HOW-TO: Music Reactive Desk Light

@ build a sound reacting LED light using Arduino:

Hi! In this build we’ll make a good looking light that dances to all sounds and music, using simple components and some basic Arduino programming. It makes an awesome effect while standing on the desk when gaming, playing music, and anything else that makes sound really. Let’s get going!

HOW-TO: Music Reactive Desk Light – [Link]

A Compact Camera Using Raspberry Pi A+ And Adafruit TFT Display

PiJuice at designed an interesting compact camera project with raspberry pi. Raspberry Pi A+ is used in this project as it is the cheapest and smallest available Raspberry Pi. The real challenge in this kind of portable Pi projects is powering the Raspberry Pi. This issue is solved using PiJuice—an all in one battery module for the Raspberry Pi.

Required Parts

Required parts to make Raspberry Pi compact camera
Required parts to make Raspberry Pi compact camera

Set Up The Raspberry Pi

Download the latest version of the Raspbian image from the Raspberry Pi Website and burn it on your blank SD card. You can use win32DiskImager or your favorite software to get the job done. Now, you need to install the drivers for the TFT screen by running the DIY installer script, explained on the Adafruit page. Connect the TFT to the Raspberry Pi, attach the PiJuice with a charged battery, and switch it on. Your screen now should display boot up messages.

Connect The Camera

Insert the ribbon cable of your camera module properly ensuring that the blue side of the ribbon is facing away from the HDMI port. Now, go to the terminal and type the following command,

sudo raspi-config

Enable the camera in the menu and then reboot the Pi. The camera should work properly after a successful reboot. To test the camera, enter the following command:

raspistill -o pic.jpg

This will take a snap and save it in the /home/pi directory.

Connect A Push Button

You need a push button to simulate a shutter action. Locate the pin 17 on the GPIO breakout on the top of the TFT screen. Now, solder two wires to the terminals of the push button. You can either solder a right angle header to the pin 17 or you can directly solder one wire from push button to that pin. There is a pad labeled WP on the board. It is actually connected to the ground. Solder another wire from the push button to this pad.

Install And Test The PiCam Software

To install the software, the Raspberry Pi must be connected to the internet. Enter the commands given below to download and install PiCam.

sudo apt-get install git-core
sudo mkdir PiCam
cd /PiCam
git clone git://

Once the software has been downloaded, navigate to the PiCam directory using the command:

cd /picam

You can run it by typing the command:

sudo python

Now, you can take pictures by simply pressing the push button. Once the button is pressed the picture will be taken. Once the captured image gets loaded, your photograph will be displayed.

Taking photograph with Raspberry Pi compact camera
Taking photograph with Raspberry Pi compact camera


Your Raspberry Pi camera is ready now. If you want to make it even more compact as well as portable, grab the official laser-cut compact camera case from the Kickstarter page by pre-ordering a Maker Kit. You can also build your own simple chassis for housing the camera.

GPS vs. Beacons vs. Wi-Fi: Three Location Identifier Technologies

In IoT and digital age, location-based services applications are widespread: starting from Google maps to anti-loss devices and not ending with location-based marketing. The most common technologies used for user location identification are: GPS, WiFi and Beacons (a custom BLE profile).

Location-based (geofencing) marketing is a new way to enhance the personal experience while shopping. For example if you were near the shampoo section you will get on your mobile exclusive offers about that section.

Choosing the right location detection technology needs to take into consideration that GPS works optimally in the open sky environments and WiFi and Beacons can work probably indoors (and outdoors but within inhabited areas with hotspots). Now let’s get a brief look at each technology:


Thanks to on-the-shelf GPS modules/receivers from vendors like: Neoway and u-blox it’s easy to embed a GPS receiver into your project. What you need is a module sending its messages via UART to the MCU and a ready-made antenna attached to the module. There is a standard format for these modules messages called NEMEA. These messages contain information about the location that includes longitude, latitude, direction, speed … etc. These receivers need to see at least 4 satellites to compute a position.

There are many navigation systems like the Russian GLONASS, the European Union’s Galileo and the American GPS.

gps system how it works
Image courtesy of: Geneko

GPS is mainly designed to be an outdoor location detection system. Therefore, its performance decreases in enclosed places and across crowded areas with buildings.


WiFi can be used in location detection (AKA Wi-Fi positioning system) when your phone or WiFi transceiver module like ESP32 or ESP8266 is near hotspots. You can consider WiFi like a coexisting system with GPS for indoor areas. Moreover, WiFi can be used to detect the location inside the enclosed/underground area; you can see the SubPos project on Hackaday to know how.

Image Courtesy of Blecky

Location detection systems using WiFi use techniques based on received signal strength indication (RSSI), angle of arrival (AoA) and time of flight (ToF). You can read more about these techniques from the Wikipedia article.

Bluetooth Beacons

Beacon technology is enabled by Bluetooth Low Energy (BLE) and it’s one of the BLE custom profiles. Beacons are used for proximity-aware applications like positioning indoors, and for location based advertisements. The idea behind this technology is to calculate the distance between the receiver and the transmitter by calculating the difference between the power of the sent and received signal (comparing the Received Signal Strength Indicator (RSSI) to a transmit (Tx) power). Knowing that, the power information is available in Apple iBeacon advertising packet (for example).


To know more about Bluetooth beacons please refer to our previous post about Beacons.

Read more about these three technologies in the DZone’s article.

RASPILIGHT: an open project for Ambilight TV effect

LucaBellan @  re-created the Ambilight TV effect on any other TV using Raspberry and Kodi. He writes:

The screen’s edges are divided into logic sectors, and each sector is associated with a specific LED and, by making a color average of the pixels, you can find the color to set to be reproduced by the LEDs; this operation is repeated for all the LEDs mounted on the TV and all of this is repeated hundreds of time per second in order to provide synchronicity and maximum smoothness to the colors projected around the TV.

With RaspiLight we can re-create this technology and apply it to any flat-screen TV, but there’s more: even when the TV is off, we can control the system through an Android or iOS app and create static or dynamic light effects and make the TV an animated lighting point and not just a simple lighting piece of furniture.

RASPILIGHT: an open project for Ambilight TV effect – [Link]

Bismuth Oxyiodide (BiOI)—A Non-toxic Alternative To Solar Cells

Bismuth is considered as a “green-element” and bismuth-based compounds are gaining attention as potentially non-toxic and defect-tolerant solar absorbers. The researchers of the University of Cambridge and the United States developed theoretical and experimental methods to show that bismuth, which sits next to lead (Pb) on the periodic table, can be used to make inexpensive solar cells.

Bismuth oxyiodide light absorbers
Bismuth oxyiodide light absorbers

The study suggests that solar cells including bismuth can have all the exceptional properties of lead-based solar cells but without any worries about toxicity. Another study by a different group discovered that bismuth-based solar cells have the ability to achieve a conversion efficiency of 22% which is comparable to the conversion efficiency of most advanced solar cell available in the market.

Many of the new materials recently investigated show limited photovoltaic performance. Bismuth Oxyiodide (BiOI) is one such compound and it is explored in detail through theory and experiment. Most of the solar cells commercially and domestically used are made from silicon (Si) which is efficient enough but has very low defect tolerance compared to bismuth oxyiodide. Low defect tolerance in silicon implies that the silicon needs to have very high levels of purity, making the production process energy-intensive.

Over the past several years researchers have been looking for an alternative to silicon for making solar cells cost effectively. The most promising group of these new materials are called hybrid lead halide perovskites. Unlike silicon, they don’t need such high purity levels. Hence, production is cheaper. But, the lead contained within perovskite solar cells represents a definite risk to all living beings and the environment. So, scientists are searching for non-toxic alternatives without compromising the performance.

Dr. Robert Hoye of Cambridge’s Cavendish Laboratory and Department of Materials Science & Metallurgy said,

We wanted to find out why defects don’t appear to affect the performance of lead-halide perovskite solar cells as much as they would in other materials.

The researchers are trying to figure out what’s special about the lead halide perovskites so that they can replicate their properties using non-toxic materials like bismuth.

Their research found that bismuth oxyiodide is as defect tolerant as lead halide perovskites are. Another interesting fact is, bismuth oxyiodide is stable in air for at least 197 days which is even better than some lead halide perovskite compounds. By sandwiching the bismuth oxyiodide between two oxide electrodes, the researchers successfully converted 80% of light to electrical charge.

SeaTalkie keeps you SAFE during water sports

John Mak @ tipped us with his latest project and he asks for our support.

SeaTalkie is an innovative waterproof walkie talkie designed for water sports. Especially for CHILDREN playing at crowded beaches.

SeaTalkie connecting in UHF radio band. Thus SeaTalkie can connect to other walkie talkies. Furthermore, Different walkie has different features. Some have further transmit range, some have larger batteries, some are louder in speaker. And SeaTalkie is featured strongly in waterproof, simply operating and mounting accessories for sports. Therefore, for better performance, A team may integrate different featured walkies all together to optimise the performance of communication. Of course including SeaTalkie.

SeaTalkie keeps you SAFE during water sports – [Link]


Build And Simulate Quantum Software Applications With Rigetti Forest 1.0

Rigetti Computing is a full-stack quantum computing company. They build hardware and software with fundamentally new integrated circuits that store and process quantum information.

Accordingly, this Silicon Valley company is providing solutions for existing problems that traditional computers can not solve. These problems include the ability to provide molecular simulation showing all interactions and to accurately predict next week’s weather.

An 8-qubit quantum processor built by Rigetti Computing. (PRNewsfoto/Rigetti Computing)

Thus, Rigetti is using quantum mechanics for computation. Adding one quantum bit (qubit) can double the performance. Below is a table mapping the computation power of qubits with classical memories.

Rigetti Computing recently unveiled its Fab-1 facility. A facility which will enable its engineers to rapidly build new generations of quantum computing hardware based on quantum bits, or qubits. In fact, the facility can spit out entirely new designs for 3D-integrated quantum circuits within about two weeks—much faster than the months usually required for academic research teams to design and build new quantum computing chips. It’s not so much a quantum computing chip factory as it is a rapid prototyping facility for experimental designs.

Software is also included

It has also announced its Forest 1.0 service that enables developers to begin writing quantum software applications and simulating them on a 30-qubit quantum virtual machine. Forest 1.0 is based on Quil—a custom instruction language for hybrid quantum/classical computing—and open-source python tools intended for building and running Quil programs.

“Developing quantum computing software is one of the most fascinating and challenging emerging fields of engineering. Today, that field is at the foundational stage, where learning and discovery are at a premium. Our full-stack strategy allows us to run faster, more tightly coupled iteration cycles between hardware, software, and applications.” – Chad Rigetti, Founder & CEO

More details about this API are available on Forest 1.0 official page and this blog. Also watch this workshop video by Rigetti:

Flat microscope for the brain could help restore lost eyesight

by Jon Fingas @ Engadget:

You’d probably prefer that doctors restore lost sight or hearing by directly repairing your eyes and ears, but Rice University is one step closer to the next best thing: transmitting info directly to your brain. It’s developing a flat microscope (the creatively titled FlatScope) that sits on your brain to both monitor and trigger neurons modified to be fluorescent when active. It should not only capture much more detail than existing brain probes (the team is hoping to see “a million” neurons), but reach levels deep enough that it should shed light on how the mind processes sensory input. And that, in turn, opens the door to controlling sensory input.

Flat microscope for the brain could help restore lost eyesight – [Link]

Movidius Deep Learning USB Stick by Intel

Last week, Intel launched the Movidius Neural Compute Stick, which is a deep learning processor on a USB stick.

This USB stick was not an Intel invention. In fact, Intel had acquired Movidius company that had produced last year the world’s first deep learning processor on a USB stick based around their Myriad 2 Vision Processor.

Neural Compute Stick is based around the Movidius MA2150, the entry level chip in the Movidius Myriad 2 family of vision processing units (VPUs). Using this stick will allow you to add some artificial visual intelligence to your applications like drones and security cameras. 

Movidius Neural Compute Stick form factor device enables you prototype and tune your deep neural network. Moreover, the USB form factor connects to existing hosts and other prototyping platforms. At the same time, the VPU provides machine learning on a low-power inference engine.

Actually, the stick role comes after training your algorithm where it is ready to try real data. All you have to do is to translate your trained neural network from the desktop using the Movidius toolkit into an embedded application inside the stick. Later on, the toolkit will optimize this input to run on the Myriad 2 VPU. Note that your trained network should be compatible with Caffe deep learning framework.

It is a simple process

  1. Enter a trained Caffe
  2. Feed-forward Convolutional Neural Network (CNN) into the toolkit
  3. Profile it
  4. Compile a tuned version ready for embedded deployment using the Neural Compute Platform API.

An outstanding feature is that the stick can work without any connection to cloud or network connection, allowing to add smart features to really small devices with lower consumption. This feature may be on of the revolutionary ideas to start combining IoT and machine learning devices.

Neural Compute Stick Features

  • Supports CNN profiling, prototyping, and tuning workflow
  • All data and power provided over a single USB Type A port
  • Real-time, on device inference – cloud connectivity not required
  • Run multiple devices on the same platform to scale performance
  • Quickly deploy existing CNN models or uniquely trained networks
  • Features the Movidius VPU with energy-efficient CNN processing

“The Myriad 2 VPU housed inside the Movidius Neural Compute Stick provides powerful, yet efficient performance — more than 100 gigaflops of performance within a 1W power envelope — to run real-time deep neural networks directly from the device. This enables a wide range of AI applications to be deployed offline.” — Remi El-Ouazzane, VP and General Manager of Movidius.

At the moment, the stick SDK in only availble for x86, and there are some hints to expand platforms support. Meanwhile, developers are hoping to have ARM processor support since many of IoT applications rely on ARM processor. However, this may be not possible since the stick is an Intel product.

This stick is available for sale now, and costs $79. More information about how to get started with the stick is available on the Movidius developer site. Also check this video by Movidius:


Pulse Generator For Stepper Controller Using AD654

This stepper pulse generator project is an easy solution for stepper controller drive. It’s a very important tool and can be used to drive stepper in standalone mode. It generates square wave pulses in frequency range 0-50Khz. This project has multiple features which are a must for stepper controller. It has on board frequency generator with wide span of frequency, Slide switch for direction control and jumper for enable or disables the stepper controller. AD654 is heart of the project and its generate the pulse for stepper controller, output frequency 0-50Khz, higher frequency output is possible by changing CT capacitor value connected between pin 6 and 7. Refer to data sheet of AD654 for alteration.

Pulse Generator For Stepper Controller Using AD654 – [Link]