Sunday, February 12, 2017

Setting up a D-Star Access Point on Raspbian with PIXEL - Part II

This is part II of a two Part series. In part I, DStarRepeater and IRCDDBGateway were compiled and DstarRepeater was configured on Raspbian Jessie with PIXEL.  In Part II, IRCDDBGateway will be configured,and an Icom ID-51a+ will connect to the D-Star network through the D-Star hotspot.

Elizabeth Tower at the North end of the Palace of Westminster in London. In June 2012, the House of Commons Commission changed the name of the tower from Clock Tower to Elizabeth Tower in honour of the Queen's Diamond Jubilee.






Requirements


Directions


Configure ircddbgateway


Execute ircddbgatewayconfig on the target (Raspberry Pi) as follows.


pi@hbox:~ $ sudo ircddbgatewayconfig &






Replace KF5SVQ with your call sign.


Make sure that you select Save from the File Menu in order to save your changed to the configuration file.   Also make sure that you select Exit from the File menu after you select Save.

Start ircddbgateway and dstarrepeater

pi@hbox:~ $ sudo ircddbgateway &
pi@hbox:~ $ sudo dstarrepeater &



Configure the Radio 


Link to the UK D-Star Megarepeater

D-Pad -> Repeater List -> Simplex -> 145.67 DV
Press PTT
D-Pad -> Local CQ
Hold Down PTT and Talk

Setting up a D-Star Access Point on Raspbian with PIXEL - Part I

This is part I of a two Part series. In part I, DStarRepeater and IRCDDBGateway will be compiled and DStarRepeater will be configured on Raspbian Jessie with PIXEL.  In Part II, IRCDDBGateway will be configured and an Icom ID-51a+ will connect to the D-Star network through the D-Star hotspot.

Purpose


Create a hotspot or access point for a handheld radio in order to connect to the D-Star network.




Requirements





Definitions


Host - Your Desktop computer (Linux)
Target - Raspberry Pi 3 running Raspbian Jessie with PIXEL (Linux)






Directions


Write the Raspbian image to the uSD on the host.  Execute the following commands on the host.

dev@fedora:~ $ sudo umount /dev/sdb*
dev@fedora:~ $ sudo dd if=2017-01-11-raspbian-jessie.img of=/dev/sdb bs=1M

Boot the Pi with the uSD card.  Execute the following commands on the Pi.

pi@hbox:~ $ apt-get install libwxgtk3.0-dev libusb-1.0-0-dev
pi@hbox:~ $ mkdir ~/src
pi@hbox:~ $ cd ~/src
pi@hbox:~ $ git clone https://github.com/dl5di/OpenDV.git -b master
pi@hbox:~ $ cd OpenDV/ircDDBGateway
pi@hbox:~ $ ./configure
pi@hbox:~ $ make
pi@hbox:~ $ sudo make install
pi@hbox:~ $ cd ..
pi@hbox:~ $ cd DStarRepeater
pi@hbox:~ $ ./configure
pi@hbox:~ $ make
pi@hbox:~ $ sudo make install
pi@hbox:~ $ sudo mkdir -p /usr/local/etc/opendv
pi@hbox:~ $ sudo mkdir -p /usr/local/var/log/opendv


Configure DStarRepeater


Execute dstarrepeaterconfig on the target as follows.
















Replace KF5SVQ with your call sign.


Friday, September 16, 2016

Implementing Software-defined radio and Infrared Time-lapse Imaging with Tensorflow on a custom Linux distribution for the Raspberry Pi 3

GNURadio Companion Qt Gui Frequency Sync - multiple FIR filter taps
sample running on Raspberry Pi 3 custom Linux distribution
The Raspberry Pi 3 is powered by the ARM® Cortex®-A53 processor.  This 1.2GHz 64-bit quad-core processor fully supports the ARMv8-A architecture.
For this project, a custom Linux distribution was created for the Raspberry Pi 3.

The custom Linux distribution includes support for GNURadio, several FPGA and ARM Powered® SDR devices, D-STAR (hotspot, repeater, and dongle support), hsuart, libusb, hardware real-time clock support, Sony 8 megapixel NoIR image sensor, HDMI and 3.5mm audio, USB Microphone input, X-windows with xfce, lighttpd and php, bluetooth, WiFi, SSH, TCPDump, Docker, Docker registry, MySQL, Perl, Python, QT, GTK, IPTables, x11vnc, SELinux, and full native-toolchain development support.  

The Sony 8 megapixel image sensor with the infrared filter removed can be connected to the Raspberry Pi 3's MIPI camera serial interface.  Image capture and recognition can then be performed over contiguous periods of time, and time-lapsed video can be created from the images.  With support for Tensorflow and OpenCV, object recognition within images can be performed. 

D-STAR hotspot with time-lapsed infrared imaging.

For the initial run, an infrared Time-lapse Video was created from an initial image capture run of  one 3280x2460 infrared jpeg image captured every 15 seconds for three hours.  40, 5mm, 940nm LEDs, powered by 500ma over 12v DC provided infrared illumination in the 940nm wavelength.

Tensorflow was running in the background (on v4l2 kmod) and providing continuous object recognition and scoring within each image via a sample model.  Finally, OpenCV was also installed in the root file system.

The time-lapse infrared video was captured of my living room using the above setup and custom Linux distribution.  Below this image are images of Tensorflow running in a terminal in the background on the Raspberry Pi 3 and recognizing/scoring objects in my living room.


The Yocto distribution configuration file, image configuration file, custom recipes, and build configuration files are available on github at github.com/bryanhinton/hampi.


Tensorflow running on the Raspberry Pi 3 and continuously capturing frames from the image sensor and scoring objects
GNURadio Companion running on xfce on the Raspberry Pi 3