Here are three practical ways DMC has used AI and Python to optimize a real-time inspection system and deliver real results to our clients.

1. Profile Your Pipeline to Find the Bottlenecks

As you move from implementation to production, the slower parts of your code can become painful while remaining hidden. In fact, slow lines of code can show up in surprising places and, depending on how your pipeline is structured, have an outsized effect on your speed. The solution is to bring a magnifying glass to your code by profiling it.

I’ve been a heavy user of Jupyter notebooks inside VS Code, which allows me to use the AI coding agent of my choice to easily set up and run experiments against my inspection pipeline to understand how it runs in practice against specific examples and, more relevantly, how long each section of the pipeline takes to run.

For example, one recent system DMC worked on involved belt masking. A client needed to inspect yellow tortillas moving down a blue conveyor belt, so we had to isolate each tortilla on each frame, separating it from the background for later steps in our analysis. The naive-but-useful approach we started with computed a full-resolution color distance, each pixel relative to the average color of the belt. This gave us a reference for what a functionally correct approach looks like, but profiling the code in conversation with AI showed us that it took up the majority of its time analyzing each frame.

2. Redesign Expensive Metrics to be Computationally Cheaper

Once you know which stage is slow, the next lever is the algorithms inside it. Many vision metrics have a naive implementation that’s accurate but expensive, and a smarter implementation that’s nearly as accurate and far faster.

After profiling the inspection system, we looked for ways to optimize it. A faster approach involved shrinking the frame by 50% on each side to reduce the number of computations by 75%. Comparing against the full-frame approach, we saw minimal reduction in accuracy. Additionally, by computing distances, we were performing a square root operation before comparing the distance to a threshold parameter; we could ditch that operation by squaring the threshold parameter and then comparing the squared distance, which sped up the process even further with no reduction in accuracy.

Eventually, we switched from checking RGB color distance to an axis-aligned range, which had similar accuracy while drastically reducing the computational load per frame. AI was vital here for quickly brainstorming and drafting these alternative formulations so we could compare their performance and accuracy.

3. Use Synthetic Data to Stress-Test Thresholds and Edge Cases

Optimization isn’t only about speed; it’s also about ensuring the system remains correct under pressure and across the full range of inputs. It’s hard to verify with only a handful of real images, which is often the case at the pre-deployment stage of a project.

Synthetic data can fill this gap. By generating test subjects across the entire pass/fail spectrum, including rare failure modes, you can stress-test thresholds and confirm the optimized pipeline still behaves correctly on edge cases.

In our example of the tortilla inspection project, we used a Python script to programmatically generate AI images of tortillas with different levels and types of defects by varying the prompts. For example, each tortilla prompt was given a probability of having a prompt addition specifying a torn edge or a burn mark. After generating 100 tortillas, we used another script to create simulated footage of these tortillas moving down a looping belt texture, with added random noise and motion blur, which we then used to verify our pipeline’s capabilities before we had any real footage to work with. These efforts allowed us to deploy our code into production quickly for our client.

Conclusion

These threThese three practices work together to create a more efficient and reliable system:

• Profiling identifies where to focus your optimization efforts.
• Metric redesign speeds up slow stages exposed by your profiling.
• Synthetic data ensures your system remains accurate across edge cases.

Each of these stages has always been essential to vision inspection, but AI and modern coding tools have made them faster and easier to implement. Additionally, unlike certain uses of AI that have greater exposure to risk, like code implementation, using AI here is less likely to cause major faults in your system. That said, AI should be an accelerator, not a replacement for engineering judgment. It is essential to verify that AI-generated code is correct and aligned with system requirements.

Ultimately, the impact of these practices is clear. By combining these approaches, you can confidently improve the speed and reliability of your real-time vision inspection pipeline.

The post 3 Ways AI Improves Real-Time Vision Inspection with Python appeared first on DMC, Inc..

To prove to myself that it could not have been an issue of technological limitations (and to have an excuse to play with a Cognex camera, ABB’s RobotStudio, and the Star Wars toys I keep on my desk), I decided to spin up a quick tutorial to show that I could, in fact, locate and retrieve the droid I was looking for.

RobotStudio Integrated Vision

The ABB RobotStudio’s “Integrated Vision” feature is essentially a “Cognex In-Sight Lite,” and as of publication, this is only functional on the 32-bit version of robot studio.

To access the Integrated Vision tab, expand your robot controller (physical or virtual) in the left-hand controller pane and right-click on the Vision System item. Right-clicking will open the Integrated Vision tab of RobotStudio.

Connecting to the Camera

Connect the camera to the robot controller service port switch. Once connected, the camera should appear under Vision System in the left-hand controller tree.

If not, then from the vision tab, select Connect/Add Senor. In this window, you should be able to see all cameras physically connected to the network and can change network settings of the cameras accordingly.

If you are connecting a camera (or an emulated camera) to a virtual robot controller, you will need to define the virtual programming port of the virtual controller to which the camera is connecting.

To do so, find the vc_network_definition.xml file in the RobotStudio/Systems/Controller Name/Home/IV folder. In this XML file, set the IP Address/Subnet to that of the NIC connected to the camera.

Warm restart the virtual controller you are using, and an unconfigured camera should appear under Vision System in the system tree.

Calibration

Now that the camera is connected, you need to calibrate the images from the camera to real-world units of measurement (pixels to millimeters). The easiest way to do this is with the grid calibration method.

To do so, select the Calibrate button from the top ribbon, then select Grid from the Calibration Type drop-down in the context menu. Selecting grid will pull up the configuration options for the grid calibration sizing.

Verify the selection of Checkerboard, with fiducial for the grid type. The other options can be left as is (see below).

The only option that may need adjustment would be the lens model. Select radial for straight-on pictures (for example, top-down) view, or choose projection if the camera is not parallel with the target object (for example, being mounted perpendicular to it).

Once the grid parameters have been set up, select Print Grid to print off a copy of the grid.

Verify the spacing of the checkers on the grid matches the selection in the previous view.

Center the grid within the field of view of the camera, and tape it in place (it’ll be important later that the grid does not move after the calibration image has been taken).

Once in place, select Next in the context menu with the grid setup, and then select Acquire Image within the context menu.

Once the image has been acquired, and the camera can recognize the calibration image, select the Calibrate button to load the calibration data into the active camera job. If the camera does not recognize the calibration image, it may require adjusting the camera position, exposure, or focus.

Now that the camera is calibrated, you will need to align the origin and orientation of the camera image (defined by the location of the fiducial in the calibration grid), with a workobject defined in the robot.

To do so, using a defined calibration pointing tool, define the camera workobject along the same X/Y axis as is on the taped calibration grid (only calibrate the user frame, as the object frame will then be used to shift the coordinate system to move to identified objects). At this point, the camera and robot workobject coordinate systems should be aligned.

Identifying Part Location

Once the camera and robot are calibrated, you can begin setting up your part location tools to locate your target objects. Several tools are available in the Add Part Location Tool menu, all of which will report back part location (X,Y coordinate) and orientation (for brevity’s sake, I won’t discuss the individual configuration of each tool).

Once a location tool has been selected and configured to locate the desired part, the next step is configuring the output parameters to the robot RAPID program.

To do so, select the Output to Rapid button at the top, which will open the Setup Communication context menu. Using this menu, you can define which parameters (Result) of which tools (Group) you want to reference in the RAPID program.

At a minimum, you should send the Fixture.X, Fixture.Y, Fixture.Angle, and Pass or Fail parameters (I’ve also included the Fixture.Score parameter for reference to show how closely the tool models match the captured images).

RAPID Configuration

Once the vision job has been set up and calibrated, and the camera workobject has been defined to align coordinate systems with the camera, the actual part identification/movement can be done.

Using one of the out-of-the-box code snippets available in RobotStudio as a starting point (RAPID/Snippet/Integrated Vision), set up a procedure to execute a camera job. After the job is complete, set the camera object frame to be vision coordinates of the camera result.

  PROC MoveToDetectedObject()
      
      VAR cameratarget mycameratarget;
      VAR wobjdata mywobj;
      VAR tooldata mytool; 
      VAR robtarget myrobtarget;
      VAR string myjob := "myjob.job";
      
      CamSetProgramMode RobotCamera;
      CamLoadJob RobotCamera, myjob;
      CamSetRunMode RobotCamera;
      
      CamReqImage RobotCamera;
      CamGetResult RobotCamera, mycameratarget;
      mywobj.oframe := mycameratarget.cframe;
      
      !During the first cycle, run the program until this point,
      !then jog the tool to the desired grip position and modpos myrobtarget.
      MoveL myrobtarget, v100, fine, mytool \WObj:=mywobj;
  ENDPROC

  PROC MoveToDetectedObject()
      
      VAR cameratarget mycameratarget;
      VAR wobjdata mywobj;
      VAR tooldata mytool; 
      VAR robtarget myrobtarget;
      VAR string myjob := "myjob.job";
      
      CamSetProgramMode RobotCamera;
      CamLoadJob RobotCamera, myjob;
      CamSetRunMode RobotCamera;
      
      CamReqImage RobotCamera;
      CamGetResult RobotCamera, mycameratarget;
      mywobj.oframe := mycameratarget.cframe;
      
      !During the first cycle, run the program until this point,
      !then jog the tool to the desired grip position and modpos myrobtarget.
      MoveL myrobtarget, v100, fine, mytool \WObj:=mywobj;
  ENDPROC

In the example above, the VAR declarations are only shown for clarity, and should most likely be declared in the user module. Additionally, if only one job is being used, the camera job can be loaded (CamLoadJob) in a startup routine to cut down on job cycle time.

To align the position of the targeted object identified by the camera to a robot position (for example, a pick or place position), you will need to run the procedure you created above through the point where the object frame of the workobject is loaded with the location data from the camera.

What this will do is align the robot position you are about to teach to be relative to both the location of the camera coordinate system (defined by the calibration procedure above), as well as the recognized position of the target object within the coordinate system.

You can now jog the robot to the desired position, being sure that the correct workobject and tool are loaded that will be used in your sequence, and save the position. Now, the position of the robot is defined as being relative to the identified position of the object, allowing the robot to consistently track the location and orientation of the object as it moves in the camera coordinate system.

Example

In the pictures below, you can see how the camera is able to track the location and orientation of BB8 as he moves to various locations along a wall in our lab. To do so, I first created a part location tool called BB8_Finder. I chose to search just for his head, as I thought that would be the best way to find a consistent location and orientation for BB8.

Pictured below is the reference picture used by the inspection tool, which looks like a BB8 mugshot.

Once I had my tool configured, I took a few shots of BB8 in different positions and orientations to verify that I could successfully track his movement (and for the record, that is “The Force” holding BB8 in place and definitely not tape…). In the tables below, you can see the camera tracking the position and orientation of BB8 in each of the pictures (the origin is located in the top-left of the picture).

Conclusion

You should now be able to track down and retrieve any droids you are looking for. 

Learn more about DMC’s Machine Vision Inspection and Integration Services. 

The post ABB Vision-Guided Movement – These ARE the Droids You’re Looking For appeared first on DMC, Inc..

 
Preparation was key for this project. We selected a camera, chose a software plan of attack, mounted the camera, played some pool to “test”, and created a camera driver. The camera chosen was a basic webcam with a 90 degree field of view and 1080p resolution. This made for a wide enough image to capture the pool table felt and the rails. The chosen software was LabVIEW as DMC has experience using NI’s versatile VAS and VDM software libraries with an integration to our existing custom Java based pool application. Once we wrote the driver we were able to start on algorithm development on FedEx Day.

 
The following software was developed on FedEx Day: Software Architecture/Application, Calibration Algorithms, Ball Location Algorithm, Ball Color and Pattern Algorithm, “Active Shooter” Algorithm. The architecture utilized a Producer/Consumer architecture with a single state machine to collect an image and determine the state of the pool table. Each ball was calibrated by snapping an image of the ball to get a known representation of each balls RGB value and whiteness. Each balls location was detected by HSB differences between a ball and the felt. Based on the calibration images captured for each ball, the RGB best match of each detected ball was matched to the corresponding calibrated ball. The pattern of a ball, striped vs. solid, was determined by white content of each detected ball. More white meant striped and less meant solid; lots of white meant the cue ball. To determine when to analyze the state of the pool table, an algorithm was written to detect whether a player was shooting and therefore blocking the camera’s view of all the balls. 

After a day of programming, we did it! The system correctly identified the location of the balls and what balls were left on the table. However, there were some challenges along the way. Lighting, RGB vs. HSB and the USB standard just to name a few. As always, lighting is very important in vision applications. The issue we ran into was shadowing around each ball. The shadowing ruined the RGB color measurement around the edges of the ball which could lead to incorrect ball color detection. HSB is a valuable domain to work in and was useful in distinguishing between the felt of the table and the color of the balls. As far as the USB standard issue, we used a USB webcam for the proof of concept. Although not a devastating issue, we needed to shorten our USB connection to the camera to be under 5 meters since our initial cable was longer and caused camera connection issues.

There are a lot of fun future applications we can do now that we can detect what balls remain on the table. The next major step will be to integrate it with our existing 3/5-way cutthroat application to automatically update what balls are sunk. If you have any fun ideas let me know by posting them below!
 

The post Pool Vision Application appeared first on DMC, Inc..

We gathered at Navy Pier on a beautiful fall day, ready to enhance our technical knowledge, connect with other local NI users, and introduce visitors to DMC. Our tilt table demo proved to be a popular draw and DMC's team had a great time showing it off at the booth. 

As always, meeting and developing relationships with National Instruments and peer integrators was a highlight of the day. We even raffled off a Fitbit Charge HR. Congratulations to our winner, Sonny Mitric of Omron!

There were plenty of insightful technical sessions to choose from throughout the day. The Advanced DAQmx Property Modes presentation offered a deep technical dive into a useful topic that's applicable in many different applications. We also attended sessions on LabVIEW for Embedded Systems and gained hands-on experience using CRIO for Real-Time and FPGA application. Another course demonstrated good electrical engineering wiring practices, shielding, and noise reduction.

DMC is an active participant in the Chicagoland LabVIEW User Group, so we were excited to see the Midwest User Group at NIDays. We stopped by the Midwest User Group booth and met up with users from across the region at a lunch meeting. 

We wrapped up the event with a fun exercise as part of the closing keynote session. Building a LEGO duck as a demonstration of stem execution was an enjoyable way to end a valuable day of learning and networking. 

Don't miss DMC at NIDays Boston 2015 on November 17!

Learn more about DMC's Test & Measurement Automation services.

The post Duck, Duck, DAQmx: NIDays Chicago 2015 Recap appeared first on DMC, Inc..

Interested in having us help with your integration? Here are some projects we have done to integrate vision into LabVIEW!

Learn more about DMC’s LabVIEW Vision Application Development services.

The post LabVIEW VAS Camera Timeout Fix appeared first on DMC, Inc..

NIWeek is always an exciting  few days of learning, exposure to new technologies, and connecting with industry peers. This year, DMC will be represented by several employees from across our network of office locations in Chicago, Boston, Denver, Houston, and New York. If you’re attending NIWeek look out for the following DMC representatives and stop by DMC’s booth 533 to say hello!

Ken Brey, Technical Director DMC Office: Chicago, IL Bio: Ken is a licensed Professional Engineer in Illinois as well as AIA Certified Vision Professional. He was nominated for Test Engineer of the Year by Test & Measurement World. Ask Me About: 3D vision techniques that are available and vision FPGA. Best Part About Working in Chicago: The variety of industries and diversity of clientele is unmatched elsewhere in the country. Chicago has some of everything. Read Ken’s Employee Spotlight interview.

Jon Carson, Project Director DMC Office: New York, NY Bio: Jon is a licensed Professional Engineer in Illinois. He is experienced with Allen Bradley and Siemens PLCs as well as ELAU motion controllers.Among his certifications are Microsoft Certified Solutions Developer C# and German Technical Translation. Ask Me About: Developing a new end-of-line test station for an automotive sensor manufacturing line using LabVIEW. Best Part About Working in NYC: Living near my family and being a five minute walk from anything I want to do. Read Jon’s Employee Spotlight interview.

Katherine Czaplicki, Systems Engineer DMC Office: Chicago, IL Bio: Katherine is a Certified LabVIEW Developer who has been working in LabVIEW since her first day at DMC. She is excited to be returning to her home state of Texas for NIWeek. Ask Me About: Automated vision inspection systems. Best Part About Working in Chicago: Attending the festivals that are held all summer.

Dan Freve, Project Director DMC Office: Boston, MA Bio: A Massachusetts native, Dan is the lead of DMC’s Boston office. He became a Certified LabVIEW Developer in 2008 and is highly experienced with MATLAB. Ask Me About: Programming DMC’s Tilt Table Demo using an NI cRIO with integrated FPGA running in LabVIEW. Best Part About Working in Boston: Spending time sailing in the Boston Harbor. Watch Dan’s Employee Spotlight video.

Otto Gottlieb, Project Engineer DMC Office:Denver, CO Bio: Otto used to develop testing applications for medical devices. He’s experienced in a broad mix of programming including Siemens PLCs, Allen-Bradley PLCs, and .NET applications. Ask Me About: End-of-Line testing applications. Best Part About Working in Denver: I get to ski all winter and hike all summer! Watch Otto’s Employee Spotlight video.

Darren Jones, Project Director DMC Office:Chicago, IL Bio: Darren is a Certified Professional Instructor and Certified LabVIEW Architect. He is DMC’s go-to person for all things National Instruments-related. Ask Me About: Designing DMC’s myRIO-based solar powered garden system. Best Part About Working in Chicago: Enjoying the skyline from our rooftop deck! Read Darren’s Employee Spotlight interview.

Jason Mayes, Project Director DMC Office:Houston, TX Bio: Jason recently returned to his home state of Texas to lead the DMC Houston office. He earned his B.S., M.S., and Ph.D. from the University of Notre Dame. Jason is a Certified LabVIEW Developer and has experience in C++, C#, Siemens, Allen-Bradley, and a host of other platforms. Ask Me About: Vision. I used vision feedback to close the loop on a 3D electron beam welder for the rapid prototyping of titanium parts. Best Part About Working in Houston: BBQ, TexMex, and the Houston Rodeo. Watch Jason’s Employee Spotlight video.

DMC will be located at booth 533 in the NIWeek exhibition hall.

Stop by DMC’s booth to see our Tilt Table demo in action, which uses National Instruments cRIO, FPGA, and LabVIEW technologies.

Learn more about DMC’s Test & Measurement Automation services.

The post DMC to Exhibit at NIWeek 2015 appeared first on DMC, Inc..

The first time I saw a blue LED was in a trade show in 1998. One booth of lighting equipment has a light that was a brilliant deep blue. I asked the man about the product, and he said the blue LEDs were just out, and cost him $15 each. His small light product had hundreds of them – hand soldered in a solid square pattern. 

Today, blue LEDs can be found everywhere. In our homes and businesses, white LED lighting is becoming more popular as the most efficient form or architectural lighting. White LED products actually work by placing blue or ultraviolet LEDs behind white phosphorescent materials. We are more likely to see blue LEDs in their raw form as part of today’s brilliant outdoor advertising signs and stadium displays.

In machine vision, we use blue LEDs as general purpose lighting for monochrome systems. In 20 years since their invention, blue LEDs are twice as bright as any other color. We also use blue LEDs in combination with red and green to create balanced lighting that can appear white, or about any other color. The same technology is also used in the multi-color mood lighting of my car. 

Because blue light has a shorter wavelength than other visible light, it makes clear images in microscopic and medical imaging. In that field, blue, or multi-color light sources including blue have largely replaced incandescent light sources with color filters.

It took 30 years from the commercial production of the first red LEDs to the production of the first blue LEDs. And during that time the importance of blue was not lost on anyone in the industry.  It obviously took great skill and dedication to make this leap which we all now benefit from.

So, thank you to Isamu Akasaki, Hiroshi Amano, and Shuji Nakamura for your invention and congratulations on your prize.

The post Nobel Committee Recognizes Inventors of Blue LED appeared first on DMC, Inc..

DMC is honored to demonstrate our expertise by having the most LabVIEW Certified Architects in the Midwest, as well as an all over strong team of LabVIEW Certified engineers.

NI has three different levels of LabVIEW Certification: Associate Developer, Developer and Architect.

Recently, we added five new engineers to our roster of LabVIEW proficient employees. DMC now has a total of nine Certified LabVIEW Architects, three Certified Professional Instructors, two Certified LabVIEW Developers and three Certified LabVIEW Associate Developers. Check out our full LabVIEW team below:

CLAs (Certified LabVIEW Architects):

• Patrick Corcoran 
• Eric West 
• Ashley Enderlin 
• Mark Locascio 
• Jeremy Green 
• Nick Aroneseno 
• Jesse Batsche 
• Leon Grossman 
• Darren Jones 

CPIs (Certified Professional Instructors)

• Darren Jones
• Patrick Corcoran
• Nick Aroneseno

CLDs (Certified LabVIEW Developers):

• Matt Goedke
• Jason Mayes

CLADs (Certified LabVIEW Associate Developers):

• Devon Fritz
• Bill Sowerine
• Elizabeth Rapport
• Katherine Czaplicki
• Spencer Glesmann

Learn more about DMC’s LabVIEW programming services.

The post DMC Congratulates LabVIEW Certified Architects and Developers appeared first on DMC, Inc..

In case you didn’t make it to NIWeek or weren’t able to join us on August 6, we have posted the presentation to SlideShare. During the presentation, Dan reviewed the many challenges that Leary faces in the folding carton industry. For example, ensuring that glue is properly applied to more than 30 separate locations on a complicated six-pack beer carrier was essentially impossible without a machine vision system. This is especially true considering the folder-gluer can process 10 to 50 cartons per second depending on the size of the carton.

After Dan presented the challenges associated with the high-speed folding carton industry, Ken and I rose to these challenges and presented some theories on designing a vision system. We explained that our favorite hardware platform for high-speed, multi-camera, 100% up-time, machine vision systems is a PC running NI’s real-time operating system. This system is connected to standard cameras as opposed to a smart camera which performs image inspection using an onboard processor.

For many of our vision inspection projects NI’s FPGA cards and expansion chassis have been critical tools for controlling digital inputs and outputs for camera triggering and encoder tracking. Though we mainly focused on giving tips on using the FPGA in this way, I’m also excited about the new tools in the 2014 Vision Development Module that make it easy to offload some intensive image processing algorithms to an FPGA. We did not discuss this in our presentation because it is a brand new feature that was just unveiled at NIWeek 2014. I’m eager to use it in future projects.

We also shared some of the random bits of knowledge we have picked up from experience on these projects. Some of my favorite “Knowledge Nuggets” are:

• Avoid creating and destroying image references. Use a static pool of image references that you create once when the program initializes.
• Use integer math instead of floating point math where possible. We demonstrated a 2.5x increase in processing speed for the same mathematical calculation using integers compared to double-precision numbers.
• Use the VI Profiler tool to determine which VIs are taking the most time to execute. This is how we discovered that some of our VIs were spending too much time creating and destroying image references.

Overall, the presentation went well and was well-received by the audience. I hope that audience members gained some useful tips that they can use on their own projects.

NIWeek 2014 had a vision summit that included some other interesting announcements. I already mentioned the FPGA Vision IP that I am excited to use. Another great new product is the cRIO with integrated display for user interfaces. NI’s real-time operating system is great for high-speed vision inspection that requires 100% availability and reliable, deterministic processing. The only downside was that there was no way to program an interface in the real-time operating system. Our other RT vision systems use a separate PC (typically running Windows) to serve as the HMI, communicating with the RT PC via TCP protocols. Now, we can streamline our development by integrating the HMI into the real-time program. Good job, NI!

The post High-Speed Vision Inspection Presentation at NIWeek 2014 appeared first on DMC, Inc..

This was my first NIWeek, and having heard great things from other friends and DMCers, I was excited to experience all that it had to offer. With such a fast-paced week, I decided to focus on three topics that I was interested in learning more about: Embedded Systems, designing better user interfaces (UI’s), and learning more about how NI and LabVIEW are utilized in the academic sphere.

I was able to attend several sessions focused on the Embedded Systems track and learn more about good RT and FPGA programming practices. In addition, I had the opportunity to play (hands-on) with some of NI’s latest hardware, including their new Linux-based cRIO. Dan Freve and I agreed that this was one of the highlights of the week: NI’s new offering is pretty sweet. In addition to the Linux based OS, it also provides a native video output to allow an embedded cRIO system to host its own UI (!!!). This is great news for developers and our clients and it will save a ton of time on debugging, development, and additional hardware costs. The new product will also allow us to take advantage of the RT front panel and eliminate the need to create a stand-along HMI for the host machine.  

There were also a great series of sessions on designing better and more innovative UIs. Sometimes as controls engineers, we get lost in functionality and lose sight of the ‘form.’ The end result is functional, but the UI might need some cosmetic improvement. These sessions presented a few good ideas for balancing this equation. Some of the cooler parts were using HTML5 UI’s over the internet and some methods for employing Measurement Studio to move the UI development into Microsoft’s WPF programming model.

Other topics covered at the event spanned a wide array of engineering and science specific fields. DMC’s own Darren Jones, Ken Brey, and Eric West gave a presentation in conjunction with Daniel McCarty, Sr. Software Development Manager at W.H. Leary Co., Inc. on high-speed vision inspection. Check out the entire presentation on SlideShare.

Another highlight of the week was talking with several college professors from universities around the country who are using NI hardware and software to train the next generation of programmers. I got a first-hand look at the myRIO and several cool examples and labs that are being used in undergraduate and graduate level courses at MIT. Now I want to buy my own! It also gave us an interesting look at ways we can better train our new employees as they get ramped up in LabVIEW.

In addition to the technical content, it was also a great time to do some team building and get to know our local NI reps a little better. 

Other notable takeaways from the four-day event included:

• Learning about FPGA Vision tools that are making NI more competitive
• Keynote speeches and presentations on the Internet of Things (IoT) and Industrial Internet

• Now that we’ve expanded to Denver, CO, NI Week was a great opportunity to meet potential clients, partners and techie residents of Colorado
• Taking a break at the Elephant Room to hear a six trombone jazz band called Jazzbones

• “Austin bats are pretty cool. We went to watch them come out from under the bridge one night, though I was hoping they would come out in a cloud in the shape of Batman’s logo. Instead they just come out really unorganized as if they have no team spirit.” – Otto 

• Networking and teambuilding activities such as paddle boarding on the Colorado River

The post NI Week 2014 Recap appeared first on DMC, Inc..