperf –v Start iperf2 server: $ iperf -s Run an iperf test as the client: $ iperf -c x.x.x.x (iperf2 server IP) Install ZAP on Mac OS Open Terminal application.
Recommended publications
  • Setting up Odroid C2 – Throughtput Testing

    Setting up Odroid C2 – Throughtput Testing

    Throughput Testing Single-Board Computer for Wireless LAN Professionals Setting up Odroid C2 As Test End-Point Inventory Project Items Unpack and knoll the following items ❑ Impact Strong 5200 mAh USB Battery o And it has a flashlight as well! ❑ USB Micro Power Cable (comes with Battery) ❑ Micro SD Card to eMCC adapter (Blueish green) ❑ eMCC Memory Card (small with Red label) ❑ Transcend USB 3.0 SD Card Reader ❑ Odroid Wi-Fi 4 NIC – (Black) ❑ USB ‘U-bend’ Adaper (Black ❑ Odroid C2 – Single Board Computer ❑ #WLPC_EU USB Drive (has the Image) ❑ Odroid Case Kit (Black) Later we will have you use as part of the testing (not in your own kit) • Small Phillips Screwdriver (to assemble your case) • Short Ethernet Cable (to test your unit when attached to a switch port) ❑ If you are running Windows copy the Win32DiskImager-0.9.5-binary executable as well. If you are running Windows follow the Windows directions, if Mac OS – move to the Mac OS directions. Mac OS Version Configure Image ❑ Copy Image File from the #WLPC_EU USB drive (in the Odroid for #WLPC_EU folder) to your Desktop… the file name is DietPi_v133_OdroidC2-arm64- (WLAN_PRO) ❑ Open Image File – Boot Drive ❑ Should have a Folder Call “Boot” ❑ Find the dietpi.txt We are changing setting so your Odroid device can join a local SSID and download more files we’ll need. ❑ Under the Wi-Fi Details section change the Wi-Fi SSID to WLPC_EU-01 with a PSK of password. #Enter your Wifi details below, if applicable (Case Sensitive). Wifi_SSID=WLPC_EU-01 Wifi_KEY=password ❑ Under WiFi Hotspot Settings,
  • Improving the Beaglebone Board with Embedded Ubuntu, Enhanced GPMC Driver and Python for Communication and Graphical Prototypes

    Improving the Beaglebone Board with Embedded Ubuntu, Enhanced GPMC Driver and Python for Communication and Graphical Prototypes

    Final Master Thesis Improving the BeagleBone board with embedded Ubuntu, enhanced GPMC driver and Python for communication and graphical prototypes By RUBÉN GONZÁLEZ MUÑOZ Directed by MANUEL M. DOMINGUEZ PUMAR FINAL MASTER THESIS 30 ECTS, JULY 2015, ELECTRICAL AND ELECTRONICS ENGINEERING Abstract Abstract BeagleBone is a low price, small size Linux embedded microcomputer with a full set of I/O pins and processing power for real-time applications, also expandable with cape pluggable boards. The current work has been focused on improving the performance of this board. In this case, the BeagleBone comes with a pre-installed Angstrom OS and with a cape board using a particular software “overlay” and applications. Due to a lack of support, this pre-installed OS has been replaced by Ubuntu. As a consequence, the cape software and applications need to be adapted. Another necessity that emerges from the stated changes is to improve the communications through a GPMC interface. The depicted driver has been built for the new system as well as synchronous variants, also developed and tested. Finally, a set of applications in Python using the cape functionalities has been developed. Some extra graphical features have been included as example. Contents Contents Abstract ..................................................................................................................................................................................... 5 List of figures .........................................................................................................................................................................
  • Suzanne's Microcluster Slides

    Suzanne's Microcluster Slides

    csinparallel.org Microclusters for teaching PDC Suzanne J. Matthews (West Point) 1 csinparallel.org What is a Microcluster? • A personal, highly portable Beowulf cluster • Enables highly interactive and tactile experiential learning • Notable early examples: – Ultimate Linux Lunch Box (Ron Minnich and Mitch Williams, Sandia National Labs) – LittleFe (Charlie Peck, Earlham College) – Microwulf (Joel Adams, Calvin College) 2 csinparallel.org Single Board Computers (SBCs) 3 csinparallel.org Student Pi (West Point) Suzanne J. Matthews Raspberry Pi nodes - Prototype: Raspberry Pi B nodes - Initial: Raspberry Pi B+ nodes - Current: Raspberry Pi 2 nodes - 900 Mhz quad-core CPU, 1 GB of RAM, HDMI, USB, 10/100 Ethernet - Raspbian Linux June 2014 - ~$40 p/node - Materials: - http://suzannejmatthews.com/private/cluster.html October 2014 May 2016 4 csinparallel.org Student Parallella (West Point) Suzanne J. Matthews Parallella nodes - dual-core ARM A9 CPU, 16-core Epiphany co-processor, 1 GB of RAM, μHDMI, μUSB, Gigabit Ethernet - Linaro Linux - ~$145 p/node - Materials: - http://suzannejmatthews.com/private/cluster.html - http://suzannejmatthews.github.io/ October 2014 April 2016 January 2015 5 csinparallel.org Half ShoeBox Clusters (Centre College) David Toth Cubieboard/ODROID nodes (2-node clusters) - Prototype: Cubieboard2: dual-core ARM Cortex A7, 1 GB of RAM, HDMI, USB, 10/100 Ethernet - Latest: ODROID C2: 2Ghz quad-core A53, 2 GB of RAM, HDMI, USB, Gigabit Ethernet, - Android/Ubuntu Linux - ~ $150-$200 p/cluster - Materials: Early 2014 - http://web.centre.edu/david.toth/portablecluster/index.html
  • Passmark - Android Device List

    Passmark - Android Device List

    PassMark - Android Device List https://www.androidbenchmark.net/device_list.php AndroidTM Benchmarks Performance Comparison of Android Devices Below is an alphabetical list of all Android device types that appear in the charts. Clicking on a specific device name will take you to the charts where it appears in and will highlight it for you. PassMark Rating CPUMark Rating PassMark Rank Android Device Type Samples (higher is better) (higher is better) (lower is better) 4G R17S 1,572 4,088 1253 1 A-gold BV9500Plus 5,052 13,068 375 1 A-gold BV9800 4,450 11,400 487 1 A-gold F1 4,237 10,869 531 7 A-gold S3_Pro 4,392 11,219 504 2 A-gold Z2_PRO 4,406 11,246 499 1 A1 Alpha 20+ 4,753 12,266 435 1 Acer A3-A40 1,982 5,269 1082 1 Acer AO722 519 1,272 1725 1 1 z 62 2020-10-14, 12:02 PassMark - Android Device List https://www.androidbenchmark.net/device_list.php PassMark Rating CPUMark Rating PassMark Rank Android Device Type Samples (higher is better) (higher is better) (lower is better) AGM A10 2,030 8,521 1066 1 ALCATEL A574BL 497 1,202 1736 1 AlcatelOneTouch Alcatel_5044R 438 1,129 1759 1 Alco CT9223W97 1,214 3,111 1384 1 ALLDOCUBE M8 2,730 7,274 882 5 ALLDOCUBE T701 1,092 4,554 1437 1 ALLDOCUBE U1006H 1,902 4,931 1125 1 ALLVIEW P7_PRO 1,691 4,543 1210 1 ALLVIEW X4_Soul 2,536 6,938 925 1 Alps Acer One 8 T4-82L 2,539 6,526 924 1 Alps Tablet18T 1,201 3,043 1394 1 Alps tb8788p1_64_bsp 2,343 5,784 983 2 Amazon KFKAWI 712 1,701 1589 4 Amazon KFMAWI 2,306 5,640 992 19 Amazon KFONWI 1,082 2,588 1442 3 Amlogic A95X-A3 1,228 3,182 1381 1 Amlogic ABOX A4 397
  • Security Monitor for Mobile Devices: Design and Applications

    Security Monitor for Mobile Devices: Design and Applications

    UNIVERSITY OF CALIFORNIA, IRVINE Security Monitor for Mobile Devices: Design and Applications DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Saeed Mirzamohammadi Dissertation Committee: Professor Ardalan Amiri Sani, UCI, Chair Professor Gene Tsudik, UCI Professor Sharad Mehrotra, UCI Doctor Sharad Agarwal, MSR 2020 Portion of Chapter 1 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 1 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 1 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 1 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 2 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 2 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 3 c 2018 ACM, reprinted, with permission, from [148] Portion of Chapter 4 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 5 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 6 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 7 c 2020 ACM, reprinted, with permission, from [151] Portion of Chapter 7 c 2017 ACM, reprinted, with permission, from [150] Portion of Chapter 7 c 2018 IEEE, reprinted, with permission, from [149] Portion of Chapter 7 c 2018 ACM, reprinted, with permission, from [148] All other materials c 2020 Saeed Mirzamohammadi TABLE OF CONTENTS Page LIST OF FIGURES v LIST OF TABLES vii ACKNOWLEDGMENTS viii VITA ix ABSTRACT OF THE DISSERTATION xi 1 Introduction 1 1.1 Applications of the security monitor .
  • Cubieboard Cubieboard2 Cubietruck Beaglebone Black

    Cubieboard Cubieboard2 Cubietruck Beaglebone Black

    Raspberry Pi (Model B rev.2) Cubieboard Cubieboard2 Cubietruck Beaglebone Black 1 Ghz (OC) ARM® Cortex-A6 1 Ghz ARM® Cortex-A8 1 Ghz ARM® Cortex-A7 Dual Core 1 Ghz ARM® Cortex-A7 Dual Core 1 Ghz ARM® Cortex-A8 CPU ARM1176JZF-F Allwinner A10 C8096CA Allwinner A20 Allwinner A20 AM335x GPU/FPU VideoCore IV Mali-400 (CedarX, OpenGL) Mali-400MP2 (CedarX, OpenGL) Mali-400MP2 (CedarX, OpenGL) SGX350 3D / NEON FPU accelerator RAM 512 MB 1 GB DDR3 2 GB 2 GB 512 MB DDR3 Storage micro SD/SDHC 4 GB NAND Flash, micro SD/SDHC, SATA 4 GB NAND Flash, micro SD/SDHC, SATA 4 GB NAND Flash, micro SD/SDHC, SATA 2.0 2GB eMMC Power micro USB (5V/1A) 3.5 W DC 5v/2A DC 5v/2A DC 5v/2.5A DC 5V/500mA Video RCA Composite Video, HDMI 1.4 HDMI HDMI HDMI/VGA microHDMI Audio 3.5 mm Headphone Jack 3.5 mm Headphone Jack / Line In 3.5 mm Headphone Jack 3.5 mm Headphone Jack, SPDIF Network 10/100 Mbps 10/100 Mbps 10/100 Mbps 10/100/1000 Mbps, Wifi, Bluetooth 10/100 Mbps 2x46 PIN GPIO I/O ports 26 PIN GPIO, 2x Ribon 2x48 PIN GPIO, 4PIN Serial, 1IR 2x48 PIN GPIO, 4PIN Serial, 1IR 1x 54 PIN GPIO (Arduino Shield Compatible) USB ports 2x USB 2.0 2x USB 2.0 2x USB 2.0, 1 mini USB OTG 2x USB 2.0, 1 mini USB OTG 1x USB 2.0 Linux (Raspbian, Debian, Fedora, Arch, Gentoo, Kali), Andoid, Angstrom, Ubuntu, Fedora, Gentoo.
  • Passmark Android Benchmark Charts - CPU Rating

    Passmark Android Benchmark Charts - CPU Rating

    PassMark Android Benchmark Charts - CPU Rating http://www.androidbenchmark.net/cpumark_chart.html Home Software Hardware Benchmarks Services Store Support Forums About Us Home » Android Benchmarks » Device Charts CPU Benchmarks Video Card Benchmarks Hard Drive Benchmarks RAM PC Systems Android iOS / iPhone Android TM Benchmarks ----Select A Page ---- Performance Comparison of Android Devices Android Devices - CPUMark Rating How does your device compare? Add your device to our benchmark chart This chart compares the CPUMark Rating made using PerformanceTest Mobile benchmark with PerformanceTest Mobile ! results and is updated daily. Submitted baselines ratings are averaged to determine the CPU rating seen on the charts. This chart shows the CPUMark for various phones, smartphones and other Android devices. The higher the rating the better the performance. Find out which Android device is best for your hand held needs! Android CPU Mark Rating Updated 14th of July 2016 Samsung SM-N920V 166,976 Samsung SM-N920P 166,588 Samsung SM-G890A 166,237 Samsung SM-G928V 164,894 Samsung Galaxy S6 Edge (Various Models) 164,146 Samsung SM-G930F 162,994 Samsung SM-N920T 162,504 Lemobile Le X620 159,530 Samsung SM-N920W8 159,160 Samsung SM-G930T 157,472 Samsung SM-G930V 157,097 Samsung SM-G935P 156,823 Samsung SM-G930A 155,820 Samsung SM-G935F 153,636 Samsung SM-G935T 152,845 Xiaomi MI 5 150,923 LG H850 150,642 Samsung Galaxy S6 (Various Models) 150,316 Samsung SM-G935A 147,826 Samsung SM-G891A 145,095 HTC HTC_M10h 144,729 Samsung SM-G928F 144,576 Samsung
  • Building a Datacenter with ARM Devices

    Building a Datacenter with ARM Devices

    Building a Datacenter with ARM Devices Taylor Chien1 1SUNY Polytechnic Institute ABSTRACT METHODS THE CASE CURRENT RESULTS The ARM CPU is becoming more prevalent as devices are shrinking and Physical Custom Enclosure Operating Systems become embedded in everything from medical devices to toasters. Build a fully operational environment out of commodity ARM devices using Designed in QCAD and laser cut on hardboard by Ponoko Multiple issues exist with both Armbian and Raspbian, including four However, Linux for ARM is still in the very early stages of release, with SBCs, Development Boards, or other ARM-based systems Design was originally only for the Raspberry Pis, Orange Pi Ones, Udoo critical issues that would prevent them from being used in a datacenter many different issues, challenges, and shortcomings. Have dedicated hard drives and power system for mass storage, including Quads, PINE64, and Cubieboard 3 multiple drives for GlusterFS operation, and an Archive disk for backups and Issue OS In order to test what level of service commodity ARM devices have, I Each device sits on a tray which can be slid in and out at will rarely-used storage Kernel and uboot are not linked together after a Armbian decided to build a small data center with these devices. This included Cable management and cooling are on the back for easy access Build a case for all of these devices that will protect them from short circuits version update building services usually found in large businesses, such as LDAP, DNS, Designed to be solid and not collapse under its own weight and dust Operating system always performs DHCP request Raspbian Mail, and certain web applications such as Roundcube webmail, Have devices hooked up to a UPS for power safety Design Flaws Allwinner CPUs crash randomly when under high Armbian ownCloud storage, and Drupal content management.
  • Smart Walker IV

    Smart Walker IV

    Multidisciplinary Senior Design Conference Kate Gleason College of Engineering Rochester Institute of Technology Rochester, New York 14623 P16041: Smart Walker IV Alex Synesael Alexei Rigaud Electrical Engineering Electrical Engineering Danielle Stone Stephen Hayes Electrical Engineering Electrical Engineering ABSTRACT Diagnostic medical technology in its current form is typically expensive and intrusive to a patient’s lifestyle. The purpose of the Smart Walker IV project was to finalize a robotic assistive walker prototype capable of collecting long-term diagnostic information about a patient’s health and activities. This gives care providers a more complete image of their health than can be achieved during a relatively short visit. In addition to the diagnostic features, Smart Walker is capable of autonomous motion such that it can provide emergency support in the event of a fall, or contact the appropriate third party support. Smart Walker IV is a culmination of three previous prototype versions spanning 2013 through 2015, and is intended to serve as the final revision before the platform is ready to be utilized as a graduate research platform for robotic assistive medical diagnostics. The goal of Smart Walker IV is to complete the build and integration phases from where the previous Smart Walker III team ended. The project progressed through analysis and completion of the existing systems, the design and testing of new diagnostic subsystems, and finally complete system integration and qualification. BACKGROUND Smart Walker began as a diagnostic tool for elderly people, and has since evolved into a research platform. The Smart Walker can take diagnostic measurements, as well as measure vital signs and detect when a patient has fallen.
  • An ARM Cluster for Running CMSSW Jobs Lirim Osmani1 and Tomas Linden´ 2 1Department of Computer Science, University of Helsinki, 2Helsinki Institute of Physics

    An ARM Cluster for Running CMSSW Jobs Lirim Osmani1 and Tomas Linden´ 2 1Department of Computer Science, University of Helsinki, 2Helsinki Institute of Physics

    An ARM cluster for running CMSSW jobs Lirim Osmani1 and Tomas Linden´ 2 1Department of Computer Science, University of Helsinki, 2Helsinki Institute of Physics Introduction Power measurements To meet the computing challenge posed by the High-Luminosity Large The RPi4 is powered by a 5 V / 3 A power supply with a USB-C connector. Hadron Collider (HL-LHC) improved software performance, changed analysis The power drawn by the RPi4 board itself was measured with an AVHzY CT-2 procedures and new hardware resources are required. ARM computers are USB-power meter, neglecting the losses of the power supply. developed for the highly comptetive mobile phone market, so they might The Odroid-N2 uses a 12 V / 1.5 A power supply so a less accurate meter provide less expensive computing resources for HL-LHC computing. had to be used than the one for the RPi4. Hardware Table 2: Power measurements. CMSSW has been ported in earlier work to armv7 [1, 2] and to armv8 [3, 4]. Computer Idle stress-ng Odroid-N2 2±1 W 6±1 W The available ARM systems are usually either servers or inexpensive Raspberry Pi 4 Model B with heat sink 2.6 ±0.1 W 5.8±0.1 W development boards. In this work we have used small ARM development Kaby Lake 14 nm quad core i7-7700 Fedora 29 8±1 W 99±1 W boards, two hexa core Odroid-N2 [5] from HardKernel and two quad core Raspberry Pi 4 Model B (RPi4 in the following) [6] from the Raspberry Pi Foundation.
  • Machine Learning/AI Processing on Mobile and Iot Edge Devices

    Machine Learning/AI Processing on Mobile and Iot Edge Devices

    Mobile AI Machine Learning/AI processing on mobile and IoT Edge Devices Emilio Jose Coronado Lopez http://seoulai.com/ Paradigm We’re living in a data world. A world of connected sensors, signals and data streams. Sensory data continuously flows and gathers into a form of mobile or IoT Edge device, then processed, transferred through network to cloud backends, storage, analytics services... Analytics, outputs coming from it, are some kind of offline, late, delayed processing, mostly runs on stored or historical data. This has been pretty much the main scenario of many business and entities entering into digital industry 4.0. However, it will scale and growth through instant feedback, real time ML/AI services, IT and cloud providers are quite use to those incremental paths on controlled environments, but adding wireless, mobile networks, tough latencies and response times hard constraints beyond the confined system is another story. Examples of Real Time Data Streams Cloud or backend AI/ML storage processing Hardware/Network Wifi, 3G/4G, LPWAN, BT Interfaces ... Camera Data Sensor Signals Signals/Data Collect, gateway and transfer to the cloud or backend for Data Processing Audio Data further processing. Sensor Signals Model Training or Mobile Device Previously IMU/Loc Data Trained Sensor Signals Temp. Data Infer/Predict Sensor Signals Iot Edge Device Stock M. Data Sensor Signals Social N. Data Data/Action/Feedback to the source Sensor Signals To another subsystems Constraints: Latencies Constraints: Latencies, Processing, Bandwidth, data storage …. It’s happening now A typical big data, analytics pipeline integrated with Open Source machine learning frameworks like Tensorflow, Caffe, Keras, etc.
  • Diplomat: Mapping of Multi-Kernel Applications Using a Static Dataflow Abstraction

    Diplomat: Mapping of Multi-Kernel Applications Using a Static Dataflow Abstraction

    Diplomat: Mapping of Multi-kernel Applications Using a Static Dataflow Abstraction Bruno Bodin Luigi Nardi & Paul H. J. Kelly Michael F. P. O’Boyle The University of Edinburgh, Imperial College London, The University of Edinburgh, Edinburgh, United Kingdom London, United Kingdom Edinburgh, United Kingdom Email: [email protected] Email: fl.nardi,[email protected] Email: [email protected] Abstract—In this paper we propose a novel approach to They provide an automatic mapping, yet these models are heterogeneous embedded systems programmability using a task- overly restrictive. As an example, many vision applications graph based framework called Diplomat. Diplomat is a task- include dynamic behavior, such as early exit conditions, graph framework that exploits the potential of static dataflow modeling and analysis to deliver performance estimation and which are not supported by those models. Tools such as CPU/GPU mapping. An application has to be specified once, and Qualcomm Symphony [3] (previously known as MARE) are then the framework can automatically propose good mappings. more expressive. Symphony lets a programmer represent an We evaluate Diplomat with a computer vision application on two application using a task-graph, a more general representation embedded platforms. Using the Diplomat generation we observed of the dataflow model where communications are no longer a 16% performance improvement on average and up to a 30% improvement over the best existing hand-coded implementation. static. However, Symphony tasks are dynamically defined and mapping decisions are performed manually. Ideally we would like a framework that is both flexible and able to provide high I.