High-Side Current/Power Sensor

Microchip Technology recently introduced the PAC1921, a high-side current sensor with both a digital output, as well as a configurable analog output that can present power, current or voltage over the single output pin. Simultaneously, all power related output values are also available over the 2-Wire digital bus, which is compatible with I2C. The PAC1921 is available in a 10-lead 3 × 3 mm VDFN package. It was designed with the 2-Wire bus to maximize data and diagnostic reporting, while having the analog output to minimize data latency. The analog output can also be adjusted for use with 3-, 2-, 1.5-, or 1-V microcontroller inputs.Microchip PAC1921 Eval

The PAC1921 is ideal for networking, power-distribution, power-supply, computing and industrial-automation applications that cannot allow for latency when performing high-speed power management. A 39-bit accumulation register and 128 times gain configuration make this device ideal for both heavy and light system-load power measurement, from 0 to 32 V. It has the ability to integrate more than two seconds of power-consumption data. Additionally, the PAC1921 has a READ/INT pin for host control of the measurement period; and this pin can be used to synchronize readings of multiple devices.

The PAC1921 is supported by Microchip’s $64.99 PAC1921 High-Side Power and Current Monitor Evaluation Board (ADM00592). The PAC1921 is available for sampling and volume production, in a 10-lead 3 × 3 mm VDFN package, starting at $1.18 each in 5,000-unit quantities.

Source: Microchip Technology

Liquid Flow Sensor Wins Innovation Prize

Sensirion recently won the DeviceMed OEM-Components innovation prize at the Compamed 2014 exhibition. The disposable liquid flow sensor LD20-2000T for medical devices features an integrated thermal sensor element in a microchip. The pinhead-sized device is based on Sensirion’s CMOSens technology.sensirionliquidflowsensor

The LD20-2000T disposable liquid flow sensor provides liquid flow measurement capability from inside medical tubing (e.g., a catheter) in a low-cost sensor, suitable for disposable applications. As a result, you can measure drug delivery from an infusion set, an infusion pump, or other medical device in real time.

A microchip inside the disposable sensor measures the flow inside a fluidic channel. Accurate (~5%) flow rates from 0 to 420 ml/h and beyond can be measured. Inert medical-grade wetted materials ensure sterile operation with no contamination of the fluid. The straight, open flow channel with no moving parts provides high reliability. Using Sensirion’s CMOSens technology, the fully calibrated signal is processed and linearized on the 7.4 mm2 chip.

Source: Sensirion

New 8-Bit PICs for Sensor Applications

Microchip Technology recently expanded it’s PIC12/16LF155X 8-bit microcontroller family with the PIC16LF1554 and PIC16LF1559 (PIC16LF1554/9), which are targeted toward a variety of sensor applications. The PIC16LF1554/9 features two independent 10-bit, 100,000 samples per second ADCs with hardware Capacitive Voltage Divider (CVD) support for capacitive touch sensing.

Source: Microchip Techno

Source: Microchip Techno

Watch a short video:

The PIC16LF1554 MCUs are available now for sampling and production in 14-pin PDIP, TSSOP, SOIC, and 16-pin QFN (4 x 4 x .9 mm) packages. The PIC16LF1559 MCUs are available for sampling and production in 20-pin PDIP, SSOP, and QFN (4 x 4 x .9 mm) packages. Pricing starts at $0.63 each, in 10,000-unit quantities.

Source: Microchip Technology

High-Performance 4- to 20-mA Output Ultrasonic Sensor

MaxBotix’s new 4-20HR-MaxSonar-WR sensors are high-accuracy ultrasonic sensors featuring a 4- to 20-mA output. Each sensor is an affordable IP67-rated drop-in replacement for use with existing PLC/process control systems. The sensors reject outside noise sources and feature speed-of-sound temperature compensation.

Source: MaxBotix

Source: MaxBotix

The 4-20HR-MaxSonar-WR sensors provide range information from 50 to 500 cm and have a 1.6-mm resolution, an operational temperature range from –40° to 65°C (–40° to 149°F), real-time automatic calibration, a 200,000-plus hours MTBF, an operational voltage range from 12 to 32 V, and a low 20- to 40-mA average current requirement. The sensors function well with multiple sensors in the same location and they are RoHS- and CE-compliant.

A six-pin screw terminal header is included to simplify system connections for quick installation in applications such as: tank level measurement, tide/water level monitoring, solar/battery powered applications, industrial automation and outdoor vehicle detection.

The 4-20HR-MaxSonar-WR sensors (and previous IP67 MaxBotix sensors) are manufactured in a variety of packages for easy mounting in existing fittings. The sensors are available in M30x1.5, 1″ BSPP, 1″ NPTS, and 0.75″ NPTS PVC pipe fittings.

Pricing starts at $199.95 each and $134.37 in 100-unit quantities.

Source: MaxBotix, Inc.

Ultra-Compact Ultrasonic Sensor Series

MaxbotixThe UCXL-MaxSonar-WR series of sensors are flexible, OEM-customizable products that can be integrated into a system with MaxBotix’s horns or flush-mounted into an existing housing. Mounting design recommendations are provided through MaxBotix’s 3-D CAD models (available in multiple formats) to facilitate your design process. The sensor layout offers four conveniently placed mounting holes for design flexibility.

The rugged, high performance sensors are individually calibrated and feature a 1-cm resolution, an operational temperature range from –40˚C to 70˚C, real-time automatic calibration (voltage, humidity, and ambient noise), 200,000+ h mean time between failures (MTBF), and an operational 3-to-5.5-V voltage range with a low 3.4-mA average current requirement.

Contact MaxBotix for pricing.

MaxBotix, Inc.
www.maxbotix.com

MCU-Based Experimental Glider with GPS Receiver

When Jens Altenburg found a design for a compass-controlled glider in a 1930s paperback, he was inspired to make his own self-controlled model aircraft (see Photo 1)

Photo 1: This is the cover of an old paperback with the description of the compass-controlled glider. The model aircraft had a so-called “canard” configuration―a very modern design concept. Some highly sophisticated fighter planes are based on the same principle. (Photo used with permission of Ravensburger.)

Photo 1: This is the cover of an old paperback with the description of the compass-controlled glider. The model aircraft had a so-called “canard” configuration―a very modern design concept. Some highly sophisticated fighter planes are based on the same principle. (Photo used with permission of Ravensburger.)

His excellent article about his high-altitude, low-cost (HALO) experimental glider appears in Circuit Cellar’s April issue. The MCU-based glider includes a micro-GPs receiver and sensors and can climb to a preprogrammed altitude and find its way back home to a given coordinate.

Altenburg, a professor at the University of Applied Sciences Bingen in Germany, added more than a few twists to the 80-year-old plan. An essential design tool was the Reflex-XTR flight simulation software he used to trim his 3-D glider plan and conduct simulated flights.

Jens also researched other early autopilots, including the one used by the Fiesler Fi 103R German V-1 flying bomb. Known as buzz bombs during World War II, these rough predecessors of the cruise missile were launched against London after D-Day. Fortunately, they were vulnerable to anti-aircraft fire, but their autopilots were nonetheless mechanical engineering masterpieces (see Figure 1)

“Equipped with a compass, a single-axis gyro, and a barometric pressure sensor, the Fiesler Fi 103R German V-1 flying bomb flew without additional control,” Altenburg says. “The compass monitored the flying direction in general, the barometer controlled the altitude, and the gyro responded to short-duration disturbances (e.g., wind gusts).”

Figure 1: These are the main components of the Fieseler Fi 103R German V-1 flying bomb. The flight controller was designed as a mechanical computer with a magnetic compass and barometric pressure sensor for input. Short-time disturbances were damped with the main gyro (gimbal mounted) and two auxiliary gyros (fixed in one axis). The “mechanical” computer was pneumatically powered. The propeller log on top of the bomb measured the distance to the target.

Figure 1: These are the main components of the Fieseler Fi 103R German V-1 flying bomb. The flight controller was designed as a mechanical computer with a magnetic compass and barometric pressure sensor for input. Short-time disturbances were damped with the main gyro (gimbal mounted) and two auxiliary gyros (fixed in one axis). The “mechanical” computer was pneumatically powered. The propeller log on top of the bomb measured the distance to the target.

Altenburg adapted some of the V-1’s ideas into the flight control system for his 21st century autopilot glider. “All the Fi 103R board system’s electromechanical components received an electronic counterpart,” he says. “I replaced the mechanical gyros, the barometer, and the magnetic compass with MEMS. But it’s 2014, so I extended the electronics with a telemetry system and a GPS sensor.” (See Figure 2)

Figure 2: This is the flight controller’s block structure. The main function blocks are GPS, CPU, and power. GPS data is processed as a control signal for the servomotor.

Figure 2: This is the flight controller’s block structure. The main function blocks are GPS, CPU, and power. GPS data is processed as a control signal for the servomotor.

His article includes a detailed description of his glider’s flight-controller hardware, including the following:

Highly sophisticated electronics are always more sensitive to noise, power loss, and so forth. As discussed in the first sections of this article, a glider can be controlled by only a magnetic compass, some coils, and a battery. What else had to be done?

I divided the electronic system into different boards. The main board contains only the CPU and the GPS sensor. I thought that would be sufficient for basic functions. The magnetic and pressure sensor can be connected in case of extra missions. The telemetry unit is also a separate PCB.

Figure 3 shows the main board. Power is provided by a CR2032 lithium coin-cell battery. Two low-dropout linear regulators support the hardware with 1.8 and 2.7 V. The 1.8-V line is only for the GPS sensor. The second power supply provides the CPU with a stable voltage. The 2.7 V is the lowest voltage for the CPU’s internal ADC.

It is extremely important for the entire system to save power. Consequently, the servomotor has a separate power switch (Q1). As long as rudder movement isn’t necessary, the servomotor is powered off. The servomotor’s gear has enough drag to hold the rudder position without electrical power. The servomotor’s control signal is exactly the same as usually needed. It has a 1.1-to-2.1-ms pulse time range with about a 20-ms period. Two connectors (JP9 and JP10) are available for the extension boards (compass and telemetry)..

I used an STMicroelectronics LSM303DLM, which is a sensor module with a three-axis magnetometer and three-axis accelerometer. The sensor is connected by an I2C bus. The Bosch Sensortec BMP085 pressure sensor uses the same bus.

For telemetry, I applied an AXSEM AX5043 IC, which is a complete, narrow-band transceiver for multiple standards. The IC has an excellent link budget, which is the difference between output power in Transmit mode and input sensitivity in Receive mode. The higher the budget, the longer the transmission distance.

The AX5043 is also optimized for battery-powered applications. For modest demands, a standard crystal (X1, 16-MHz) is used for clock generation. In case of higher requirements, a temperature-compensated crystal oscillator (TCXO) is recommended.

The main board’s hardware with a CPU and a GPS sensor is shown. A CR2032 lithium coin-cell battery supplies the power. Two regulators provide 1.8  and 2.7 V for the GPS and the CPU. The main outputs are the servomotor’s signal and power switch.

Figure 3: The main board’s hardware with a CPU and a GPS sensor is shown. A CR2032 lithium coin-cell battery supplies the power. Two regulators provide 1.8 and 2.7 V for the GPS and the CPU. The main outputs are the servomotor’s signal and power switch.

Altenburg’s article also walks readers through the mathematical calculations needed to provide longitude, latitude, and course data to support navigation and the CPU’s most important sensor— the u-blox Fastrax UC430 GPS. He also discusses his experience using the Renesas Electronics R5F100AA microcontroller to equip the prototype board. (Altenburg’s glider won honorable mention in the 2012 Renesas RL78 Green Energy Challenge, see Photos 2 and 3).

The full article is in the April issue, now available for download by members or single-issue purchase.

One of the final steps is mounting the servomotor for rudder control. Thin cords connect the servomotor horn and the rudder. Two metal springs balance mechanical tolerances.

Photo 2: One of the final steps is mounting the servomotor for rudder control. Thin cords connect the servomotor horn and the rudder. Two metal springs balance mechanical tolerances.

Photo 2: This is the well-equipped high-altitude low-cost (HALO) experimental glider.

Photo 3: This is the well-equipped high-altitude low-cost (HALO) experimental glider.

The Future of Small Radar Technology

Directing the limited resources of Fighter Command to intercept a fleet of Luftwaffe bombers en route to London or accurately engaging the Imperial Navy at 18,000 yards in the dead of night. This was our grandfather’s radar, the technology that evened the odds in World War II.

This is the combat information center aboard a World War II destroyer with two radar displays.

This is the combat information center aboard a World War II destroyer with two radar displays.

Today there is an insatiable demand for short-range sensors (i.e., small radar technology)—from autonomous vehicles to gaming consoles and consumer devices. State-of-the-art sensors that can provide full 3-D mapping of a small-target scenes include laser radar and time-of-flight (ToF) cameras. Less expensive and less accurate acoustic and infrared devices sense proximity and coarse angle of arrival. The one sensor often overlooked by the both the DIY and professional designer is radar.

However, some are beginning to apply small radar technology to solve the world’s problems. Here are specific examples:

Autonomous vehicles: In 2007, the General Motors and Carnegie Mellon University Tartan Racing team won the Defense Advanced Research Projects Agency (DARPA) Urban Challenge, where autonomous vehicles had to drive through a city in the shortest possible time period. Numerous small radar devices aided in their real-time decision making. Small radar devices will be a key enabling technology for autonomous vehicles—from self-driving automobiles to unmanned aerial drones.

Consumer products: Recently, Massachusetts Institute of Technology (MIT) researchers developed a radar sensor for gaming systems, shown to be capable of detecting gestures and other complex movements inside a room and through interior walls. Expect small radar devices to play a key role in enabling user interface on gaming consoles to smartphones.

The Internet of Things (IoT): Fybr is a technology company that uses small radar sensors to detect the presence of parked automobiles, creating the most accurate parking detection system in the world for smart cities to manage parking and traffic congestion in real time. Small radar sensors will enable the IoT by providing accurate intelligence to data aggregators.

Automotive: Small radar devices are found in mid- to high-priced automobiles in automated cruise control, blind-spot detection, and parking aids. Small radar devices will soon play a key role in automatic braking, obstacle-avoidance systems, and eventually self-driving automobiles, greatly increasing passenger safety.

Through-Wall Imaging: Advances in small radar have numerous possible military applications, including recent MIT work on through-wall imaging of human targets through solid concrete walls. Expect more military uses of small radar technology.

What is taking so long? A tremendous knowledge gap exists between writing the application and emitting, then detecting, scattered microwave fields and understanding the result. Radar was originally developed by physicists who had a deep understanding of electromagnetics and were interested in the theory of microwave propagation and scattering. They created everything from scratch, from antennas to specialized vacuum tubes.

Microwave tube development, for example, required a working knowledge of particle physics. Due to this legacy, radar textbooks are often intensely theoretical. Furthermore, microwave components were very expensive—handmade and gold-plated. Radar was primarily developed by governments and the military, which made high-dollar investments for national security.

Small radar devices such as the RFBeam Microwave K-LC1a radio transceiver cost less than $10 when purchased in quantity.

Small radar devices such as the RFBeam Microwave K-LC1a radio transceiver cost less than $10 when purchased in quantity.

It’s time we make radar a viable option for DIY projects and consumer devices by developing low-cost, easy-to-use, capable technology and bridging the knowledge gap!
Today you can buy small radar sensors for less than $10. Couple this with learning practical radar processing methods, and you can solve a critical sensing problem for your project.

Learn by doing. I created the MIT short-course “Build a Small Radar Sensor,” where students learn about radar by building a device from scratch. Those interested can take the online course for free through MIT Opencourseware or enroll in the five-day MIT Professional Education course.

Dive deeper. My soon-to-be published multimedia book, Small and Short-Range Radar Systems, explains the principles and building of numerous small radar devices and then demonstrates them so readers at all levels can create their own radar devices or learn how to use data from off-the-shelf radar sensors.

This is just the beginning. Soon small radar sensors will be everywhere.

Using Arduino for Prototypes (EE Tip #121)

Arduino is an open-source development kit with a cult following. Open source means the software and hardware design files are available for free download. This begs the question of how the Arduino team can turn a profit, and the answer is the trademark and reputation of the Arduino name and symbol.

Arduino Uno PosterWhile there are now many Arduino clones, the original Arduino boards still outperform most. Arduino is very useful for prototyping. A recent example in my own work is adding a gyroscope sensor to a project. First, I purchased a gyroscope board from Pololu for a small amount. I plugged it into an Arduino breadboard shield purchased from eBay for roughly $5, and wired up the four pins: VCC (3.3 V), GND, SCL, and SDA. Pololu’s website has a link to some demo firmware and I downloaded this from GitHub. The library folders were extracted and renamed according to the instructions and then the example was run. The Arduino serial monitor then showed the gyroscope data in real-time, and the entire process took no more than 30 minutes.

Editor’s note: This EE Tip was written by Fergus Dixon of Sydney, Australia. Dixon runs Electronic System Design, a website set up to promote easy to use and inexpensive development kits. The Arduino Uno pictured above is a small portion of a full Arduino blueprint poster available for free download here.

Build an Inexpensive Wireless Water Alarm

The best DIY electrical engineering projects are effective, simple, and inexpensive. Devlin Gualtieri’s design of a wireless water alarm, which he describes in Circuit Cellar’s February issue, meets all those requirements.

Like most homeowners, Gualtieri has discovered water leaks in his northern New Jersey home after the damage has already started.

“In all cases, an early warning about water on the floor would have prevented a lot of the resulting damage,” he says.

You can certainly buy water alarm systems that will alert you to everything from a leak in a well-water storage tank to moisture from a cracked boiler. But they typically work with proprietary and expensive home-alarm systems that also charge a monthly “monitoring” fee.

“As an advocate of free and open-source software, it’s not surprising that I object to such schemes,” Gualtieri says.

In February’s Circuit Cellar magazine, now available for membership download or single-issue purchase, Gualtieri describes his battery-operated water alarm. The system, which includes a number of wireless units that signal a single receiver, includes a wireless receiver, audible alarm, and battery monitor to indicate low power.

Photo 1: An interdigital water detection sensor is shown. Alternate rows are lengths of AWG 22 copper wire, which is either bare or has its insulation removed. The sensor is shown mounted to the bottom of the box containing the water alarm circuitry. I attached it with double-stick foam tape, but silicone adhesive should also work.

Photo 1: An interdigital water detection sensor is shown. Alternate rows are lengths of AWG 22 copper wire, which is either bare or has its insulation removed. The sensor is shown mounted to the bottom of the box containing the water alarm circuitry. I attached it with double-stick foam tape, but silicone adhesive should also work.

Because water conducts electricity, Gualtieri sensors are DIY interdigital electrodes that can lie flat on a surface to detect the first presence of water. And their design couldn’t be easier.

“You can simply wind two parallel coils of 22 AWG wire on a perforated board about 2″ by 4″, he says. (See Photo 1.)

He also shares a number of design “tricks,” including one he used to make his low-battery alert work:

“A battery monitor is an important feature of any battery-powered alarm circuit. The Microchip Technology PIC12F675 microcontroller I used in my alarm circuit has 10-bit ADCs that can be optionally assigned to the I/O pins. However, the problem is that the reference voltage for this conversion comes from the battery itself. As the battery drains from 100% downward, so does the voltage reference, so no voltage change would be registered.

Figure 1: This is the portion of the water alarm circuit used for the battery monitor. The series diodes offer a 1.33-V total  drop, which offers a reference voltage so the ADC can see changes in the battery voltage.

Figure 1: This is the portion of the water alarm circuit used for the battery monitor. The series diodes offer a 1.33-V total drop, which offers a reference voltage so the ADC can see changes in the battery voltage.

“I used a simple mathematical trick to enable battery monitoring. Figure 1 shows a portion of the schematic diagram. As you can see, the analog input pin connects to an output pin, which is at the battery voltage when it’s high through a series connection of four small signal diodes (1N4148). The 1-MΩ resistor in series with the diodes limits their current to a few microamps when the output pin is energized. At such low current, the voltage drop across each diode is about 0.35 V. An actual measurement showed the total voltage drop across the four diodes to be 1.33 V.

“This voltage actually presents a new reference value for my analog conversion. The analog conversion now provides the following digital values:

EQ1Table 1 shows the digital values as a function of battery voltage. The nominal voltage of three alkaline cells is 4.75 V. The nominal voltage of three lithium cells is 5.4 V. The PIC12F675 functions from approximately 2 to 6.5 V, but the wireless transmitter needs as much voltage as possible to generate a reliable signal. I arbitrarily coded the battery alarm at 685, or a little above 4 V. That way, there’s still enough power to energize the wireless transmitter at a useful power level.”

Table 1
Battery Voltage ADC Value
5 751
4.75 737
4.5 721
4.24 704
4 683
3.75 661

 

Gaultieri’s wireless transmitter, utilizing lower-frequency bands, is also straightforward.

Photo 2 shows one of the transmitter modules I used in my system,” he says. “The round device is a surface acoustic wave (SAW) resonator. It just takes a few components to transform this into a low-power transmitter operable over a wide supply voltage range, up to 12 V. The companion receiver module is also shown. My alarm has a 916.5-MHz operating frequency, but 433 MHz is a more popular alarm frequency with many similar modules.”

These transmitter and receiver modules are used in the water alarm. The modules operate at 916.5 MHz, but 433 MHz is a more common alarm frequency with similar modules. The scale is inches.

Photo 2: These transmitter and receiver modules are used in the water alarm. The modules operate at 916.5 MHz, but 433 MHz is a more common alarm frequency with similar modules. The scale is inches.

Gualtieri goes on to describe the alarm circuitry (see Photo 3) and receiver circuit (see Photo 4.)

For more details on this easy and affordable early-warning water alarm, check out the February issue.

Photo 3: This is the water alarm’s interior. The transmitter module with its antenna can be seen in the upper right. The battery holder was harvested from a $1 LED flashlight. The box is 2.25“ × 3.5“, excluding the tabs.

Photo 3: This is the water alarm’s interior. The transmitter module with its antenna can be seen in the upper right. The battery holder was harvested from a $1 LED flashlight. The box is 2.25“ × 3.5“, excluding the tabs.

Photo 4: Here is my receiver circuit. One connector was used to monitor the signal strength voltage during development. The other connector feeds an input on a home alarm system. The short antenna reveals its 916.5-MHz operating frequency. Modules with a 433-MHz frequency will have a longer antenna.

Photo 4: Here is my receiver circuit. One connector was used to monitor the signal strength voltage during development. The other connector feeds an input on a home alarm system. The short antenna reveals its 916.5-MHz operating frequency. Modules with a 433-MHz frequency will have a longer antenna.

 

Q&A: Scott Garman, Technical Evangelist

Scott Garman is more than just a Linux software engineer. He is also heavily involved with the Yocto Project, an open-source collaboration that provides tools for the embedded Linux industry. In 2013, Scott helped Intel launch the MinnowBoard, the company’s first open-hardware SBC. —Nan Price, Associate Editor

Scott Garman

Scott Garman

NAN: Describe your current position at Intel. What types of projects have you developed?

SCOTT: I’ve worked at Intel’s Open Source Technology Center for just about four years. I began as an embedded Linux software engineer working on the Yocto Project and within the last year, I moved into a technical evangelism role representing Intel’s involvement with the MinnowBoard.

Before working at Intel, my background was in developing audio products based on embedded Linux for both consumer and industrial markets. I also started my career as a Linux system administrator in academic computing for a particle physics group.

Scott was involved with an Intel MinnowBoard robotics and computer vision demo, which took place at LinuxCon Japan in May 2013.

Scott was involved with an Intel MinnowBoard robotics and computer vision demo, which took place at LinuxCon Japan in May 2013.

I’m definitely a generalist when it comes to working with Linux. I tend to bounce around between things that don’t always get the attention they need, whether it is security, developer training, or community outreach.

More specifically, I’ve developed and maintained parallel computing clusters, created sound-level management systems used at concert stadiums, worked on multi-room home audio media servers and touchscreen control systems, dug into the dark areas of the Autotools and embedded Linux build systems, and developed fun conference demos involving robotics and computer vision. I feel very fortunate to be involved with embedded Linux at this point in history—these are very exciting times!

Scott is shown working on an Intel MinnowBoard demo, which was built around an OWI Robotic Arm.

Scott is shown working on an Intel MinnowBoard demo, which was built around an OWI Robotic Arm.

NAN: Can you tell us a little more about your involvement with the Yocto Project (www.yoctoproject.org)?

SCOTT: The Yocto Project is an effort to reduce the amount of fragmentation in the embedded Linux industry. It is centered on the OpenEmbedded build system, which offers a tremendous amount of flexibility in how you can create embedded Linux distros. It gives you the ability to customize nearly every policy of your embedded Linux system, such as which compiler optimizations you want or which binary package format you need to use. Its killer feature is a layer-based architecture that makes it easy to reuse your code to develop embedded applications that can run on multiple hardware platforms by just swapping out the board support package (BSP) layer and issuing a rebuild command.

New releases of the build system come out twice a year, in April and October.

Here, the OWI Robotic Arm is being assembled.

Here, the OWI Robotic Arm is being assembled.

I’ve maintained various user space recipes (i.e., software components) within OpenEmbedded (e.g., sudo, openssh, etc.). I’ve also made various improvements to our emulation environment, which enables you to run QEMU and test your Linux images without having to install it on hardware.

I created the first version of a security tracking system to monitor Common Vulnerabilities and Exposures (CVE) reports that are relevant to recipes we maintain. I also developed training materials for new developers getting started with the Yocto Project, including a very popular introductory screencast “Getting Started with the Yocto Project—New Developer Screencast Tutorial

NAN: Intel recently introduced the MinnowBoard SBC. Describe the board’s components and uses.

SCOTT: The MinnowBoard is based on Intel’s Queens Bay platform, which pairs a Tunnel Creek Atom CPU (the E640 running at 1 GHz) with the Topcliff Platform controller hub. The board has 1 GB of RAM and includes PCI Express, which powers our SATA disk support and gigabit Ethernet. It’s an SBC that’s well suited for embedded applications that can use that extra CPU and especially I/O performance.

Scott doesn’t have a dedicated workbench or garage. He says he tends to just clear off his desk, lay down some cardboard, and work on things such as the Trippy RGB Waves Kit, which is shown.

Scott doesn’t have a dedicated workbench or garage. He says he tends to just clear off his desk, lay down some cardboard, and work on things such as the Trippy RGB Waves Kit, which is shown.

The MinnowBoard also has the embedded bus standards you’d expect, including GPIO, I2C, SPI, and even CAN (used in automotive applications) support. We have an expansion connector on the board where we route these buses, as well as two lanes of PCI Express for custom high-speed I/O expansion.

There are countless things you can do with MinnowBoard, but I’ve found it is especially well suited for projects where you want to combine embedded hardware with computing applications that benefit from higher performance (e.g., robots that use computer vision, as a central hub for home automation projects, networked video streaming appliances, etc.).

And of course it’s open hardware, which means the schematics, Gerber files, and other design files are available under a Creative Commons license. This makes it attractive for companies that want to customize the board for a commercial product; educational environments, where students can learn how boards like this are designed; or for those who want an open environment to interface their hardware projects.

I created a MinnowBoard embedded Linux board demo involving an OWI Robotic Arm. You can watch a YouTube video to see how it works.

NAN: What compelled Intel to make the MinnowBoard open hardware?

SCOTT: The main motivation for the MinnowBoard was to create an affordable Atom-based development platform for the Yocto Project. We also felt it was a great opportunity to try to release the board’s design as open hardware. It was exciting to be part of this, because the MinnowBoard is the first Atom-based embedded board to be released as open hardware and reach the market in volume.

Open hardware enables our customers to take the design and build on it in ways we couldn’t anticipate. It’s a concept that is gaining traction within Intel, as can be seen with the announcement of Intel’s open-hardware Galileo project.

NAN: What types of personal projects are you working on?

SCOTT: I’ve recently gone on an electronics kit-building binge. Just getting some practice again with my soldering iron with a well-paced project is a meditative and restorative activity for me.

Scott’s Blinky POV Kit is shown. “I don’t know what I’d do without my PanaVise Jr. [vise] and some alligator clips,” he said.

Scott’s Blinky POV Kit is shown. “I don’t know what I’d do without my PanaVise Jr. [vise] and some alligator clips,” he said.

I worked on one project, the Trippy RGB Waves Kit, which includes an RGB LED and is controlled by a microcontroller. It also has an IR sensor that is intended to detect when you wave your hand over it. This can be used to trigger some behavior of the RGB LED (e.g., cycling the colors). Another project, the Blinky POV Kit, is a row of LEDs that can be programmed to create simple text or logos when you wave the device around, using image persistence.

Below is a completed JeeNode v6 Kit Scott built one weekend.

Below is a completed JeeNode v6 Kit Scott built one weekend.

My current project is to add some wireless sensors around my home, including temperature sensors and a homebrew security system to monitor when doors get opened using 915-MHz JeeNodes. The JeeNode is a microcontroller paired with a low-power RF transceiver, which is useful for home-automation projects and sensor networks. Of course the central server for collating and reporting sensor data will be a MinnowBoard.

NAN: Tell us about your involvement in the Portland, OR, open-source developer community.

SCOTT: Portland has an amazing community of open-source developers. There is an especially strong community of web application developers, but more people are hacking on hardware nowadays, too. It’s a very social community and we have multiple nights per week where you can show up at a bar and hack on things with people.

This photo was taken in the Open Source Bridge hacker lounge, where people socialize and collaborate on projects. Here someone brought a brainwave-control game. The players are wearing electroencephalography (EEG) readers, which are strapped to their heads. The goal of the game is to use biofeedback to move the floating ball to your opponent’s side of the board.

This photo was taken in the Open Source Bridge hacker lounge, where people socialize and collaborate on projects. Here someone brought a brainwave-control game. The players are wearing electroencephalography (EEG) readers, which are strapped to their heads. The goal of the game is to use biofeedback to move the floating ball to your opponent’s side of the board.

I’d say it’s a novelty if I wasn’t so used to it already—walking into a bar or coffee shop and joining a cluster of friendly people, all with their laptops open. We have coworking spaces, such as Collective Agency, and hackerspaces, such as BrainSilo and Flux (a hackerspace focused on creating a welcoming space for women).

Take a look at Calagator to catch a glimpse of all the open-source and entrepreneurial activity going on in Portland. There are often multiple events going on every night of the week. Calagator itself is a Ruby on Rails application that was frequently developed at the bar gatherings I referred to earlier. We also have technical conferences ranging from the professional OSCON to the more grassroots and intimate Open Source Bridge.

I would unequivocally state that moving to Portland was one of the best things I did for developing a career working with open-source technologies, and in my case, on open-source projects.

Arduino MOSFET-Based Power Switch

Circuit Cellar columnist Ed Nisley has used Arduino SBCs in many projects over the years. He has found them perfect for one-off designs and prototypes, since the board’s all-in-one layout includes a micrcontroller with USB connectivity, simple connectors, and a power regulator.

But the standard Arduino presents some design limitations.

“The on-board regulator can be either a blessing or a curse, depending on the application. Although the board will run from an unregulated supply and you can power additional circuitry from the regulator, the minute PCB heatsink drastically limits the available current,” Nisley says. “Worse, putting the microcontroller into one of its sleep modes doesn’t shut off the rest of the Arduino PCB or your added circuits, so a standard Arduino board isn’t suitable for battery-powered applications.”

In Circuit Cellar’s January issue, Nisley presents a MOSFET-based power switch that addresses such concerns. He also refers to one of his own projects where it would be helpful.

“The low-resistance Hall effect current sensor that I described in my November 2013 column should be useful in a bright bicycle taillight, but only if there’s a way to turn everything off after the ride without flipping a mechanical switch…,” Nisley says. “Of course, I could build a custom microcontroller circuit, but it’s much easier to drop an Arduino Pro Mini board atop the more interesting analog circuitry.”

Nisley’s January article describes “a simple MOSFET-based power switch that turns on with a push button and turns off under program control: the Arduino can shut itself off and reduce the battery drain to nearly zero.”

Readers should find the article’s information and circuitry design helpful in other applications requiring automatic shutoff, “even if they’re not running from battery power,” Nisley says.

Figure 1: This SPICE simulation models a power p-MOSFET with a logic-level gate controlling the current from the battery to C1 and R2, which simulate a 500-mA load that is far below Q2’s rating. S1, a voltage-controlled switch, mimics an ordinary push button. Q1 isolates the Arduino digital output pin from the raw battery voltage.

Figure 1: This SPICE simulation models a power p-MOSFET with a logic-level gate controlling the current from the battery to C1 and R2, which simulate a 500-mA load that is far below Q2’s rating. S1, a voltage-controlled switch, mimics an ordinary push button. Q1 isolates the Arduino digital output pin from the raw battery voltage.

The article takes readers from SPICE modeling of the circuitry (see Figure 1) through developing a schematic and building a hardware prototype.

“The PCB in Photo 1 combines the p-MOSFET power switch from Figure 2 with a Hall effect current sensor, a pair of PWM-controlled n-MOFSETs, and an Arduino Pro Mini into

The power switch components occupy the upper left corner of the PCB, with the Hall effect current sensor near the middle and the Arduino Pro Mini board to the upper right. The 3-D printed red frame stiffens the circuit board during construction.

Photo 1: The power switch components occupy the upper left corner of the PCB, with the Hall effect current sensor near the middle and the Arduino Pro Mini board to the upper right. The 3-D printed red frame stiffens the circuit board during construction.

a brassboard layout,” Nisley says. “It’s one step beyond the breadboard hairball I showed in my article “Low-Loss Hall Effect Current Sensing” (Circuit Cellar 280, 2013), and will help verify that all the components operate properly on a real circuit board with a good layout.”

For much more detail about the verification process, PCB design, Arduino interface, and more, download the January issue.

The actual circuit schematic includes the same parts as the SPICE schematic, plus the assortment of connectors and jumpers required to actually build the PCB shown in Photo 1.

Figure 2: The actual circuit schematic includes the same parts as the SPICE schematic, as well as the assortment of connectors and jumpers required to actually build the PCB shown in Photo 1.

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Contact: Elizabeth Presson
elizabeth.presson@digi.com

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A Look at Low-Noise Amplifiers

Maurizio Di Paolo Emilio, who has a PhD in Physics, is an Italian telecommunications engineer who works mainly as a software developer with a focus on data acquisition systems. Emilio has authored articles about electronic designs, data acquisition systems, power supplies, and photovoltaic systems. In this article, he provides an overview of what is generally available in low-noise amplifiers (LNAs) and some of the applications.

By Maurizio Di Paolo Emilio
An LNA, or preamplifier, is an electronic amplifier used to amplify sometimes very weak signals. To minimize signal power loss, it is usually located close to the signal source (antenna or sensor). An LNA is ideal for many applications including low-temperature measurements, optical detection, and audio engineering. This article presents LNA systems and ICs.

Signal amplifiers are electronic devices that can amplify a relatively small signal from a sensor (e.g., temperature sensors and magnetic-field sensors). The parameters that describe an amplifier’s quality are:

  • Gain: The ratio between output and input power or amplitude, usually measured in decibels
  • Bandwidth: The range of frequencies in which the amplifier works correctly
  • Noise: The noise level introduced in the amplification process
  • Slew rate: The maximum rate of voltage change per unit of time
  • Overshoot: The tendency of the output to swing beyond its final value before settling down

Feedback amplifiers combine the output and input so a negative feedback opposes the original signal (see Figure 1). Feedback in amplifiers provides better performance. In particular, it increases amplification stability, reduces distortion, and increases the amplifier’s bandwidth.

 Figure 1: A feedback amplifier model is shown here.


Figure 1: A feedback amplifier model is shown.

A preamplifier amplifies an analog signal, generally in the stage that precedes a higher-power amplifier.

IC LOW-NOISE PREAMPLIFIERS
Op-amps are widely used as AC amplifiers. Linear Technology’s LT1028 or LT1128 and Analog Devices’s ADA4898 or AD8597 are especially suitable ultra-low-noise amplifiers. The LT1128 is an ultra-low-noise, high-speed op-amp. Its main characteristics are:

  • Noise voltage: 0.85 nV/√Hz at 1 kHz
  • Bandwidth: 13 MHz
  • Slew rate: 5 V/µs
  • Offset voltage: 40 µV

Both the Linear Technology and Analog Devices amplifiers have voltage noise density at 1 kHz at around 1 nV/√Hz  and also offer excellent DC precision. Texas Instruments (TI)  offers some very low-noise amplifiers. They include the OPA211, which has 1.1 nV/√Hz  noise density at a  3.6 mA from 5 V supply current and the LME49990, which has very low distortion. Maxim Integrated offers the MAX9632 with noise below 1nV/√Hz.

The op-amp can be realized with a bipolar junction transistor (BJT), as in the case of the LT1128, or a MOSFET, which works at higher frequencies and with a higher input impedance and a lower energy consumption. The differential structure is used in applications where it is necessary to eliminate the undesired common components to the two inputs. Because of this, low-frequency and DC common-mode signals (e.g., thermal drift) are eliminated at the output. A differential gain can be defined as (Ad = A2 – A1) and a common-mode gain can be defined as (Ac = A1 + A2 = 2).

An important parameter is the common-mode rejection ratio (CMRR), which is the ratio of common-mode gain to the differential-mode gain. This parameter is used to measure the  differential amplifier’s performance.

Figure 2: The design of a simple preamplifier is shown. Its main components are the Linear Technology LT112 and the Interfet IF3602 junction field-effect transistor (JFET).

Figure 2: The design of a simple preamplifier is shown. Its main components are the Linear Technology LT1128 and the Interfet IF3602 junction field-effect transistor (JFET).

Figure 2 shows a simple preamplifier’s design with 0.8 nV/√Hz at 1 kHz background noise. Its main components are the LT1128 and the Interfet IF3602 junction field-effect transistor (JFET).  The IF3602 is a dual Nchannel JFET used as stage for the op-amp’s input. Figure 3 shows the gain and Figure 4 shows the noise response.

Figure 3: The gain of a low-noise preamplifier.

Figure 3: The is a low-noise preamplifier’s gain.

 

Figure 4: The noise response of a low-noise preamplifier

Figure 4: A low-noise preamplifier’s noise response is shown.

LOW NOISE PREAMPLIFIER SYSTEMS
The Stanford Research Systems SR560 low-noise voltage preamplifier has a differential front end with 4nV/√Hz input noise and a 100-MΩ input impedance (see Photo 1a). Input offset nulling is accomplished by a front-panel potentiometer, which is accessible with a small screwdriver. In addition to the signal inputs, a rear-panel TTL blanking input enables you to quickly turn the instrument’s gain on and off (see Photo 1b).

Photo 1a:The Stanford Research Systems SR560 low-noise voltage preamplifier

Photo 1a: The Stanford Research Systems SR560 low-noise voltage preamplifier. (Photo courtesy of Stanford Research Systems)

Photo 1 b: A rear-panel TTL blanking input enables you to quickly turn the Stanford Research Systems SR560 gain on and off.

Photo 1b: A rear-panel TTL blanking input enables you to quickly turn the Stanford Research Systems SR560 gain on and off. (Photo courtesy of Stanford Research Systems)

The Picotest J2180A low-noise preamplifier provides a fixed 20-dB gain while converting a 1-MΩ input impedance to a 50-Ω output impedance and 0.1-Hz to 100-MHz bandwidth (see Photo 2). The preamplifier is used to improve the sensitivity of oscilloscopes, network analyzers, and spectrum analyzers while reducing the effective noise floor and spurious response.

Photo 2: The Picotest J2180A low-noise preamplifier is shown.

Photo 2: The Picotest J2180A low-noise preamplifier is shown. (Photo courtesy of picotest.com)

Signal Recovery’s Model 5113 is among the best low-noise preamplifier systems. Its principal characteristics are:

  • Single-ended or differential input modes
  • DC to 1-MHz frequency response
  • Optional low-pass, band-pass, or high-pass signal channel filtering
  • Sleep mode to eliminate digital noise
  • Optically isolated RS-232 control interface
  • Battery or line power

The 5113 (see Photo 3 and Figure 5) is used in applications as diverse as radio astronomy, audiometry, test and measurement, process control, and general-purpose signal amplification. It’s also ideally suited to work with a range of lock-in amplifiers.

Photo 3: This is the Signal Recovery Model 5113 low-noise pre-amplifier.

Photo 3: This is the Signal Recovery Model 5113 low-noise preamplifier. (Photo courtesy of Signal Recovery)

Figure 5: Noise contour figures are shown for the Signal Recovery Model 5113.

Figure 5: Noise contour figures are shown for the Signal Recovery Model 5113.

WRAPPING UP
This article briefly introduced low-noise amplifiers, in particular IC system designs utilized in simple or more complex systems such as the Signal Recovery Model 5113, which is a classic amplifier able to obtain different frequency bands with relative gain. A similar device is the SR560, which is a high-performance, low-noise preamplifier that is ideal for a wide variety of applications including low-temperature measurements, optical detection, and audio engineering.

Moreover, the Krohn-Hite custom Models 7000 and 7008 low-noise differential preamplifiers provide a high gain amplification to 1 MHz with an AC output derived from a very-low-noise FET instrumentation amplifier.

One common LNA amplifier is a satellite communications system. The ground station receiving antenna will connect to an LNA, which is needed because the received signal is weak. The received signal is usually a little above background noise. Satellites have limited power, so they use low-power transmitters.

Telecommunications engineer Maurizio Di Paolo Emilio was born in Pescara, Italy. Working mainly as a software developer with a focus on data acquisition systems, he helped design the thermal compensation system (TCS) for the optical system used in the Virgo Experiment (an experiment for detecting gravitational waves). Maurizio currently collaborates with researchers at the University of L’Aquila on X-ray technology. He also develops data acquisition hardware and software for industrial applications and manages technical training courses. To learn more about Maurizio and his expertise, read his essay on “The Future of Data Acquisition Technology.”

Client Profile: Pololu Robotics

Pololu Robotics
www.pololu.com
920 Pilot Road
Las Vegas, NV 89119

Contact: inbox@pololu.com

Pololu Robotics Zumo

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Natural Human-Computer Interaction

Recent innovations in both hardware and software have brought on a new wave of interaction techniques that depart from mice and keyboards. The widespread adoption of smartphones and tablets with capacitive touchscreens shows people’s preference to directly manipulate virtual objects with their hands.

Going beyond touch-only interaction, the Microsoft Kinect sensor enables users to play

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus: https://project.eia-fr.ch/chairgest/Pages/Overview.aspx).

This shows the hand tracking result from Kinect data. The red regions are our tracking results and the green lines are the skeleton tracking results from the Kinect SDK (based on data from the ChAirGest corpus: https://project.eia-fr.ch/chairgest/Pages/Overview.aspx).

games with their entire body. More recently, Leap Motion’s new compact sensor, consisting of two cameras and three infrared LEDs, has opened up the possibility of accurate fingertip tracking. With Project Glass, Google is pioneering new technology in the wearable human-computer interface. Other new additions to wearable technology include Samsung’s Galaxy Gear Smartwatch and Apple’s rumored iWatch.

A natural interface reduces the learning curve, or the amount of time and energy a person requires to complete a particular task. Instead of a user learning to communicate with a machine through a programming language, the machine is now learning to understand the user.

Hardware advancements have led to our clunky computer boxes becoming miniaturized, stylish sci-fi-like phones and watches. Along with these shrinking computers come ever-smaller sensors that enable a once keyboard-constrained computer to listen, see, and feel. These developments pave the way to natural human-computer interfaces.
If sensors are like eyes and ears, software would be analogous to our brains.

Understanding human speech and gestures in real time is a challenging task for natural human-computer interaction. At a higher level, both speech and gesture recognition require similar processing pipelines that include data streaming from sensors, feature extraction, and pattern recognition of a time series of feature vectors. One of the main differences between the two is feature representation because speech involves audio data while gestures involve video data.

For gesture recognition, the first main step is locating the user’s hand. Popular libraries for doing this include Microsoft’s Kinect SDK or PrimeSense’s NITE library. However, these libraries only give the coordinates of the hands as points, so the actual hand shapes cannot be evaluated.

Fingertip tracking using a Kinect sensor. The green dots are the tracked fingertips.

Our team at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory has developed methods that use a combination of skin-color and motion detection to compute a probability map of gesture salience location. The gesture salience computation takes into consideration the amount of movement and the closeness of movement to the observer (i.e., the sensor).

We can use the probability map to find the most likely area of the gesturing hands. For each time frame, after extracting the depth data for the entire hand, we compute a histogram of oriented gradients to represent the hand shape as a more compact feature descriptor. The final feature vector for a time frame includes 3-D position, velocity, and hand acceleration as well as the hand shape descriptor. We also apply principal component analysis to reduce the feature vector’s final dimension.

A 3-D model of pointing gestures using a Kinect sensor. The top left video shows background subtraction, arm segmentation, and fingertip tracking. The top right video shows the raw depth-mapped data. The bottom left video shows the 3D model with the white plane as the tabletop, the green line as the arm, and the small red dot as the fingertip.

The next step in the gesture-recognition pipeline is to classify the feature vector sequence into different gestures. Many machine-learning methods have been used to solve this problem. A popular one is called the hidden Markov model (HMM), which is commonly used to model sequence data. It was earlier used in speech recognition with great success.

There are two steps in gesture classification. First, we need to obtain training data to learn the models for different gestures. Then, during recognition, we find the most likely model that can produce the given observed feature vectors. New developments in the area involve some variations in the HMM, such as using hierarchical HMM for real-time inference or using discriminative training to increase the recognition accuracy.

Ying Yin

Ying Yin is a PhD candidate and a Research Assistant at the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory. Originally from Suzhou, China, Ying received her BASc in Computer Engineering from the University of British Columbia in Vancouver, Canada, in 2008 and an MS in Computer Science from MIT in 2010. Her research focuses on applying machine learning and computer vision methods to multimodal human-computer interaction. Ying is also interested in web and mobile application development. She has won awards in web and mobile programming competitions at MIT.

Currently, the newest development in speech recognition at the industry scale is a method called deep learning. Earlier machine-learning methods require careful selection of feature vectors. The goal of deep learning is automatic discovery of powerful features from raw input data. So far, it has shown promising results in speech recognition. It can possibly be applied to gesture recognition to see whether it can further improve accuracy.

As component form factors shrink, sensor resolutions grow, and recognition algorithms become more accurate, natural human-computer interaction will become more and more ubiquitous in our everyday life.