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COLUMNS 7ForewordThinking Workingsmarterandharder

By Don Dingee

34TheFinalWord IndustrialTransformation–

it’sallaboutchangeBy Jerry Gipper

E-CASTSWorking Together to Drive a Mainstream Market for Open Industry Standards-based Communications Platforms May 24, 2 p.m. ESTwww.opensystems-publishing.com/ecast

EVENTSSPSElectricAutomationAmericaMay 23-25 • Donald E. Stephens Convention Center, Rosemont, Illinoiswww.ea-america.com

SensorsExpo&ConferenceJune 5-7 • Donald E. Stephens Convention Center, Rosemont, Illinoiswww.sensorsexpo.com/sensors2006/

COVERWireless sensor networks and industrial systems are converging and generating greater efficiencies not experienced since the first Industrial Revolution. Discover how wireless mesh networking topologies help drive new levels of reliability on page 8.

PRODUCTThe SSP1492 from Sensor Platforms provides a host of powerful built-in signal processing tools driven by an 8051 processor running at over 14 MIPS. Read more about this sensor signal processing chip on page 24.

FEATURESExecutiveSpeakout:

AdaptiveAutomationinAction 8Wirelesssensornetworks:

DrivingtheNewIndustrialRevolutionBy Rob Conant, Dust Networks

12Fasterisbetter:High-speedModel-FreeAdaptivecontrol

By Dr. George S. Cheng, CyboSoft

SmartSensors18 IEEE1451.2smartsensorsenable

networkequipmentmonitoringBy James Wiczer, Sensor Synergy, and Lawrence Anderson, Intermatic

24 AuniversalsensorsignalprocessorchipBy John Garay and Joseph Miller, Sensor Platforms

NoPCsAllowed22 Supercomputergraphicsgoportable

By Helen Francini, NextCom

ProductGuide28 SmartSensors

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ENDURO OUTDOORIndustrial-Grade Compact Computer

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Features include shock and vibration resistant mounting,conformal coated electronics, and weather-proof housing.Install the ENDURO OUTDOOR anywhere, regardless ofwiring or protection against environmental conditions, andwhere installation and maintenance are fast and easy.

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� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Advertiser IndexRSC# Company Advertisement 21 ACCES I/O Products Integrated PC/104

Embedded Systems 11 Advantech Mission-Critical Ap­p­lications 13 Arcom Control Systems Vulcan/Vip­er 26 Ardence ReadyOn - RTX 20 Axiomtek SBC84820,

GOT-1840T, eBOX638 3 Data Device RD-19240 Series 15 DIGITAL-LOGIC AG Embedded Box PC 23 Grid Connect Intelligent Chip­s and Modules 2 Intel Intel XScale 35 Kontron Industrial Comp­uting Products 16 MPL IP65 All-In-One PC2701 MPL PC/104-Plus CPU Module2901 SCIDYNE PC/104 Perip­herals 17 Sensors Exp­o Sensors Exp­o

& Conference 2006 5 SMA ENDURO OUTDOOR 2702 Technobox Adap­ters and Tools2902 Technobox Adding Async I/O3102 Technobox PMCs and PIMs3101 TEWS Technologies Embedded I/O Solutions 19 Tri-M Systems TMZ104 25 Tri-M Systems MOPSIcd7 36 WinSystems EPX-C3

By Don Dingee>>foreword thinking

Somewhere along the line, somebody coined this cliché work smarter, not harder. It’s so overused, a search for it turns up 192,000 results on Google. I’m sure it was originally devised by a clever marketing department trying to sell some kind of labor-saving, productivity-improving device. It would be great if we could make things easy simply by being smart, like by using the easy button featured in a recent commercial series. We don’t want everything to be hard, right?

Here’s the rub: If lots of things were easy, anyone and everyone would do them. The term for this, commoditization, really means nobody makes any money doing it. It’s the hard that keeps average participants away from some things, and makes the effort of the few who tackle the problem and deliver results valuable. The way to financial success is to take something hard, so much so, few other people can do it, and make it look easy.

One thing is clear, today’s winners work both smarter and harder. Working smarter means taking routine tasks and automating them through the application of technology. Working harder means taking on something extraordinary and applying a concentrated effort, both technological and human, to produce an outstanding result. Here are a couple of examples of new products that could help designers do this:

n AMCC, www.amcc.com, is gearing up for a push into industrial markets with their new PowerPC 405EZ microprocessor, being announced at Embedded Systems Conference Silicon Valley. It targets low-power, small footprint designs with its high level of integration. A 32-bit System-on-Chip (SoC) with a 100 to 400 MHz core, the 405EZ features an industry first – a 10/100 MB Ethernet controller with an integrated IEEE 1588 controller, allowing time synchronization of events across a network. It also includes an advanced timer controller with fifteen 24-bit timers, a 32 KB SRAM on chip, and I/O features such as CAN, USB, and 10-bit A/D and D/A controllers. To extend the functionality even more, AMCC has worked closely with Micron Technology to design external interfaces for the emerging CellularRAM, www.cellularRAM.com, family of PseudoStatic RAM (PSRAM). These are low-power, self-refreshing memories with an SRAM-like interface targeted at small footprint devices such as wireless handsets, but they are a great fit for industrial devices, too.

n Matrox, www.matrox.com, has announced the Extio F1400 remote graphics unit, designed to extend Human Machine Interface (HMI) capability up to 250 meters from the main computer unit. Targeted at applications like security

monitoring, process control, computer-aided dispatch, and other mission-critical applications, the Extio is a small footprint fanless unit connected via a fiber optic cable to a PCI or PCE Express interface adapter in the system. It uses a Matrox graphics chip with 128 MB of graphics memory to drive up to four displays with DVI-I, and includes USB 2.0, audio, and microphone ports. This enables systems integrators to present a remote HMI with monitor, keyboard, mouse, microphone, and speakers reliably and securely.

In this issue, we’ve got some more fresh ideas to help you work smarter and harder, solving challenges in industrial applications.

n In our Executive Speakout feature on adaptive automation, Rob Conant of Dust Networks informs us about wireless mesh networking, and George Cheng of CyboSoft discusses model-free adaptive control.

n In other articles, we’ll hear about IEEE 1451.2 from Sensor Synergy and Intermatic, a sensor signal processing chip from Sensor Platforms, and a new portable supercomputer platform from NextCom.

n Also, check our website for exclusive new content including a Market Pulse column on Distributed Control Systems, an Industrial Europe column on a wearable computer, an article from Clarkson University and Advanced Cerametrics on self-powered sensors, and an article from Pilz on safety in the Australian Synchrotron.

Also be sure to see this issue’s Product Guide featuring companies and products in the area of Smart Sensors for industrial applications.

We’re hoping you are finding both the print issues and E-letters of Industrial Embedded Systems useful, and you can help us spread the word by sending folks to www.industrial-embedded.com where they can subscribe and use online resources such as the Product Guide and Newswire. As always, I enjoy hearing from you. E-mail me with your thoughts (pro or con), ideas, and suggestions at ddingee@opensystems-publishing.com.

Don Dingee Editorial Director

Working smarter and harder

I n d u s t r i a l E m b e d d e d S y s t e m s Spring/Summer 2006 / �

Executive Speakout: Adaptive Automation in Action

Wirelesssensornetworks:DrivingtheNewIndustrialRevolutionBy Rob Conant

Modern industry is increasingly dependent on the seamless transfer of information between plant systems and business systems. In this article, wireless sensor networking expert Rob Conant describes the vision for wireless sensor networks comprised of motes connecting wireless sensor elements, and highlights several industries applying the tech-nology today to deliver financial results.

Globalization, rising energy prices, and a strict regulatory environment are driving companies in a wide variety of industries to cut costs while increasing efficiency and productivity. Companies are changing organizational infrastructure and processes in their facilities to stay competitive. Meanwhile, trying to meet production and profitability goals remains a significant organizational hurdle. Given these market dynamics, industry leaders are feeling pressure to adopt new technologies that will help them gain competitive advantages wherever they can find them.

These forces are contributing to a wave of innovation in how things are done in industrial sectors that has not been seen for many years. The same pressures that led to eighteenth century developments by inventors such as Eli Whitney, James Watt, and Charles Babbage are reasserting themselves in the collision of the two worlds of industrial and Information Technology (IT). Just as the seed press and the steam engine catalyzed industries, so, too, are today’s manufacturing innovations – particularly those that can provide more access to more knowledge to plant managers and process engineers. This convergence of industry and information is leading to the New Industrial Revolution.

Imagine, for example, if environmental information and other data from the

physical world could be measured, managed, and refined with the same reliability as wired networks, but at a lower cost. Data from the physical world, including temperature, lighting, humidity, energy consumption, and movement could then be married to the world of industrial systems and IT. Wireless sensor networks and industrial systems are now converging and giving rise to greater efficiencies not experienced since the first Industrial Revolution. As industrial plant managers discover they can do more with less, they are turning to wireless mesh networks that seamlessly integrate with legacy plant systems to develop comprehensive monitoring and control strategies.

Wireless sensor networks allow information to be collected with more monitoring points, providing awareness into the environmental conditions that affect overall uptime, safety, or compliance in industrial environments and enabling agile and flexible monitoring and control systems. These networks connect critical processes or assets with the systems or experts that can interpret the data or take immediate action. At the end of the day, operational teams with more visibility into their processes can prevent shutdowns and increase efficiencies while reducing the total cost of data acquisition. All of this can add up to a distinct competitive advantage and a head start in the New Industrial Revolution.

Why wireless, why now?Recent advances in wireless networking technologies leverage the capabilities of the existing monitoring and control infrastructure in areas not possible before the New Industrial Revolution, more than doubling the available monitoring points at a lower cost per point than current wired solutions. Wireless sensor networks are comprised of battery-operated motes that have the ability to quickly form a

network and communicate with each other. They are deployed in a full mesh networking topology where each mote is a router – ensuring extremely low power and achieving over 99.9 percent reliability. Through techniques such as redundant routing and frequency hopping, wireless mesh networks approach the reliability of wired networks, significantly knocking down the barriers to collecting information from the physical world by field intelligent devices.

While the potential to marry physical monitoring with wireless has always existed, the adoption of wireless technology has been slow in making its way into industrial-grade monitoring and control systems. Many organizations have discovered that traditional point-to-point wireless networks are prone to failure when faced with the challenging and dynamic Radio-Frequency (RF) landscape presented by commercial and industrial environments. Likewise, wireless sensor networks designed for consumer-grade applications such as home automation, PC peripherals, and remote controls are simply inadequate for industrial applications. However, with recent technological advances in wireless mesh networking technology, seamlessly integrating wireless sensors into existing plant infrastructures can enable a whole host of monitoring and control applications, such as oil and gas, cold chain, and machine health monitoring. The flexibility and adaptability of wireless lowers the physical and cost limitations posed by wired systems, thereby lowering the total cost of ownership of an adaptive control strategy.

Reliability for harsh environmentsThe measure of success for an industrial-grade wireless sensor network is not how any individual network device performs, but how the system as a whole ensures a

� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Executive Speakout: Adaptive Automation in Action

reliable flow of critical data. Reliability is an absolute requirement for any monitoring technology, because if the data is not reliable, the economic benefits of its low installation costs are rendered irrelevant.

Specifically, for a wireless technology to be reliable in industrial applications, it must:

nFunction in harsh industrial environments with unpredictable Electromagnetic Interference (EMI), RF fading, and multipath interference

nCoexist in the field with other wireless devices or noise emitters such as machine equipment, communications devices, walkie-talkies, instant connect phones, pagers, cell phones, remote controls, and other wireless frequency emitters common in the industrial environment

Typical industrial environments have time, frequency, and location varying RF interference, as shown in Figure 1.

New wireless mesh networking topologies are one factor that drives new levels of reliability. In a mesh network topology, each mote has at least two parent motes with which it can communicate. Even if an individual link becomes inoperable, a mote still has a communication path available. This redundant routing ensures resiliency in case of offline motes or broken links.

Wireless sensor networks also use a combination of Frequency Hopping Spread Spectrum (FHSS) transmission and time synchronization, varying communications in both frequency and in time to sidestep RF interference

problems. This technique ensures that alternate paths are available if any signal is blocked due to RF interference. FHSS technology is particularly useful in industrial environments where intermittent RF interference is common.

Agile frequency hopping sidesteps interference by utilizing several discrete frequency slices, as depicted in Figure 2.

Wireless sensor networks provide adaptive monitoring systems in industrial environments with the flexibility and adaptability needed in a plant’s monitoring and control strategy. Wireless motes are placed where needed without the need of specialized RF skills or site surveys, while the network handles the rest such as wireless connectivity, routing redundancy, and frequency agility. Additionally, these networks are adaptable to changes in both the configuration of equipment on the plant floor and in the layout of the network itself. If managers add or remove monitoring devices, the network simply reconfigures itself automatically.

As wireless networking technology advances, it is also becoming more cost effective. Current wireless sensor networks are designed to ease development and integration with other systems. No customization, integration, or development is required, and there are no wiring or installation costs. Battery-powered motes don’t require AC power, which can make wireless networks suitable for locations where power distribution is not designed for additional monitoring equipment. Because motes self-organize into a functioning mesh network, no site survey or wireless expertise is required, and the installer does not have to program or configure the devices.

Wireless mesh sensor networks are also designed to deliver long lives with a minimum of ongoing maintenance. Motes can have a lifetime of five to seven years on a single pair of AA batteries, and they report on their power status, so operators or managers can tell when battery replacements are due.

Monitoring and control applicationsThe value of wireless networks is becoming apparent to organizations that have found they need real-time access to knowledge about their plant’s environment, processes, and equipment in order to prevent disruption. Spurred by the recent technological advances, the New Industrial Revolution is beginning to impact three very distinct monitoring and control applications: oil and gas, cold chain, and machine health monitoring.

Oil and gasOil and gas manufacturing is one industry looking toward wireless sensor networks to solve critical information gaps. Large industrial sites, such as oil refineries,

I n d u s t r i a l E m b e d d e d S y s t e m s Spring/Summer 2006 / �

Figure 1

Figure 2

Executive Speakout: Adaptive Automation in Action

already have complex process control systems in place, but many additional points could provide additional data to optimize processes. Because of the complexity and cost of integrating these non-mission-critical points into the existing control architectures, many of these points are not automatically measured today. Monitoring is usually done manually as inspectors check the status of key motors, valves, pumps, and supporting process variables. Not only is this time consuming, but when multiplied across an enterprise’s global infrastructure it also becomes very expensive. Dedicated staff inspecting the refinery may be able to detect a problem, but most likely only after the problem has occurred.

Wireless sensor networks reduce the oil and gas industry’s reliance on inspection personnel by supplying information about utilization rates, energy usage, equipment conditions, and environmental conditions. Information derived from wireless networks can help quickly identify and address issues that could jeopardize

overall uptime, safety, compliance, and profits. The network drastically reduces the cost of accessing information per point, and the ability to gather information around the clock provides unprecedented monitoring capabilities.

Cold chainCompanies in the food, chemical, and pharmaceutical industries have unique business problems and monitoring requirements as well. The shrinkage of perishable product inventory can range from between 5 and 10 percent. In real dollar terms, that could translate into hundreds of millions of dollars in losses at a large grocer, for example. Companies in these industries depend on the cold chain – the monitoring of environmental conditions such as temperature and humidity throughout the supply chain – in order to prevent such losses. These conditions have a major effect on freshness, and poor cold chain management results in spoilage and other conditions that make products unsuitable for stocking.

Financial considerations notwithstanding, the cold chain is also crucial to the food, chemical, and pharmaceutical industries’ compliance efforts. For example, federal penalties can be thousands of dollars per violation when regulators find that perishables have not been stored within federally mandated levels to ensure food safety. The risk of drugs stored in inappropriate environments can be orders of magnitude higher. Wireless sensor networks can provide actionable information about environmental conditions in physically challenging environments such as a cold storage container, a large distribution facility, or in a hospital. Information on temperature, humidity, and carbon dioxide levels alerts managers that preventative action must be taken. It can also provide evidence of compliance to regulatory bodies, thus avoiding fines, and alert managers to equipment that may be outdated or operating at less than optimal levels. The resulting savings are critical, and the low-cost nature of wireless mesh sensor networking eases adoption barriers.

10 / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Set it and forget it Industrial environments pose a big question for any wireless network: Can needed reliability and power efficiency be delivered to truly set and forget the network over years? Dust Networks has developed wireless sensor networking that tackles this challenge, helping large industrial sites extend monitoring and control capabilities including points not previously measured due to the complexity and cost of reaching them.

Dust Networks’ SmartMesh products are built to be highly reliable, ultra-low-power, and scalable. They are designed with industry-standard IEEE 802.15.4 radio chipsets and utilize frequency hopping and continual network self-healing to maintain more than 99.9 percent network reliability for the life of the network. They utilize a time-synchronized communication protocol to enable deep duty-cycling; tightly limiting radio activity results in power efficiency allowing batteries to last 5-10 years.

A SmartMesh wireless system includes:

nBattery-powered motes as shown in Figure 1nA network managernAn Application Programming Interface (API)nMesh networking software

These elements help make it easy for an OEM to integrate wireless sensor networking capabilities into new or existing monitoring and control systems.

For instance, an oil refinery needs thousands of sensors providing critical readings on pressure, flow, and temperature. Forgoing wires and linking these sensors via a reliable, low-power wireless sensor network helps process engineers add more monitoring points at a lower cost. These points can include new locations that were previously inaccessible to the operations team – and they can be left unattended for years. The data from the sensors can then be linked to an alert system that can trigger alarms to key personnel. The benefits from expanded monitoring capabilities are reduced costs, prevented disruptions, greater uptime, and ultimately improved production goals.

Figure 1

Executive Speakout: Adaptive Automation in Action

Machine health monitoringIn many industries, especially manufacturing, equipment performance is of critical importance. In many plants, the maintenance of machine condition or operation has traditionally been performed manually. Bearing problems are one of the most common faults in industrial machines, causing unplanned downtime on essential production equipment. Typically, monitoring vibration to detect bearing problems involves manual recordings, or requires expensive systems or services. Another aspect of machine health monitoring, temperature monitoring, detects abnormal or suboptimal machine behavior in order to prevent equipment from further deterioration.

Wireless sensor networks continuously collect information about the condition and behavior of machines within plants. Those responsible for equipment benefit from access to expanded monitoring information at greatly reduced costs, allowing them to prevent disruptions, achieve greater uptime results, and meet production goals.

The New Industrial Revolution is hereSpurred by global competition, rising energy prices, and a strict regulatory environment, the New Industrial Revolution is the long awaited convergence between industrial systems and reliable, flexible sensor networks. Today’s plant managers and engineers can now expand the capabilities of existing monitoring and control solutions to new applications and be free from the physical and cost limitations of wiring to achieve effective adaptive control strategies. The ramifications for the industry as a whole are clear – greater insight into machine behavior, less unplanned down time, improved data analysis, and a safer and more productive working environment, which all translates into dramatic improvements in plant operations, reduced costs, and increased competitive advantage. IES

Rob Conant is vice president of marketing and business development and cofounder of Dust Networks. With a history of bringing technology to

market in industries ranging from telecom to aerospace, Rob is focused on driving embedded wireless networking technologies to the forefront of ubiquitous sensing. He invented the micromirror technology for MEMS-based fiber optic switches, and led various teams in engineering and

business development to deliver leading market technologies. Rob holds a PhD and MS in Electrical Engineering and a BS in Mechanical Engineering from UC Berkeley.

To learn more, contact Rob at:

Dust Networks30695 Huntwood AvenueHayward, CA 94544Tel: 510-400-2900E-mail: rconant@dustnetworks.comWebsite: www.dustnetworks.com

“The New Industrial Revolution

is the long awaited

convergence between

industrial systems and reliable,

flexible sensor networks. ”

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Executive Speakout: Adaptive Automation in Action

Fasterisbetter:High-speedModel-FreeAdaptivecontrolBy Dr. George S. Cheng

Adaptive control is helping create faster, more accurate control for many applications. In this article, control industry leader Dr. George Cheng describes the concepts behind Model-Free Adaptive (MFA) control and shows its benefits compared to Proportional-Integral-Derivative (PID) and model-based control. He also highlights the elements of a real-world application in piezomotor control.

Since the beginning of time there has been a desire to move life along at a faster pace. In our world today, speed is indeed often the most decisive factor between winning and losing.

This faster is better rule impacts our industry in a huge way. For example, saving a second or two in a semiconductor wafer process step can substantially reduce the cost of ownership of a multimillion dollar tool. For some control components such as a gas flow or liquid flow controller, a quarter or half second faster to force the flow to track its setpoint can be decisive. In high-end motion control, customers demand subnanometer accuracy and submicrosecond control update rates.

Why PID and model-based control failThe speed bottleneck of a piece of equipment is often related to its automatic control system, which has three key elements: sensors, actuators, and controllers. When equipment becomes more complex and the flexible production capability is critical, high-speed adaptive control systems become critical and must deal effectively with problems including:

nNonlinearitynDead-zone and hysteresisnOpen-loop oscillatingnLarge τ-T ratio (where τ is delay time

and T is time constant)

nRapid and irregular setpoint trajectory changes

nNoise, disturbances, and changing operating conditions

PID controllers can be embedded in high-speed control equipment, but cannot deal effectively with all these problems. Since PID is a fixed controller with no adaptive capability, its parameters have to be retuned when process dynamics, product types, or operating conditions change.

A model-based adaptive control approach can be difficult and costly to implement because developing a process model and keeping it accurate always pose a challenge. For dynamic modeling based adaptive control, there may not be enough time and data to learn a new model. Control experts typically design a high-speed complex control system with precise process models requiring significant financial and time investments. These systems usually require special hardware and software that are difficult to build and change.

Fundamentally, the control speed and the control algorithm complexity will always be in conflict. Therefore, a simpler control algorithm is always desirable, no matter how much computing power is available.

Benefits of MFA controlMFA control, as its name suggests, is an adaptive control method that does not require process models. This patented technology has been developed by CyboSoft. An MFA control system is defined to have the following properties:

1. Precise quantitative knowledge of the process is not necessary

2. Process identification mechanism or identifier is not included in the system

3. Controller design for a specific process is not needed

4. Manual tuning of controller parameters is not required

5. Closed-loop system stability analysis and criteria are available to guarantee the system stability[1]

Based on the core MFA technology, CyboSoft has developed a set of general-purpose MFA controllers, each of which solves a tough control problem:

nSingle Input Single Output (SISO) MFA to replace PID and eliminate controller manual tuning

nNonlinear MFA to control nonlinear processes

nTime-optimal MFA to reach control condition in minimum time

nFeedforward MFA controller to deal with measurable disturbances

nAntidelay MFA to control processes with large τ-T ratio

nRobust MFA to protect the process variables from running outside their bounds

nTime-varying MFA controller to control time-varying processes

nMultiple Input Multiple Output (MIMO) MFA to control multivariable processes

MFA control products provide cost-effective and user-friendly solutions for the high-speed complex control market. MFA controllers do not require process models that are difficult to develop and maintain. Once installed, no controller tuning is required. In most cases, MFA controllers can directly replace the legacy controllers such as PIDs and achieve immediate improvements in control performance resulting in significant economic benefits. Due to their general-purpose nature, MFA controllers have been deployed in almost every sector of industrial control applications[2].

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Executive Speakout: Adaptive Automation in Action

Single-loop MFA control system structureThe system structure of a SISO MFA control system, shown in Figure 1, is as simple as a traditional single-loop control system that includes a SISO process, a SISO MFA controller, and a feedback loop.

The control objective for the controller is to produce an output u(t) to force the process variable y(t) to track the given trajectory of its setpoint r(t) under variations of setpoint, disturbances, and process dynamics. In other words, the task of the MFA controller is to minimize the error e(t) in an online fashion. The minimization of e(t) is achieved by:

nThe regulatory control capability of the MFA controller

nThe adjustment of the MFA’s weighting factors allowing the controller to deal with process dynamic changes, disturbances, and other uncertainties

MFA controller architectureFigure 2 illustrates the core architecture of a SISO MFA controller. Used as a key component, a multilayer perceptron neural network consists of one input layer, one hidden layer with N neurons, and one output layer with one neuron. Within the neural network there is a group of weighting factors (w

ii and h

i) that can be

updated as needed to vary the behavior of the controller. The algorithm for updating

the weighting factors is based on the goal of minimizing the error e(t). Since this effort is the same as the control objective, the adaptation of the weighting factors can assist the controller in minimizing the error while process dynamics are changing.

Also, the neural network-based MFA controller remembers a portion of the process data providing valuable information for the process dynamics. In comparison, a digital version of the PID remembers only the current and previous two samples. In this regard, PID has almost no memory, and MFA possesses the memory essential to an intelligent controller.

Adaptive capability of MFA controllerThe adaptive capability of MFA is illustrated when compared with PID in Figure 3. The performance of MFA (top) measured against that of PID (bottom) shows how MFA adapts when process

dynamics change. From the beginning, MFA and PID control two identical processes. Then both processes have a major dynamic change causing the systems to oscillate. The PID system continues to oscillate while MFA quickly adapts to an excellent control condition. When the setpoints are changed again, the MFA system no longer shows oscillation. If both controllers start from a sluggish situation, MFA will control the process faster and better while PID will remain sluggish.

MFA’s adaptive capability can be used in equipment or control device autocalibration. In many situations, when the equipment is properly calibrated and the controller is tuned, control performance has to be consistent during its normal operation. That means any oscillation or sluggishness of the control system is not desirable. In this case, MFA controllers that have the autocalibration feature can be used.

First, the system is put into an autocalibration mode to allow the MFA controllers to adapt until the system runs in an optimal condition based on certain criteria or specifications. For a high-speed system, autocalibration will take only a few seconds. Then, the system can be switched to the normal operation mode, where the controllers are fixed to provide consistent performance. As long as the system runs inside the defined operating condition, the controllers don’t need to adapt. Third, the adaptive information of the MFA controllers can be saved in a database so that such information or value(s) can be preloaded the next time the equipment is to run in the same condition. The autocalibration feature can save a lot of manpower in applications where tedious one-on-one manual calibration is required for each control loop or device.

Figure 1

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1� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Application: MFA piezomotor control A general-purpose high-speed MFA control system is based on National Instruments’ (NI’s) CompactRIO embedded control and data acquisition platform. The combination of NI’s LabVIEW software, related hardware including a PC, CompactRIO, Compact Fieldpoint, PXI, and third-party specialty hardware and software makes it a user- and developer-friendly Programmable Automation Controller (PAC) platform, well suited for high-speed control applications.

CyboSoft has embedded a variety of MFA controllers into the LabVIEW

platform and can deliver a general-purpose high-speed and high-precision adaptive control solution running on NI’s CompactRIO PAC system with performance (10 microsecond updates and I/O using an FPGA, 1 millisecond run time in the operating system) and precision (with 16-bit analog inputs and outputs) previously unattainable with off-the-shelf hardware.

Figure 4 shows a CyboSoft time-optimal MFA controller in NI’s CompactRIO controlling a piezomotor manufactured by Physik Instrumente, which demonstrates consistent control performance whether the piezo stage has weight or no weight on it.

Figure 3

Figure 4

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Executive Speakout: Adaptive Automation in Action

Benefits of high-speed MFA controlHigh-speed MFA control solutions offer many benefits:

1. Consistent control performance in all operating conditions

2. Elimination of controller manual tuning

3. Lower support costs4. Safer high-speed operations5. Higher production throughput Most importantly, MFA has become an enabling technology for a new generation of equipment where high-speed, long-range, high-accuracy motion control with adaptive capability is essential.

For example, oil or gas well drilling is typically done manually by drilling machine operators. The drilling speed and efficiency can be inconsistent depending on the operator’s skill and work attitude. If the drill bit hits a rock, the operator would want to penetrate slowly to avoid breaking the drilling bit. If the drilling is going through mud, the operator would want to penetrate as quickly as possible under certain safety boundaries.

Based on this idea, automatic drilling equipment is developed to improve the drilling safety and efficiency by automatically controlling the rate of penetration and weight on bit. However, each drilling rig can be different due to its design and type, wear and tear of the mechanical components, and so on. In addition, the operating conditions will be different from rig to rig, and from location to location, the controllers used in drilling equipment often require parameter tuning. Retuning PID controllers requires experienced engineers or technicians to travel to a remote location after each rig up-and-down. This effort can cost both time and money. MFA-based autodrilling equipment eliminates the need for controller tuning providing major economical and competitive benefits to the equipment vendors and users.

Faster MFA is betterTo serve the high-speed control market, MFA controllers are typically embedded in custom hardware. Since MFA is model-free, the MFA control software functions have a very small footprint and can run at high speed in a regular embedded environment. This capability makes the

MFA control solution even more compelling for customers or equipment vendors using high-speed c o n t r o l . T h i s r e v o l u t i o n a r y technology allows users to achieve significant advantages by being faster and more competitive. IES

George S. Cheng has BS, MS, and PhD degrees in Electrical Engineering and is the chairman and chief technical officer of CyboSoft, General Cybernation Group, Inc. He holds 10 U.S. patents and the related international patents, and is the

developer of MFA control technology and Combined Intelligence methodology. George is a senior member of ISA and IEEE, and a member of ISA’s Automation Industry Advisory Council.

To learn more, contact George at:

CyboSoft, General Cybernation Group, Inc.2868 Prospect Park Drive, Third FloorRancho Cordova, CA 95670 Tel: 916-631-6313Fax: 916-631-6312E-mail: gscheng@cybosoft.comWebsite: www.cybosoft.com

References[1] George Cheng, “Model-Free Adaptive Control,” in Instrument Engineers’ Handbook - Process Control and Optimization, ed. Bela Liptak (CRC Press LLC, 2005) George Cheng, “MFA in Control with CyboCon,” CyboSoft, General Cybernation Group Inc., March 2002 George Cheng, “Model-Free Adaptive Control,” in Techniques of Adaptive Control, ed. Vance VanDoren (Burlington: Elsevier Science, 2003).

[2] Vance VanDoren, “Model Free Adaptive Control - This New Technique for Adaptive Control Addresses a Variety of Technical Challenges,” Control Engineering Europe, March 2001 George Cheng, Min He, and De-Lin Li, “Model-Free Coking Furnace Adaptive Control,” Hydrocarbon Processing, December 1999George Cheng and Li-Qun Huo, “Control System Optimizes EOR Steam Generator Output,” Oil & Gas Journal (September 2003)George Cheng and Li-Qun Huo “Chinese Steel Plant Clears Air with Software - Model-Free Adaptive Control Puts Ling-Yuan Iron and Steel in League of Its Own,” InTech, October 2003Michael Major, “Model-Free Adaptive Control on an Evaporator,” Control, Sepember. 1998Dave Seiver and Ovidiu Marin, “Air Separation Advances with MFA Control,” Control, May 2001Stephen Harris, “Model-Free Adaptive Control of an FCC Catalyst Dilute Phase Vacuum Transport System,” Control, June 2004George Cheng, “Tomato Process Improvements Using Model-Free Adaptive Controllers,” Tomato News, April 2004George Cheng, “Model-Free Adaptive (MFA) Control,” IEEE Computing and Control Engineering, June/July 2004George Cheng and Wen-De Zhang, “Model-Free Adaptive Technology Improves Distillation Column Chain Control,” Hydrocarbon Processing, October 2004George Cheng and Zhong-Wei Zhang, “Model-Free Adaptive Control of Turbidity in Water Coagulation Process,” Control Engineering Europe, October 2004.

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Smart Sensors

Smart sensors combine data gathering with networking. This application highlights the principles behind the IEEE 1451.2 standard for smart sensors, and describes how a network-enabled equipment monitor has transformed conventional standalone manufacturing tools into continuously monitored online resources.

Each year, equipment used in manufacturing and other industrial operations is challenged to perform operations at a faster rate, for lower costs, and with higher quality – faster, cheaper, and better. With this growth of embedded electronics in equipment, automation engineers can find new opportunities to monitor and control their processes. Sensors embedded in or attached to equipment can provide monitoring capabilities to satisfy most needs.

The IEEE has developed a set of standards focused on connectivity features for intelligent transducers, including smart sensors and smart actuators. New types of

network-enabled equipment monitors with remote monitoring and smart sensor features for a wide range of commonly used, ordinary sensor devices are based on one of these IEEE standards – IEEE 1451.2. This sensor-to-Internet interface equipment transforms conventional, standalone manufacturing equipment into continuously monitored online resources.

IEEE 1451 standardsA committee of industry and government experts working under the auspices of the IEEE[1] has created a series of standards to support the connection of smart sensors and actuators to computer networks as illustrated in Figure 1. This set of standards includes support for a range of connectivity options from conventional serial interfaces (IEEE 1451.2) to wireless capabilities (IEEE P1451.5) and others (IEEE 1451.1, IEEE 1451.3, IEEE P1451.6, and IEEE P1451.0).

These standards include most of the important sensor-to-network connectivity features including:

nElectronic data sheetsnCommon command sets nOperational status functions nSelf-test commandsnSensor data correction algorithms nNetwork-independent architectures nSoftware language independencenCommon data formats

Although a complete description of these standards is beyond the scope of this article, the details of these standards can be found in the relevant IEEE standards documents.

An IEEE 1451.2 compliant monitorAn example implementation of these standards is Sensor Synergy’s Network Enabled Equipment Monitor (NEEM,

1� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

IEEE 1451.2 smart sensors enable network equipment monitoringBy James Wiczer and Lawrence Anderson

with the NEEM-112 shown in Figure 2) hardware and software technology, developed using an IEEE 1451.2 compliant smart transducer-to-computer network interface[2]. This implementation supports up to 10 discrete sensors, with six common types of analog sensor signals:

n ±10 V signals n0-5 V high resolution signalsn4-20 mA analog current loopsnType K thermocouplesn0-5 V pulse counter for

counting eventsnSwitch closures

Connecting sensors to Sensor Synergy’s NEEM unit enhances them with IEEE 1451.2 smart sensor features including network connectivity, Transducer Electronic Data Sheets (TEDS), and nonlinearity correction engines. In addition, Sensor Synergy further enhanced this unit with features not included in IEEE 1451.2 such as data acquisition storage memory, time stamps, thresholding features, and an auxiliary data port for secure unit configuration.

These features provide easy-to-use, high-level capabilities for low-effort data acquisition and interpretation. Sensors often can be plugged directly into the unit

without additional signal conditioning. The electronic data sheets provide online self-documenting and self-identifying features that enable the ultimate end user to verify that the data being viewed is associated with the desired sensor. The advanced thresholding features calculate equipment utilization percentage during the user-defined workday.

Web-centric data acquisitionThe NEEM uses TCP/IP with HTTP over wired Ethernet for network connectivity. Additionally, calibrated sensor data, TEDS, and other related information are made available to a microweb server embedded within the unit. The microweb server provides an Internet-connected client running a conventional Web browser software program with Web pages containing sensor data tables, graphs, and the text from TEDS. All of these tasks are performed while consuming less than 3.0 W of electrical power.

This form of data acquisition, with the sensor coupled to a network connection, is often referred to as a Web-centric approach since no conventional computer (meaning a PC) is needed near the sensor location in the manufacturing environment. In this Web-centric approach, only the small, rugged hardware interface unit and a network interface connection are needed in close proximity to the hardware sensor element. Reducing the number of PC-style computers on the factory floor can be important for many manufacturing environments in which security, footprint size, physical integrity of the equipment, and costs are driving factors.

Factory floor applicationOne application at Intermatic uses Sensor Synergy’s NEEM event counter interface channel to count cyclic operations of a

Figure 1

Overview of IEEE 1451

PHY– Physical Interface for Data Interconnection

NCAP to NetworkConnection

TIM– Transducer Interface Module

External Network Used by Smart Transducer “Clients” to Connect toTransducers

NCAP– Network CapableApplication Processor Module

TIMModule

IEEE 1451.xSpecific PHY

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NCAPModule

Figure 2

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Smart Sensorsproduction machine. Machine activity for the equipment shown in Figure 3 is monitored in order to better understand and manage throughput issues. In this case, the sensor is a simple microswitch physically coupled to a rotating shaft in the high-speed offset printing machine, with rates between 0 and 300 cycles per minute.

In addition to processing the raw sensor signals into meaningful sensor information with relevant physical units every 20 seconds, the IEEE 1451.2 driven NEEM technology also provides threshold comparisons referenced to user-specified

workday hours. The resulting calculations provide a machine utilization percentage during each 15-minute increment of the workday, and a total aggregate utilization per day based on the number of in-use 15-minute intervals recorded during the prior day. This information can be used to manage manufacturing assets in order to determine when a machine is in use, and can help determine optimal machine placement, schedules, and overall return on investment.

A data logger program records all of this information to disk archives at Intermatic corporate headquarters, where production problems are detected, verified, and identified. It also generates e-mails, audible alerts, and Windows messages from the remote PC when production conditions violate user-specified tolerances. All of these features are available without software programming efforts.

Figure 4 illustrates the type of data collected. With a threshold of 100 machine cycles per minute (multiply the scale on the vertical axis by a factor of 3 since the interval is 20 seconds), this equipment was operational approximately 40 percent of the time between 7:30 a.m. and 11:30 a.m. during the morning of

Figure 4

Figure 3

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EmbeddedAxiomAd.pdf 3/3/2006 12:34:02 PM

Intermatic’s ProductionHigh-Speed Offset Printer

Sensor Synergy’sNEEM Unit

Smart Sensorsthese test runs. The browser view of the data chart illustrates the relative ease of interpretation to assess the operations of a production asset.

ConclusionsSensor Synergy has developed an easy way to transform standalone manufacturing assets into network-enabled, continuously monitored online resources. Using the NEEM-112 equipment monitor driven by IEEE 1451.2 smart sensor interface technology can help managers to better understand the scope and details of production issues, anticipate scheduling issues, manage assets, and improve overall manufacturing control with a small investment in financial and engineering resources. IES

James Wiczer is president and founder of Sensor Synergy, and is currently chair of the IEEE 1451.2 Standards Committee. Prior to founding Sensor Synergy,

he managed teams at Sandia National Laboratories in Albuquerque,

New Mexico. James received his BS in Electrical Engineering from Purdue University and his PhD in Electrical Engineering from the University of Illinois at Champaign-Urbana.

Lawrence G. Anderson is currently an electronic engineer at Intermatic Incorporated. He is responsible for manu-facturing processes at Intermatic involving

more than 50 production machines and data collection from 50 molding machines and 25 punch presses. In addition to his 20 years of industrial electronics experience, Lawrence’s back-ground includes an AS in Electronics and a BS in Technical Management, both from DeVry University.

To learn more, contact James or Lawrence at:

Sensor Synergy, Inc.1110 W. Lake Cook Road, Suite 340Buffalo Grove, IL 60089Tel: 847-353-8200

E-mail: jwiczer@sensorsynergy.com Website: www.sensorsynergy.com

Intermatic, Inc.Intermatic PlazaSpring Grove, IL 60081Tel: 847-675-7252E-mail: landerson@intermatic.comWebsite: www.intermatic.com

References

[1] IEEE 1451 Committees on Smart Sensor/Smart Transducer Interfaces including IEEE 1451.0, IEEE 1451.1, IEEE 1451.2, IEEE 1451.3, IEEE 1451.4, IEEE 1451.5, and IEEE 1451.6. For more details, see http://ieee1451.nist.gov/.

[2] IEEE Std 1451.2-1997, “IEEE Standard for a Smart Transducer Interface for Sensors and Actuators – Transducer to Microprocessor Communication Protocols and Transducer Electronic Data Sheet (TEDS) Formats” IEEE Instrumentation and Measurement Society, TC-9 Committee on Sensor Technology, Institute of Electrical and Electronics Engineers, New York, N.Y., Sept. 1998.

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No PCs Allowed

Delivering the graphics power of a supercomputer in a portable workstation is no easy task. With new technologies such as PCI Express and dual-core 64-bit processors from AMD, designers at NextCom produced a powerful, yet portable workstation for industries such as oil and gas exploration. Helen explores the key elements behind delivering high-performance graphics in a small package.

The sophistication of computer graphics has burgeoned in the past few years, especially in industrial applications. EDA and CAD/CAM applications used in manufacturing need increasingly sophisticated graphics in every phase. Industrial equipment monitoring relies more on 2D and 3D visual representations of the status of functions within a machine, allowing engineers to make real-time adjustments as necessary. Extreme graphics is vital to oil and gas exploration, where seismic data is collected and analyzed via complex algorithms and results are represented visually. Delivering the needed graphics performance for these applications presents a systems challenge.

High-end graphics pose difficultiesTo produce the most accurate, realistic graphics, a computer needs a powerful graphics card, a CPU that has enough performance, and a fast communications bus. Just as a truly top-notch entertainment center doesn’t combine mostly low-end components with one high-end component (for example, a high-priced TV screen and a low-priced set of speakers, DVR/VCR, and amp), skimping on a computer’s CPU, bus speed and efficiency, or I/O creates inferior visual output even from an exceptional graphics card.

Top-of-the-line graphics capability often is not found in PCs, because the best graphics cards and powerful processors produce too much heat for a PC-sized computer to deal with effectively. Because of the heat produced, truly top-of-the-line, high-end graphics have been found only in desktop towers and rack-mount servers that cannot travel with their users.

But, as industrial technology advances, the need for mobile computing capability increases. On-site and remote monitoring is far less expensive and more convenient with mobile equipment in that the same computer system can be used for multiple monitoring sites, particularly when diagnosing a specific functionality problem. Even though the capabilities of laptops have improved, they generally lack the cooling, processing, and I/O capacity for these tasks.

PCI Express – don’t compute without itNew technology is here to help. The industry standard for the

highest-end graphics applications is the Commercial Off-the-Shelf (COTS) PCI Express card. To engineer the finest graphics available, a computer needs a PCI Express 16x slot and connection to the core logic chipset. PCI Express uses different channel sizes, and 16x means that there are 16 channels of data transferred between the graphics card and the core of the computer at the same time. The result is up to 40 Gbps of information going back and forth in both directions simultaneously. When used in conjunction with a superior internal graphics controller, COTS PCI Express cards can provide superior 2D/3D graphics acceleration, additional external monitor options, and special effects.

ATI Technologies’ Mobility Radeon line of internal graphics controllers are known for providing exceptional video quality with cinema-like look and feel, as well as the fastest-performing, most advanced 3D graphics. Combining this controller with an NVIDIA Quadro FX 4400 PCI Express card increases the performance, programmability, precision, and quality of graphics for professional CAD, scientific, and industrial engineering applications. The ultra-high-end Quadro FX 4400 provides greater sophistication in the multisampling pattern, significantly increasing color accuracy and the visual quality of edges and lines without compromising performance.

Digging to the core of processing powerMany types of computer applications have seen an increase in the size of the data sets they are called on to crunch. Nowhere is this truer than in the oil and gas industry, where real-time seismic imaging is crucial to the ecological safety of offshore oil extraction (see the online sidebar “Offshore reservoir management” under this article on our website, www.industrial-embedded.com). The amount of data collected, analyzed, and stored with seismic imaging applications calls for the best, fastest processing capability as well as the most state-of-the-art graphics.

For pure crunch, dual-core processing is currently winning the high-powered CPU race. AMD’s dual-core Opteron processor architecture, shown in Figure 1, places two CPUs side by side in the same space as previous single-core architectures offered one CPU, optimizing processing and response time. Furthermore, multithreaded software functions most efficiently on a dual-core architecture – independent tasks can run concurrently, yet separately, as assigned to one of the two CPUs.

Each CPU’s separate cache and separate link to the bus, which connects to the motherboard’s main memory, speed up dual-core processing. This gives a computer the ability to perform 32- and

Supercomputer graphics go portable By Helen Francini

22 / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

No PCs Allowed

64-bit computing at the same time with no loss of performance.

In real life, the dual-core processor works at approximately one-and-a-half times the speed of a single-core processor. With two dual-core Opteron processors ganged together for quad processing, the speed increases even more. AMD’s Direct Connect Architecture optimizes that speed by using the computer’s resources in the most effective and fastest way. It eliminates much of the bottlenecks of regular system architecture by connecting the processor directly with DRAM memory and the I/O subsystem, as well as to other CPUs, allowing a possible per-processor peak bandwidth of up to 24 GBps.

A supercomputer packed in a boxTo meet the requirements of today’s industrial imaging applications, computer manufacturers must work to optimize graphics capability, processing power, and computational speed, while shrinking the box that holds all this power. But, this has traditionally been a bigger, faster, cheaper – pick two out of three problem.

NextCom, LLC’s FleXtreme NextDimension (Figure 2) contains all these com-ponents in a portable form factor. Well-suited

to industrial computing, it doesn’t just look cool, it is cool – literally cooler than laptops and notebooks. Two fans cool the interior: one for the power supply, and one for the entire system. Additional fans are added for specific PCI Express cards that tend to produce more heat. In addition, Opteron processors run on less power than single-core processors and dissipate less heat. An energy-efficient version, in which the Opteron processor runs on only 30 W, is also available. Only 12" (H) x 17" (W) x 4.25" (D) (30.48 cm x 43.18 cm x 10.8 cm), the FleXtreme NextDimension has an integrated TFT screen, which looks much like a plasma TV, and is available in either a 15- or 17-inch model. Additional monitor connections are integrated

for multiple viewing options. A screenless version of the computer is also available. This can be added to an existing network of computers, or plugged into up to two third-party monitors.

The optional 16 GB of DDRAM, which supports data transfers on both the rising and falling edges of each clock cycle, doubles the data throughput of the memory chip and consumes less power than regular RAM.

The FleXtreme NextDimension is designed to take the best possible advantage of open standards. Since it supports all major operating systems and incorporates the most technologically advanced and commonly used I/O options, customization becomes a simple component of the overall application requirements.

Pushing applications fartherDeeply entrenched in this technology age, we can certainly expect to see an ever increasing need for more processing power, better graphics, and more I/O – all in the smallest package computer manufacturers can muster.

Innovative technology breakthroughs such as the NextDimension help users push their applications even farther. Its designers aimed for longer lasting systems that could serve multiple needs, for graphics capabilities that look into the future of visualization, and for a compact chassis that can move around from place to place or squeeze into a tight corner when necessary. In fact, the NextDimension can be put in a bag and slung over the user’s shoulder, putting its level of supercomputer graphics capability right where it is needed most. IES

Helen Francini has been a technical writer since 1989, and now heads the documentation depart-ment at NextCom, LLC. She also helps to main-tain the company’s website.

To learn more, contact Helen at:

NextCom, LLC131 Burke StreetNashua, NH 03060Tel: 603-886-3874E-mail: hfrancini@nextcomputing.comWebsite: www.nextcomputing.com

Figure 1

Figure 2

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Smart Sensors

A universal sensor signal processor chip

By John Garay and Joseph Miller

Smart sensors combine analog and microprocessor elements into one device to make interfacing easy for designers. A new sensor signal processing chip simplifies integration with analog sen-sors that do not incorporate an on-board signal processing solution. John and Joseph describe the architecture of the signal processing chip and an example of a three-axis compass designed around it. The Smart Sensors Product Guide that follows on page 28 provides descriptions of these types of devices.

Sensors are finding their way into almost every conceivable electronic and mechanical product manufactured today. Designers face the challenge of implementing unique sensor interface designs every time a new and different sensor is selected, which can be troublesome particularly in developing high-volume products sold into cost-sensitive, highly competitive markets.

One option is developing a custom ASIC and embedding code unique to the application, but pitfalls include high NRE costs, extended development cycles, higher risk, and lost time to market. Chip graveyards are littered with failed ASIC developments, and a better choice is needed.

Sensor processing simplifiedRecognizing the need for a common sensor signal processing chip, Sensor Platforms created a proprietary analog front-end sensor signal processing architecture. Specifically targeting sensor applications, the SSP1492 Sensor Signal Processor Chip is designed to interface directly with a wide variety of sensors. Sensors based upon operating principles of pulse, voltage, current, inductance, capacitance, and resistance can interface directly to the chip with no additional active electronics and a minimum

number of external passive components. An integral high-performance op-amp is also provided for user signal conditioning.

In addition, the SSP1492 provides a host of powerful built-in signal processing tools driven by an 8051 processor running at over 14 MIPS. These include two integrated math engines for vector and scalar calculations, user-accessible floating-point tools such as rudimentary

math functions and high-order polynomial fit functions. The SSP1492’s maximum resolution is virtually infinite with native resolution of internal registers at 16 bits and default ROM containing routines for 32-bit sensor measurements as part of the on-chip firmware.

Figure 1 illustrates the SSP1492’s system architecture, showing the capability of directly connecting up to 15 sensors when utilizing all available I/O channels.

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2� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Bringing analog signals on-chipThe SSP1492 utilizes a unique frequency mode data acquisition technique that converts sensor output into a frequency-based signal by placing the sensor element into a Resistance-Capacitance (RC) oscillator as shown in Figure 2. The oscillator circuit consists of a comparator with a positive feedback loop to create an oscillator that converts the time-varying sensor signal into a square wave with a varying time period. This signal can be counted and digitized into a value proportional to the oscillator frequency, and in turn, proportional to the sensor’s measurand.

In the RC oscillator configuration, the sensor is introduced as a variable Resistor (R) with an external fixed Capacitor (C) in the circuit. A period counter unit shown in Figure 3 on page 26 demodulates and digitizes the output signal from the sensor oscillator section, and can measure two parameters of time-varying signals – cycle period and pulse width. The sensor data converter has scalable dynamic range, accuracy, and speed and can be scaled easily from less than 8 to over 24 bits of conversion resolution.

Application: Three-axis, tilt-compensated compassDesigned with compass applications in mind, the SSP1492 uses built-in advanced processing algorithms to easily resolve two- and three-axis, tilt-compensated compass designs. External hardware requirements are limited to two or three inductive sensors that change inductance relative to the external magnetic field, a capacitive inclinometer, and two resistors. Design steps are straightforward:

nHaving selected the appropriate inductive sensors and inclinometer, the designer begins by configuring an LR oscillator. The inductive sensors are connected directly to pads L1A, L1B, L2A, L2B, L3A, and L3B.

nThe capacitive inclinometer is connected across pads Cap1 through Cap4. Fundamental to the design is the LR oscillators shift in frequency as the sensor’s inductance changes. This change in frequency is measured by the period counter logic and used to determine magnetic field strength.

nImplementing a three-axis compass design requires that the L1 and L2 coils be physically placed

Figure 2

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Smart Sensors

perpendicular in the same plane to determine the direction in a 360° circle. When rotated around a fixed magnetic field, the L1 coil will generate a sinusoidal wave while the L2 coil generates a cosine wave. The L3 coil is placed in an axis vertical to the horizontal plane of the L1 and L2 coils.

nTo nullify noise and establish the equivalent of a fully differential measurement, each coil is driven in opposing directions to induce current flow for two measurements per coil. Both trip points of the LR sensor oscillator are adjustable with a resistive DAC. This further enhances the system’s ability to filter out noise, allowing the designer to fine-tune the oscillator’s trip points in an effort to maximize system performance.

Figure 4 illustrates a three-axis compass solution using the LR and RC oscillator block. An inclinometer is only required

when a tilt-compensated compass is desired, detecting how the compass module is positioned relative to the horizontal plane. In this circuit the inclinometer is represented as a capacitive sensor. The sensor will change capacitance as the compass module changes position relative to the horizontal and vertical axis. Any change in the sensor’s position affects the frequency of the RC sensor oscillator. This configuration requires that four frequency measurements be made, one for each of the four external capacitors.

When configuring an LR or RC oscillator circuit it’s a good idea to

select complementary components that maximize the number of sensor oscillator cycles that can be integrated during a single measurement cycle. This insures that more oscillator transitions are averaged out over the same measurement period, thereby reducing the combined effects of the sensor’s own noise and the oscillator’s threshold noise reproduced in the form of phase jitter at the output of the sensor oscillator.

Using the SSP1492’s 8051 microprocessor and a powerful array of built-in firmware routines, commands such as hard-iron compensation, tilt-compensating

AVDD

Rg

Rg

Rtrip1

Rtrip2

Rtrip3

L3

SensorCOMP

TripDAC

L2

L1A1

Cap4

Cap3

Cap2

Cap1

Rin1 Rin2R1GPIO General Purpose I/O pin

DACO CMPP

SENS CMPN

CMPO DRVI

L1A L1B

L2A L2B

L3A L3B

SENSOR OSCILLATOR

SECTION

16-BIT CYCLE COUNTER16-BIT HIGH-SPEED

COUNTER

HIGH-SPEED CLOCK

16-BIT CYCLE COUNTER PRESET REGISTER

(CCP)

16-BIT RESULTS REGISTER

(RSR)

UNDERFLOW

SENSORCOMP

OVERFLOW OFL bit

CONVERSION COMPLETE (CCF bit)

Figure 3

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Figure 4

Smart Sensorsheading, and pitch and roll are executed easily by simply calling up the associated commands via the I2C or SPI serial interface. This combination of integrated hardware and embedded software routines further minimizes the complex task of implementing a tilt-compensated, two- or three-axis compass design.

An SSP1492 evaluation board with USB interface and serial EEPROM burner is available for designers to begin system test and evaluation. A prototyping section enables fast and secure connection to external sensors. Software tools are also provided for system analysis, including spreadsheets and a demo version of Spice with models. Configuration files, templates, examples for freeware C compilers, and an IDE are included.

Meeting sensor-based design needsThe SSP1492 sensor signal processing chip specifically targets smart sensor applications, helping designers implement complex sensor designs with minimal resources and limited time. With no lengthy ASIC development, high NRE cost, or risk, the SSP1492 presents an

attractive and capable solution for today’s sensor-based designs.

The SSP1492 has been engineered to interface directly with a wide variety of sensors. Industrial, automotive, consumer electronics, PCs, mobile handsets, and medical are but a few of the applications that benefit from the low-cost, short development cycle and performance advantages the SSP1492 offers. By simply using the SPI or I2C serial communication protocols and selecting a CAN interface chip or CAN-enabled microprocessor as the system host, designers can quickly implement a robust link for automotive, consumer, and industrial applications that require a CAN protocol solution. IES

John L. Garay brings more than 20 years of engineering and managerial experience to Sensor Platforms. He obtained a BSEE degree from Fairfield University,

and has designed sensor products for the automotive and consumer electronics markets. John also holds two patents and presented a paper titled “Bodylink –

Linking Wrist Instruments to a Network of Sensors” at the 2004 Congress International De Chronometrie in Montreux, Switzerland.

Joseph Miller is responsible for systems design and development of Sensor Platforms’ mixed-signal ASIC and OEM development tools. He has been an elec-trical engineer,

designing circuits and systems involving sensors, for more than 20 years. His designs have included high-speed sensitive weighing equipment, industrial systems including high-power laser power supplies, instrumentation, and control systems. Joseph has also published articles in the robotic field.

To learn more, contact John at:

Sensor Platforms Incorporated1550 Airport Blvd., Suite 220Santa Rosa, CA 95403Tel: 707-490-5823 E-mail: jgaray@sensorplatforms.comWebsite: www.sensorplatforms.com

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Product GuideSmartsensors:Acquisition

Comp­any name/Model number Website/Descrip­tionApplied Data Systems www.applieddata.net

DAQPODA portable weather data acquisition unit • High-performance digital thermometer, hygrometer, and barometer in a handheld, 9 V battery-powered unit • Suitable for monitoring greenhouses, HVAC systems, high-performance engine tuning, and determining temperature trends

GE Fanuc Automation www.gefanuc.com/embedded

VMIVME-341932 differential or single-ended low-level analog input channels • Programmable gain • Gain selection of x1, x10, x100, x1,000 with input ranges from ±10 mV to ±10 V • Online autozeroing • Each channel provides individual • Configurable for RTD, strain gauge bridge, and local or remote sensing

Interactive Circuits & Systems www.ics-ltd.com

daqPCOffers high-speed record/playback capability, storing up to 2.8 TB of digital data on an array of hard drives • Digital data recording of sensor signals at over 600 MBps sustained rate • daqPC supports time-stamp recording using either an on-card or off-card source

Mesa Electronics www.mesanet.com

4A23High-resolution, 20-bit, low-power, delta-sigma, A/D card for the PC/104 bus • Provides 8 differential or 16 single-ended inputs with 500 V isolation from system ground • Five programmable input ranges from 25 mV to 5 V full scale, all except 5 V can be bipolar

Micro/sys www.embeddedsys.com

MPC624A PC/104 expansion board • 24-bit delta-sigma A/D converter • Multichannel with single cycle settling • Fault protection on analog/digital • Direct connect to sensors • Input range ±10.1 Vdc or ±1.01 Vdc • Selectable Zin per channel

Radical Systems Engineering www.radicalsystems.com

Temperature & Voltage DAQ Measurement Systems

Temperature and voltage DAQ measurement systems in desktop, laptop, or remote configurations; no programming required • Expandable and scalable: 32 to 384 input channels • 12- or 16-bit DAQ resolution • User-defined displays, reports, and plots • Low-pass noise filters

Sensor Platforms www.sensorplatforms.com

SSP1492 Sensor Signal Processing Chip

Draws < 10 mA at 3 Vdc for ~ 150 µs in active measurement mode • Standby idle current < 10 A • Integrated user accessible 8051 microprocessor • EEPROM retains calibration coefficients or user data after removal of power • Accessible 2.3 V regulator/reference • Fully integrated solution that only requires a few passive external compo-nents • SPI or IIC serial data communication protocol for interfacing to a host processor

Soltec www.solteccorp.com

PCD-300ATwo types of sensor interface are available: PCD-300A to measure strain and PCD-320A to measure voltage • The sensor interface is connected to a PC via USB interface • Each sensor interface unit has four channels • It is possible to take measurements using up to four units (16 channels)

2� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Product GuideSmartsensors:Acquisition

Comp­any name/Model number Website/Descrip­tionWinSystems www.winsystems.com

PCM-518Intelligent sensor interface module for acquiring high-precision sensor data for PC/104-based systems • Optimized for temperature and low-level signal measurements, including signal filtering, sensor excitation, and linearization on all channels • Supports thermocouples, RTDs, strain gauges, thermistors, and low-level voltage inputs

Smartsensors:BiometricEzValidation, Inc. www.ezvalidation.com

EzPassport Toolkit (SDK)Allows quick integration of any biometric (such as fingerprint, face, iris, retina, voice) or nonbiometric (smartcard and wireless) authentication into any Windows application • Provides mechanism to replace Windows native authentication with a biometric authentication • Allows developers to create custom applications using EzPassport API

Smartsensors:DisplacementOPTEK Technology www.optekinc.com

SensorNoncontacting position sensor provides 360-degree angle and linear displacement sensing • Virtually immune to temperature, vibration, and other environmental factors • Multiple pucks and a single pad can be used to determine absolute position in 3-space (x, y, z)

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Product Guide

Smartsensors:LightComp­any name/Model number Website/Descrip­tion

Vishay www.vishay.com

TEMT6000

A miniature ambient light sensor • Silicon NPN planar phototransistor for building backlit displays that respond automatically to changes in ambient light • Measures 4.0 mm x 2.0 mm • Peak sensitivity of 570 nm • Wide angle of half sensitivity of ±60 °C • Suppresses the infrared spectrum to offer improved human eye-like responsiveness to the visible light spectrum

Smartsensors:NetworkedAcromag www.acromag.com

965PB

A series of Profibus, 6-channel, thermocouple input modules • Interfaces thermocouple and millivolt signals directly to a Profibus-DP network • Separate bus coupler is not required • Modules are one inch wide and are mountable on DIN rails • Supports operation on 12 to 36 Vdc power supplies • Accepts universal thermocouple/millivolt inputs with user-selectable ranges for type J, K, T, R, S, E, B, or N sensors and bipolar ±100 mV or ±1 Vdc signals

EnOcean www.enocean.com

STM100 Energy harvesting • Self powered • Batteryless • Maintenance free • Wireless • Wireless sensor • Cost effective • Miniature

Huron www.huronnet.com

DN-400A general-purpose, low-density, low-cost embedded I/O module with DeviceNet protocol • Connects up to four sensors, limit switches, or other discrete input devices and up to four outputs for discrete output devices • Accommodates inputs from any PNP input device • Each PNP output provides up to 1 A • 60 mm x 68 mm size

IOtech www.iotech.com

DBK90

A 56-channel thermocouple (TC) input module for the DaqBook/2000 and WaveBook/516E series of Ethernet-based data acquisition products • Any combination of TC types can be attached to any channel via mini-TC input connectors • Each bank of 14 TC inputs has a separate cold-junction sensor • Requires 1 ms/channel to make a TC measurement

Phoenix Contact www.phoenixcontact.com

IBS VME6H SC/I-T

An Interbus Generation 4 controller board • 6U VMEbus • Remote connection via 9-position D-sub connector • Serial diagnostic interface • Networking of up to 4,096 sensors and actuators • Up to 512 Interbus devices • Up to 255 remote bus devices • Supports loop devices • Up to 62 PCP devices • Synchronous operating modes • Logical addressing

Sensoray www.sensoray.com

2518

A 10BASE-T Ethernet interface combined with Smart A/D for sensors allows data acquisition of 8 or 16 channels of thermocouple, RTD, strain gauge, thermistor, voltage, 4-20 mA, or resistance data through Ethernet connections • Each channel is software programmable for a different sensor type • Screw terminations direct sensor connections

30 / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Product Guide

Smartsensors:PressureComp­any name/Model number Website/Descrip­tion

Parvus www.parvus.com

Dual Pressure Sensor BoardDual air pressure sensors (Motorola Senseon MPX2200AP) • Microcontroller (Motorola MC68HC05C8) • Two digital and four analog inputs • Precision, chopper-stabilized instrumentation amplifier • External 10-pin analog connector • Dual digital thermostats • 32 digital outputs (via 50-pin 32-bit digital output and RTD connector)

Smartsensors:RFIDArcom Control Systems www.arcom.com

RFID Edge Controller

An RFID Edge Controller Industrial Compact enclosure (RFID-EC-IC) for warehouse and factory floor locations • Built upon a fanless, diskless embedded hardware platform offering very high MTBF and a large number of physical interfaces • Fitted with Arcom VIPER M64-F32 processor board • Four RS-232 ports, one RS-485 port • 10/100BASE-T Ethernet

Smartsensors:TemperatureSaelig www.saelig.com

DLP-TH1A low-cost, USB-based digital temperature and humidity sensor • Monitors temperature, humidity, and dew point via a PC USB port • Power supplied by USB bus • Provided with Windows software for charting and logging results • –40 °C to +123 °C usable temperature range

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Product Guide

Smartsensors:VisionComp­any name/Model number Website/Descrip­tion

Advanced Micro Peripherals www.ampltd.com

AVC2000

A high-performance multimedia video capture and overlay card on a compact PC/104-Plus card format • 32-bit PCI architecture • Real-time image capture from a PAL or NTSC video source to the system memory or directly to the system display • Images can be captured continuously to system memory or disk for image processing, surveillance, and multimedia applications

Barco www.barco.com

FlexiVision III

High-performance Electro-Optical (EO) sensor video processing system • Real-time video processing (video fusion, image warping, image stabilization, super resolution) • Low-latency networked video using Gigabit Ethernet • Wavelet-based JPEG 2000 for lossless quality video streaming • Expandable number of input sources by adding FlexiVision III modules

COGNEX www.cognex.com

In-Sight 5100/5400Two industrial-grade vision sensors for machine vision applications • Both models meet IEC specifications for shock and vibration and have an IP67 (NEMA 6) rating for dust and wash-down protection • Acquires up to 60 full frames per second; higher speeds avail-able using partial acquisition

Computer Modules www.computermodules.com

CMI Ultra CameraA machine-vision-class CMOS camera with USB 2.0 connectivity • USB 2.0 allows the camera to interface to any desktop or portable PC that has a USB 2.0 port or an added interface card, eliminating the need for a frame grabber to capture images • Transfers a 1k x 1k image to the host at 17 fps

DALSA Coreco www.imaging.com

XRI-1200PC-based real-time digital image processor board • Engineered for demanding X-ray imaging applications • Performs adaptive image processing to reduce noise in both still and dynamic images significantly improving image quality and contrast

Electrim www.electrim.com

EDC-3000C/DColor (3000C) and monochrome (3000D) USB 2.0 scientific-grade camera systems • 1280 x 1024 • Scientific-grade CMOS sensor • Progressive scan • Low noise/low light levels • Exposure time up to 10 seconds • Subarray (region of interest) scanning, with frame rates greater than 3,000 frames/sec using a very small ROI • External triggering

Eonic BV www.eonic.com

Image Processing SystemSmall, lightweight, power efficient (3U CompactPCI form factor) • Flexible, reprogram-mable, agile • Real-time image processing for multisensor synchronization (RF and video) and template matching, image enhancement, target recognition • Fully integrated system

Evalue Technology www.evalue-tech.com

XQure SeriesA digital video recorder for surveillance applications • Low-power Intel Pentium M processor • Industrial-grade mainboard • 3 to 5-year life cycle • Supports up to 16 cameras for display and recording • MPEG-4 compression technology • Supports 320 x 240, 640 x 240, and 640 x 480 resolutions

32 / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Product Guide

Smartsensors:VisionComp­any name/Model number Website/Descrip­tion

Great River Technology www.greatrivertech.com

HOTLink II Video Frame Grabber (HL2V)

Frame grabber and camera emulator • Based on the Cypress HOTLink II Interface that is Fibre Channel compatible at the FC-0 and FC-1 layers • Supports a wide range of video formats with baud rates from 160 Mbps to 1.5 Gbps • Dedicated SXGA video port for viewing live video on a standard computer monitor

Kontron www.kontron.com

TEK-380VIPer Vision video interface module offers real-time digital video windows for industrial environments • Produces high-quality video for LCDs and analog CRT screen without occupying bandwidth at the system bus level

Matrox www.matrox.com/mga

Iris P-SeriesA line of programmable smart cameras that supports PC-based machine vision systems • Hardware for image sensing and a software API for image analysis • Ultra-Low-Power (ULP) Intel Celeron processor • Runs the Windows CE.NET Real-Time Operating System

National Instruments www.ni.com

Ni CVS-1455A high-performance, compact machine vision system optimized for inspection • Supplies nearly twice the processing power and four times the storage of the NI CVS-1454 • Performs at 1,436 MIPS; more than three times the processing power of a typical smart camera • Choice of more than 50 IEEE 1394-compliant cameras

VC www.vision-comp.com

SBC4018 OEM Board Smart Camera

OEM board smart camera • Operates in real time • Multitasking abilities • DSP onboard • Progressive scan 640 x 480 CCD • Remote head • SDK • TCP/IP stack • 32 MB DRAM • 4 MB flash ROM • Digital IOs • Light control

XILINX www.xilinx.com

V4SX35 Video Starter Kit

An ideal hardware platform to evaluate Xilinx FPGA in a wide range of video and imaging applications • Fully integrated and supported by the Xilinx System Generator for DSP software • Utilizes high-speed Ethernet hardware cosimulation capability and enables system integration, development, and verification of codecs, IP, and video algorithms in real time

Smartsensors:ZigBeeRadiocrafts www.radiocrafts.com

RC2200AT-SPPIOZigBee-ready module with embedded SPPIO application profile • Simple UART interface and AT commands • Small size: 16.5 x 35.6 mm • Shielded module with integrated antenna • Self-configurating mesh network • Very low power consumption

I n d u s t r i a l E m b e d d e d S y s t e m s Spring/Summer 2006 / 33

The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word The Final Word THE FINAL WORd

By Jerry Gipper

Industrial Embedded Systems is focused on intelligent technologies driving Industrial Transformation, but what do we mean when we say Industrial Transformation? What industries are included in this transformation? What types of transformations are covered?

The International Human Dimensions Programme on Global Environmental Change (IHDP) has an interesting definition of Industrial Transformation that I find suitable for the uses of Industrial Embedded Systems. An IHDP research paper on Industrial Transformation provides some background for my discussion.

Industrial Transformation is based on the assumption that important changes in production and consumption systems will be required in order to meet the needs and aspirations of a growing world population while using environmental resources in a sustainable manner. Industrial Embedded Systems is specifically focused on intelligent electronic technologies – computing, human interface, networking, sensors/control, and storage, including hardware and software, that fuel this change within relevant markets. This means the devices we cover will usually have intelligence in the form of an embedded processor of some type that allows a combination of hardware and software to make it function in a predetermined manner.

To distinguish the Industrial Embedded Systems coverage from the typical industrial applications involving only items such as factory and process automation and control, Industrial Embedded Systems provides a broader definition for industrial applications.

To capture the essence of industrial disciplinary fields, IHDP developed a framework, shown in Figure 1, that fits our purpose.

The rows reflect the actors or supply chain, while the columns describe a set of human activities aimed at meeting specific human needs. Through this multidisciplinary framework, Industrial Embedded Systems gives the broadest and most relevant context to industrial markets covered in our editorial.

IHDP further defines systems in the framework of Industrial Transformation as:

A chain of interrelated economic activities aimed at providing a specific need for society (e.g., energy, food, water, shelter, and transport). Such a system includes the actors (government, producers, and consumers), the flow of goods and/or services they deal with (including the metabolism along the chain), and the overall physical and institutional setting in which they operate.

Players within this framework that develop and use intelligent technologies will be our primary focus. Interesting and clever application of intelligent technologies that can have a significant impact on transformation will especially catch our attention.

We will emphasize the impact the technology has in driving the Industrial Transformation, why it is important, and how technology changes the way things are done in industrial applications. We invite your ideas and input.

Industrial Transformation – it’s all about change

3� / Spring/Summer 2006 I n d u s t r i a l E m b e d d e d S y s t e m s

Shelter FoodMaterial

Processingand Use

EnergyRecreation

andTourism

FinancialServices

Informationand

CommunicationTransport Water

Macro-Systemsand

Incentive Structure

ProductionSystem

ConsumptionSystem

Figure 1

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