CAEN News Center Home

The Michigan Engineering information technology environment is continually evolving. In order to keep the College community informed about what’s happening around the network, CAEN regularly posts news articles on this site. Important notices about current and upcoming service outages can also be found on the CAEN Service Status page.


Peer annotation support shifting from nb to Perusall

Collaborative peer annotation tools help students actively engage with textbooks or other reading materials in a course. Instructors assign textbooks, articles, or PDFs, and students annotate these documents and respond to each other’s comments and questions.

CAEN has run a local instance of one of these tools, nb, since 2015. This local service will be discontinued for Fall 2018. Instructors who wish to continue with nb can still use the free MIT-hosted service (where nb was developed).

An alternative service called Perusall has since emerged that provides many of the same features, also adds Canvas integration, and includes the ability to purchase and use textbooks from many publishers. As with nb, instructors can upload their own PDFs to Perusall, and there is no cost for instructors or students (except when purchasing textbooks).

If you are interested in trying one of these tools, Perusall’s help and support resources provide a good starting point. You can also contact CAEN to discuss the options, and get help with any questions you may have.

 


Lecture Capture Service manual control no longer requires Adobe Flash

While most instructors opt for pre-set schedules to make recordings using the Lecture Capture Service, the system allows for manual control in cases where live movement of the camera or impromptu starting-and-stopping of the recording is desired. Previously, using the manual control required a web browser with Adobe Flash support. Flash has been superseded by improved web technologies, and we are happy to report that this last dependency on Flash has been removed from the recording system.

The functionality and appearance is very similar to the previous version. To try it out, simply create a manual recording and choose to connect with the new control interface:

Starting Fall 2018, the old Flash-based interface will no longer be an option. Also note that we still offer M-Cam for manually controlling the recorder from an iOS device such as an iPhone or iPad.

 


LabVIEW Boot Camp for Summer 2018 (sponsored by CAEN)

The LabVIEW Boot Camp is a compressed LabVIEW Core 1 and Core 2 training. Designed specifically for academic researchers, educators, and graduate students, this free, five-day hands-on course teaches you a basic understanding of coding with LabVIEW as well as the fundamental skills needed to develop applications using the state machine design pattern to process, display, and store real-world data and programmatically control a user interface. At the end of the course, you are given the opportunity to prepare for and take the Certified LabVIEW Associate Developer (CLAD) exam, a globally recognized entry-level certification exam for free.

The training will be held July 16 through 20 in the IOE Building, and is sponsored by CAEN. Space is limited, so registration is required to attend.

For more information on the LabVIEW Boot Camp and to register, visit: https://events.ni.com/profile/form/index.cfm?PKformID=0x247058abcd

 


Cable-free presentation available in CoE classrooms

All 50 College of Engineering (CoE) classrooms are equipped with Apple TVs to allow the presentation of slides or other content without physically plugging-in a device. The type of connection supported, called “AirPlay,” is built into Apple devices including laptops, iPhones, and iPads. You do not need to be on a particular wireless network for this to work. Apple provides some instructions on how to use AirPlay:

AirPlay video from your iPhone, iPad, or iPod touch

Use AirPlay to display video from your Mac on an HDTV

What if you are not using an Apple device? CAEN offers licenses for a utility called “AirParrot” to instructors using Windows-based computers and tablets in CoE classrooms. Note that it is necessary to connect to a special wireless network called “CAEN-Presenter” before AirParrot will work with any device. This network requires a password, which we can provide on request.

Why might you consider using wireless to display? One advantage is you do not need any special adapters, which may be needed to connect certain devices to the room’s HDMI or VGA cables. Also, if you have a tablet computer, wireless frees you to walk the stage and annotate in real-time, or even pass the device to a student to demonstrate a solution if desired.

What are the steps to get connected? It’s pretty simple. Just select the “Apple TV” source on the room’s touchscreen panel. Then connect your device to the AirPlay destination named for the room number, for example, “Dow 1013 Apple TV.” To ensure the connection to the display is coming from inside the classroom, a 4-digit code will be shown on the screen, and your device will prompt you for this code to enable the connection.

The big question: Is this all reliable? The wireless connection is typically very reliable, however it is possible that interference from other devices can cause a disconnection. CAEN is working to minimize this by relocating antennas and upgrading hardware as needed. If you run into any problems with wireless display, let us know at caen@umich.edu. A backup option is to use the wired connections at the lectern, and we are available to advise on what adapters, if any, would be needed for your device.

If you would like to meet with CAEN staff in a classroom to go over the process for getting connected, or if you have any other questions about presenting over a wireless connection, contact us anytime at caen@umich.edu.

 


Demystifying Deep Learning: A practical approach in MATLAB Seminar & Workshop March 13

Are you new to deep learning and want to learn how to use it in your work? Deep learning can achieve state-of-the-art accuracy in many human-like tasks such as naming objects in a scene or recognizing optimal paths in an environment. Please join us for a MathWorks Seminar and Workshop on Deep Learning using MATLAB, sponsored by CAEN.

Registration is required to attend. Please register at:​ www.mathworks.com/umich

Session 1 (Seminar) – Demystifying Deep Learning: A Practical Approach in MATLAB
When: Tuesday, March 13, 3:00 p.m. – 5:00 p.m.
Where: Duderstadt Center 3336 (Advanced Training Lab 1)

In this seminar, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate the low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, and figure out the drivable area in a city environment.

Along the way, you’ll see MATLAB features that make it easy to:

  • Manage extremely large sets of images
  • Visualize networks and gain insight into the black box nature of deep networks
  • Perform classification and pixel-level semantic segmentation on images
  • Import training data sets from networks such as GoogLeNet and ResNet
  • Import and use pre-trained models from TensorFlow and Caffe
  • Speed up network training with parallel computing on a cluster
  • Automate manual effort required to label ground truth
  • Automatically convert a model to CUDA to run on GPUs

Session 2 (Workshop) – Practical Applications of Deep Learning – A Hands-On MATLAB Workshop
When: Tuesday, March 13, 6:00 p.m. – 9:00 p.m.
Where: Bob & Betty Beyster Building 1670

Deep learning achieves human-like accuracy for many tasks considered algorithmically unsolvable with traditional machine learning. It is frequently used to develop applications such as face recognition, automated driving, and image classification.

In this hands-on workshop, you will write code and use MATLAB to:

  • Learn the fundamentals of deep learning and understand terms like “layers”, “networks”, and “loss”
  • Build a deep network that can classify your own handwritten digits
  • Access and explore various pre-trained models
  • Use transfer learning to build a network that classifies different types of food
  • Train deep learning networks on GPUs in the cloud
  • Learn how to use GPU code generation technology to accelerate inference performance

Anyone can register for the seminar, but if you plan on attending the workshop, the seminar is a prerequisite.

Reserve your spot now!

 


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