TRACE32 Now Supports Xilinx MicroBlaze 8.50.C

LauterbachThe TRACE32 modular hardware and software supports up to 350 different CPUs. The microprocessor development tools now support the latest version of Xilinx’s MicroBlaze 8.50.c, which is a soft processor core designed for Xilinx FPGAs. The MicroBlaze core is included with Xilinx’s Vivado Design Edition and IDS Embedded Edition.

The TRACE32 tools have supported MicroBlaze for many years by providing efficient and user-friendly debugging at the C or C++ level using the on-chip JTAG interface. This interface also provides code download, flash programming, and quick access to all internal chip peripherals and registers.
Contact Lauterbach for pricing.

Lauterbach GmbH
www.lauterbach.com

Xilinx, Inc.
www.xilinx.com

Client Profile: Digi International, Inc

Contact: Elizabeth Presson
elizabeth.presson@digi.com

Featured Product: The XBee product family (www.digi.com/xbee) is a series of modular products that make adding wireless technology easy and cost-effective. Whether you need a ZigBee module or a fast multipoint solution, 2.4 GHz or long-range 900 MHz—there’s an XBee to meet your specific requirements.

XBee Cloud Kit

Digi International XBee Cloud Kit

Product information: Digi now offers the XBee Wi-Fi Cloud Kit (www.digi.com/xbeewificloudkit) for those who want to try the XBee Wi-Fi (XB2B-WFUT-001) with seamless cloud connectivity. The Cloud Kit brings the Internet of Things (IoT) to the popular XBee platform. Built around Digi’s new XBee Wi-Fi
module, which fully integrates into the Device Cloud by Etherios, the kit is a simple way for anyone with an interest in M2M and the IoT to build a hardware prototype and integrate it into an Internet-based application. This kit is suitable for electronics engineers, software designers, educators, and innovators.

Exclusive Offer: The XBee Wi-Fi Cloud Kit includes an XBee Wi-Fi module; a development board with a variety of sensors and actuators; loose electronic prototyping parts to make circuits of your own; a free subscription to Device Cloud; fully customizable widgets to monitor and control connected devices; an open-source application that enables two-way communication and control with the development board over the Internet; and cables, accessories, and everything needed to connect to the web. The Cloud Kit costs $149.

Small, Self-Contained GNSS Receiver

TM Series GNSS modules are self-contained, high-performance global navigation satellite system (GNSS) receivers designed for navigation, asset tracking, and positioning applications. Based on the MediaTek chipset, the receivers can simultaneously acquire and track several satellite constellations, including the US GPS, Europe’s GALILEO, Russia’s GLONASS, and Japan’s QZSS.

LinxThe 10-mm × 10-mm receivers are capable of better than 2.5-m position accuracy. Hybrid ephemeris prediction can be used to achieve less than 15-s cold start times. The receiver can operate down to 3 V and has a 20-mA low tracking current. To save power, the TM Series GNSS modules have built-in receiver duty cycling that can be configured to periodically turn off. This feature, combined with the module’s low power consumption, helps maximize battery life in battery-powered systems.

The receiver modules are easy to integrate, since they don’t require software setup or configuration to power up and output position data. The TM Series GNSS receivers use a standard UART serial interface to send and receive NMEA messages in ASCII format. A serial command set can be used to configure optional features. Using a USB or RS-232 converter chip, the modules’ UART can be directly connected to a microcontroller or a PC’s UART.

The GPS Master Development System connects a TM Series Evaluation Module to a prototyping board with a color display that shows coordinates, a speedometer, and a compass for mobile evaluation. A USB interface enables simple viewing of satellite data and Internet mapping and custom software application development.
Contact Linx Technologies for pricing.

Linx Technologies
www.linxtechnologies.com

Q&A: Marilyn Wolf, Embedded Computing Expert

Marilyn Wolf has created embedded computing techniques, co-founded two companies, and received several Institute of Electrical and Electronics Engineers (IEEE) distinctions. She is currently teaching at Georgia Institute of Technology’s School of Electrical and Computer Engineering and researching smart-energy grids.—Nan Price, Associate Editor

NAN: Do you remember your first computer engineering project?

MARILYN: My dad is an inventor. One of his stories was about using copper sewer pipe as a drum memory. In elementary school, my friend and I tried to build a computer and bought a PCB fabrication kit from RadioShack. We carefully made the switch features using masking tape and etched the board. Then we tried to solder it and found that our patterning technology outpaced our soldering technology.

NAN: You have developed many embedded computing techniques—from hardware/software co-design algorithms and real-time scheduling algorithms to distributed smart cameras and code compression. Can you provide some information about these techniques?

Marilyn Wolf

Marilyn Wolf

MARILYN: I was inspired to work on co-design by my boss at Bell Labs, Al Dunlop. I was working on very-large-scale integration (VLSI) CAD at the time and he brought in someone who designed consumer telephones. Those designers didn’t care a bit about our fancy VLSI because it was too expensive. They wanted help designing software for microprocessors.

Microprocessors in the 1980s were pretty small, so I started on simple problems, such as partitioning a specification into software plus a hardware accelerator. Around the turn of the millennium, we started to see some very powerful processors (e.g., the Philips Trimedia). I decided to pick up on one of my earliest interests, photography, and look at smart cameras for real-time computer vision.

That work eventually led us to form Verificon, which developed smart camera systems. We closed the company because the market for surveillance systems is very competitive.
We have started a new company, SVT Analytics, to pursue customer analytics for retail using smart camera technologies. I also continued to look at methodologies and tools for bigger software systems, yet another interest I inherited from my dad.

NAN: Tell us a little more about SVT Analytics. What services does the company provide and how does it utilize smart-camera technology?

MARILYN: We started SVT Analytics to develop customer analytics for software. Our goal is to do for bricks-and-mortar retailers what web retailers can do to learn about their customers.

On the web, retailers can track the pages customers visit, how long they stay at a page, what page they visit next, and all sorts of other statistics. Retailers use that information to suggest other things to buy, for example.

Bricks-and-mortar stores know what sells but they don’t know why. Using computer vision, we can determine how long people stay in a particular area of the store, where they came from, where they go to, or whether employees are interacting with customers.

Our experience with embedded computer vision helps us develop algorithms that are accurate but also run on inexpensive platforms. Bad data leads to bad decisions, but these systems need to be inexpensive enough to be sprinkled all around the store so they can capture a lot of data.

NAN: Can you provide a more detailed overview of the impact of IC technology on surveillance in recent years? What do you see as the most active areas for research and advancements in this field?

MARILYN: Moore’s law has advanced to the point that we can provide a huge amount of computational power on a single chip. We explored two different architectures: an FPGA accelerator with a CPU and a programmable video processor.

We were able to provide highly accurate computer vision on inexpensive platforms, about $500 per channel. Even so, we had to design our algorithms very carefully to make the best use of the compute horsepower available to us.

Computer vision can soak up as much computation as you can throw at it. Over the years, we have developed some secret sauce for reducing computational cost while maintaining sufficient accuracy.

NAN: You wrote several books, including Computers as Components: Principles of Embedded Computing System Design and Embedded Software Design and Programming of Multiprocessor System-on-Chip: Simulink and System C Case Studies. What can readers expect to gain from reading your books?

MARILYN: Computers as Components is an undergraduate text. I tried to hit the fundamentals (e.g., real-time scheduling theory, software performance analysis, and low-power computing) but wrap around real-world examples and systems.

Embedded Software Design is a research monograph that primarily came out of Katalin Popovici’s work in Ahmed Jerraya’s group. Ahmed is an old friend and collaborator.

NAN: When did you transition from engineering to teaching? What prompted this change?

MARILYN: Actually, being a professor and teaching in a classroom have surprisingly little to do with each other. I spend a lot of time funding research, writing proposals, and dealing with students.

I spent five years at Bell Labs before moving to Princeton, NJ. I thought moving to a new environment would challenge me, which is always good. And although we were very well supported at Bell Labs, ultimately we had only one customer for our ideas. At a university, you can shop around to find someone interested in what you want to do.

NAN: How long have you been at Georgia Institute of Technology’s School of Electrical and Computer Engineering? What courses do you currently teach and what do you enjoy most about instructing?

MARILYN: I recently designed a new course, Physics of Computing, which is a very different take on an introduction to computer engineering. Instead of directly focusing on logic design and computer organization, we discuss the physical basis of delay and energy consumption.

You can talk about an amazingly large number of problems involving just inverters and RC circuits. We relate these basic physical phenomena to systems. For example, we figure out why dynamic RAM (DRAM) gets bigger but not faster, then see how that has driven computer architecture as DRAM has hit the memory wall.

NAN: As an engineering professor, you have some insight into what excites future engineers. With respect to electrical engineering and embedded design/programming, what are some “hot topics” your students are currently attracted to?

MARILYN: Embedded software—real-time, low-power—is everywhere. The more general term today is “cyber-physical systems,” which are systems that interact with the physical world. I am moving slowly into control-oriented software from signal/image processing. Closing the loop in a control system makes things very interesting.

My Georgia Tech colleague Eric Feron and I have a small project on jet engine control. His engine test room has a 6” thick blast window. You don’t get much more exciting than that.

NAN: That does sound exciting. Tell us more about the project and what you are exploring with it in terms of embedded software and closed-loop control systems.

MARILYN: Jet engine designers are under the same pressures now that have faced car engine designers for years: better fuel efficiency, lower emissions, lower maintenance cost, and lower noise. In the car world, CPU-based engine controllers were the critical factor that enabled car manufacturers to simultaneously improve fuel efficiency and reduce emissions.

Jet engines need to incorporate more sensors and more computers to use those sensors to crunch the data in real time and figure out how to control the engine. Jet engine designers are also looking at more complex engine designs with more flaps and controls to make the best use of that sensor data.

One challenge of jet engines is the high temperatures. Jet engines are so hot that some parts of the engine would melt without careful design. We need to provide more computational power while living with the restrictions of high-temperature electronics.

NAN: Your research interests include embedded computing, smart devices, VLSI systems, and biochips. What types of projects are you currently working on?

MARILYN: I’m working on with Santiago Grivalga of Georgia Tech on smart-energy grids, which are really huge systems that would span entire countries or continents. I continue to work on VLSI-related topics, such as the work on error-aware computing that I pursued with Saibal Mukopodhyay.

I also work with my friend Shuvra Bhattacharyya on architectures for signal-processing systems. As for more unusual things, I’m working on a medical device project that is at the early stages, so I can’t say too much specifically about it.

NAN: Can you provide more specifics about your research into smart energy grids?

MARILYN: Smart-energy grids are also driven by the push for greater efficiency. In addition, renewable energy sources have different characteristics than traditional coal-fired generators. For example, because winds are so variable, the energy produced by wind generators can quickly change.

The uses of electricity are also more complex, and we see increasing opportunities to shift demand to level out generation needs. For example, electric cars need to be recharged, but that can happen during off-peak hours. But energy systems are huge. A single grid covers the eastern US from Florida to Minnesota.

To make all these improvements requires sophisticated software and careful design to ensure that the grid is highly reliable. Smart-energy grids are a prime example of Internet-based control.

We have so many devices on the grid that need to coordinate that the Internet is the only way to connect them. But the Internet isn’t very good at real-time control, so we have to be careful.

We also have to worry about security Internet-enabled devices enable smart grid operations but they also provide opportunities for tampering.

NAN: You’ve earned several distinctions. You were the recipient of the Institute of Electrical and Electronics Engineers (IEEE) Circuits and Systems Society Education Award and the IEEE Computer Society Golden Core Award. Tell us about these experiences.

MARILYN: These awards are presented at conferences. The presentation is a very warm, happy experience. Everyone is happy. These things are time to celebrate the field and the many friends I’ve made through my work.

Low-Cost SBCs Could Revolutionize Robotics Education

For my entire life, my mother has been a technology trainer for various educational institutions, so it’s probably no surprise that I ended up as an engineer with a passion for STEM education. When I heard about the Raspberry Pi, a diminutive $25 computer, my thoughts immediately turned to creating low-cost mobile computing labs. These labs could be easily and quickly loaded with a variety of programming environments, walking students through a step-by-step curriculum to teach them about computer hardware and software.

However, my time in the robotics field has made me realize that this endeavor could be so much more than a traditional computer lab. By adding actuators and sensors, these low-cost SBCs could become fully fledged robotic platforms. Leveraging the common I2C protocol, adding chains of these sensors would be incredibly easy. The SBCs could even be paired with microcontrollers to add more functionality and introduce students to embedded design.

rover_webThere are many ways to introduce students to programming robot-computers, but I believe that a web-based interface is ideal. By setting up each computer as a web server, students can easily access the interface for their robot directly though the computer itself, or remotely from any web-enabled device (e.g., a smartphone or tablet). Through a web browser, these devices provide a uniform interface for remote control and even programming robotic platforms.

A server-side language (e.g., Python or PHP) can handle direct serial/I2C communications with actuators and sensors. It can also wrap more complicated robotic concepts into easily accessible functions. For example, the server-side language could handle PID and odometry control for a small rover, then provide the user functions such as “right, “left,“ and “forward“ to move the robot. These functions could be accessed through an AJAX interface directly controlled through a web browser, enabling the robot to perform simple tasks.

This web-based approach is great for an educational environment, as students can systematically pull back programming layers to learn more. Beginning students would be able to string preprogrammed movements together to make the robot perform simple tasks. Each movement could then be dissected into more basic commands, teaching students how to make their own movements by combining, rearranging, and altering these commands.

By adding more complex commands, students can even introduce autonomous behaviors into their robotic platforms. Eventually, students can be given access to the HTML user interfaces and begin to alter and customize the user interface. This small superficial step can give students insight into what they can do, spurring them ahead into the next phase.
Students can start as end users of this robotic framework, but can eventually graduate to become its developers. By mapping different commands to different functions in the server side code, students can begin to understand the links between the web interface and the code that runs it.

Kyle Granat

Kyle Granat, who wrote this essay for Circuit Cellar,  is a hardware engineer at Trossen Robotics, headquarted in Downers Grove, IL. Kyle graduated from Purdue University with a degree in Computer Engineering. Kyle, who lives in Valparaiso, IN, specializes in embedded system design and is dedicated to STEM education.

Students will delve deeper into the server-side code, eventually directly controlling actuators and sensors. Once students begin to understand the electronics at a much more basic level, they will be able to improve this robotic infrastructure by adding more features and languages. While the Raspberry Pi is one of today’s more popular SBCs, a variety of SBCs (e.g., the BeagleBone and the pcDuino) lend themselves nicely to building educational robotic platforms. As the cost of these platforms decreases, it becomes even more feasible for advanced students to recreate the experience on many platforms.

We’re already seeing web-based interfaces (e.g., ArduinoPi and WebIOPi) lay down the beginnings of a web-based framework to interact with hardware on SBCs. As these frameworks evolve, and as the costs of hardware drops even further, I’m confident we’ll see educational robotic platforms built by the open-source community.