A recent post on Slashdot led me to this interesting video produced by the folks at Ishikawa Komuro Laboratory at the University of Tokyo. Using their Vision Chip, a CMOS image sensor with a parallel image processor, they created an in-air, finger motion based interface for a mobile phone. While I do not believe that in-air typing is the right application for this interface (a lack of tactile feel will I think turn off users), this certainly is a fascinating project that may be what we see commercially in the next couple of decades! Take a look at their video below. Also, if you haven’t done so, check out Zeljko’s post about Project Natal – a similar input interface for the future Xbox 360.

Mark Taipan

Yesterday Mark Taipan and I presented KLAS, or Kingsbury Location Awareness System at the Interdisciplinary Science & Engineering (ISE) Symposium for the 10th annual UNH Undergraduate Research Conference. We were able to share with the audience our project poster, our KLAS video, as well as have two PDAs on hand to provide user demos.

Here we see the KLAS URC presentation setup where both Mark and I are showing two interested engineers our project’s attributes.

This pictures (taken during one of our slower traffic moments) shows a little more of our presentation display.

Finally, we see both Mark (Left) and Myself (Right), standing next to our project poster.

In addition to these photos, we have posted many other pictures from this year’s URC here:

http://www.flickr.com/photos/eceblogger/sets/72157617236370816/

These pictures, as well as the ones above, were taken by Oskar Palinko. Thanks Oskar for capturing the event!

Overall this year’s URC was an excellent experience, and both Mark and I are proud to have been able to do the work to be involved!

Matthew Lape

Recently Mark Taipan and I completed the development of our Kingsbury Location Awareness System (KLAS) Video. Here you will see the development and operation of KLAS, and its principle functions, giving an example usage for both the Tour Guide and Navigation Applications. Hope you enjoy the video!

Matthew Lape

A few days ago I ran across an interesting device in a local electronic store. Finally it is time when such device arrived at our lives with satisfying performance for an affordable price. I found that buying it as a new PDA/phone accessory would be worth considering. It is an ultra-mobile (size: 2.0×0.9×4.5″, weight: 5.6 oz, battery life: 40-60 minutes) handheld projector which uses LED technology to project an image up to 50 inches in diagonal. It might potentially solve the burden of a small phone screen.

Although, the video shows some potential applications, it is still not clear what the killer app will be, how the mobile phone users will accept mobile projectors (regarding privacy), and how the projectors will enhance user experience and multi-user interaction.

Ivan Elhart

As you may know, Mark Taipan and I have been working on our KLAS Project. Recently we created a quick video demonstrating the KLAS Navigation system. Here it is:

Matt

With our Hampton, NH deployments just around the corner, we sought concrete evidence that the capacities of our handheld-device batteries, of Symbol MC70 / MC50 EDAs, would provide officers with Project54 (P54) services throughout the duration of their eight hour shifts. To do so, I’ve modified the existing structure of our automated button-presser, the P54-Testbot, now offering battery-usage logging of something I like to call “sequential routines”. Contrary to the normal operation of P54-Testbot, the sequenced operation allows for sets of button-presses to be categorized as sequences. Because of reduced display area, the handheld version of P54 has a significant number of button-locations declared as absolute screen-positions rather than P54-typical row-column declarations. To increase its ease-of-use, the sequential routine’s algorithms have been designed to support each method of button-creation. After the execution of a sequence the percentage of battery remaining is recorded and textually labeled with the sequence’s name. Sequences are then repeated at developer-defined frequencies. All parameters governing these sequential routines are editable through the device’s registry, pictured below, along with a termination value which causes P54 to exit if the main-battery level becomes less than some threshold.

Results from the sequences pictured above are depicted in the figure below. Slight differences in the definitions of the sequences’ iteration count per hour caused the variances between the origination points of the colored plots. I’ll spare you from most of the details, but not all. Each plot represents a test consisting of three sequences: one submitting a records query, another activating the barcode scanner, and a final capturing an image. The black plots represent tests of lower frequency sequences (about 22.5 minutes), while the red plots represent tests of higher frequency sequences (about 15 minutes). As we can see, even in the case of higher frequency sequences, the battery capacities of our handheld devices fall to 10% after a period of 10 hours, exceeding the 8 hour requirement.

Deployment results to come

Michael Farrar

Hello ecebloggers,

Last week, a group of us from the lab had traveled to Boston’s Northeastern University, attending the NECHFES student conference.  It was my first conference experience and I do have to say that it surpassed my expectations.  The atmosphere was very relaxed, and presentations were nicely sequenced with 10 – 15 minute breaks.  Of course, breakfast and lunch were served, both of which were outstanding, and free!  The keynote speaker, Daniel Serfaty from Aptima Inc, had some unique perspectives on the ubiquitous computing world of today, and tomorrow, very interesting!  Photos of my speech, entitle “Using voice to tag digital photographs on the spot” can be found here, and the complete presentation here.  I look forward to attending similar events.

Michael Farrar

In our last post Mark explained the software components and structure of KLAS (Kingsbury Location Awareness Systems). These components allow the system to take in data from an external source, estimate the user’s location (more on this in a minute) and then utilize that data to provide both navigation and tour guide capabilities.

The location aware portion of the system had to have a method of determining the user’s location through the use of some external infrastructure (e.g. GPS, Wi-Fi, RFID, etc.). After some reading and discussion, Mark and I decided that we needed a reliable indoor infrastructure, one that could either be easily developed, or ideally, one that was already established. We found, in the reading, that GPS indoors was considered mostly as an unreliable method, and RFID, although very accurate, was relatively expensive and too short range for our application. Looking at Wi-Fi, we found that it was acceptably reliable in this type of environment. The best part is that because Wireless Internet is so prevalent, the infrastructure in many locations is already established. It was thus why we chose to use Wi-Fi (IEEE 802.11) for our system.

To develop our specific location system using Wi-Fi, we had to investigate our target location (Kingsbury Hall) to determine the coverage and location of the Wi-Fi Access Points. We found eight UNH Wireless Access Points located on the second floor, equally spaced to cover the majority of the area. We then added two more Access Points in a few weak areas to give our system a total of ten Access Points to utilize.



Here we see two examples of the KLAS Access Points. The one on the left is a typical UNH Wireless Access Point, and on the right is one of the two Access Points that we added to provide stronger coverage in a few areas.

Our next step was to create an algorithm which would read in the Relative Signal Strength Indicator (RSSI) values from the Wi-Fi card on our PDAs and then determine the user’s location based on that data. To do this we spent a bit of time investigating the pros and cons of the methods used by other researchers, utilizing either probabilistic or deterministic models. We found, through testing and our reading, that Wi-Fi signals are highly susceptible to environmental changes such as the thickness and density of walls, the amount of people between the Access Point and the user, and even the relative humidity of the area. We decided, with the help of Professor Andrew Kun and Oskar Palinko, that due to the size of the area and the system’s environment, using a calibrated deterministic model would be best. This essential meant that we would evenly space master location points (also called reference points) throughout the floor. These points would then be compared to the user’s actual location to determine which master location they are closest to.

Through this testing we found that although the Wi-Fi structure was able to provide a reasonable estimate of the location, our initial points were too close, causing multiple real-time points to map to one reference point. This allowed use to gauge the reference location spreading, which eventually provided us with a total of 46 points for the entire second floor. After further testing with these points, it was found that there were still areas that had a tendency to “group” together. This was seen because of the fact that the building’s features caused certain areas to estimate the same master location. Recognizing this, we were able to break up the floor even more generally into 16 areas, which could be used by our system to categorize the user’s current location.



This map shows the even spread of the 16 user areas throughout the floor, as well as the locations of the ten KLAS Access Points. Dividing the floor into these areas allows for our system to have a built in buffer for the variations in the environment, as well as any variations that might be present in the user’s device itself. This allows for a more stable, reliable system, which is used, as Mark mentioned in his post, to provide navigation and tour guide capabilities for our users.

We hope to continue to refine our system in the next few months, making it even more reliable and stable. This will allow us to conduct more research in the area of the user interface, and hopefully give us the opportunity to develop a simple, intuitive system which will make KLAS a useful tool for all.

Matthew Lape

Hi ecebloggers,

In the past I’ve discussed the imaging application, and in particular, the tagging capabilities it provides.  Now it’s time to put the application to work.  Tagging of media, particularly photographs, has become a very popular and efficient means of organizing material on the internet and on personal computers.  Over a short period of time the technique has evolved from an optional feature to a must-provide service, and can be found within modern desktop and internet photo galleries.  However, tagging is normally accomplished long after the images have been captured, and possibly at the expense of in-the-moment information.  In this view, we hypothesize that tagging photos right after they are taken, or on the spot, will result in a larger number of tags than tagging photos long after they are taken.  We also hypothesize that the tags created on the spot will be perceived to better describe the photos by consumers of the photos.  Finally, we hypothesize that a convenient way of tagging photos on the spot is by using voice commands.  

To test these hypotheses we will conduct a study in which participants will be asked to introduce the University of New Hampshire campus in a number of low-resolution pictures captured using a Symbol MC50 PDA. Participants will be divided into two groups.  One group will be able to issue voice commands to select tags from a list while the other group will have to manually select or type in tags from the same list.  The images and their tags will be posted to an internet photo gallery, such as Flickr, which will allow us to recruit a third group of participants who will compare the quality of the tags created by the first two participant groups.  The study will be conducted throughout the months of November and December, so check back for the results soon after.  Below is a sample image of UNH posted on Flickr.  The tagging section is highlighted in red.

Michael Farrar

A few day ago, Prof Andrew Kun, Andras Fekete and I visited the Intelligent Environments ‘08 Conference in Seattle, WA. An earlier post already introduced this conference on eceblogger. We presented three works there. Andras had a great poster on the deployment of his new P54 PDA software. The poster session took place in the afternoon of the first day. I think his work drew the biggest crowd.

Andras presented the PDA study with great confidence and answered the questions flawlessly. Besides him, I also presented my research results from the past year. I had two oral presentations. The first one was on the steering wheel sensor device. This was a mixture of a regular slide-show presentation and a demonstration. For this purpose we shipped out a scaled down version of our driving simulator equipped with the new sensor. Here, we are testing the system right before the the start of the presentation.

Luckily, none of the equipment got broken during transportation, so everything worked perfectly. My other presentation took place in the afternoon of the second day. It was on the results of the PTT glove experiment that we mentioned here before. This presentation also went smoothly.

The conference was organized very nicely, with helpful hosts and great food. They even scheduled a visit for us to see the Microsoft Home project. The location of the conference was on the campus of the University of Washington in Seattle. It proved to be a beautiful place. I didn’t even know that there are campuses in the USA that are built in gothic style. I have seen this before only in Europe. Here Andras and Andrew explore the square in front of the landmark library building of the university, that looks more like a gothic cathedral.

Thanks go to Prof Kun for helping us and actively participating in writing all three papers (second author on all of them). Also, thanks to Erika Clifford for doing all the logistics for the trip and shipping the equipment.

Oszkar