In an age of so many new wearable devices, e.g., smartwatches, glasses, rings, clothing, and so on, headphones can be recognized as the first widely adopted wearable device. They have been around for more than a century and have been used mostly as an output device for listening to music, or, in the recent decades, talking on the phone. Even more recently, Ohmic has developed a technology formed by a suite of hardware and software solutions that enables headphones to go beyond their initial purpose.
Current RFID tags process reveals that it is impossible to set the cost per tag to less than 5 cents. Similarly, area is a precious quantity. Much of the area in a tag is used by digital logic and capacitors. Merely adding more area is not a sustainable solution. This project will help the community to decrease the cost per tag to less than 5 cents as expected with much more compactness as compared to available tags.
The partner will benefit from the research by collaborating with one of the top research labs in the field.
We present a method for automatically estimating the lighting conditions from a single image. As opposed to most previous works which proposed methods that deal with individual aspects of the problem (e.g. indoors vs outdoors, parametric vs non-parametric), the proposed method unifies these ideas into a single, coherent framework. Our method will automatically estimate both parametric (individual light sources) and non-parametric (environment maps) lighting representations from both indoor and outdoor images.
he goal of this project is to achieve high quality real-time motion deblurring for images captured by cameras mounted on mobile devices. First, we will propose a novel two-camera technique that exploits the trade-off between spatial and temporal resolutions in capturing the photo with the camera movement information to be used in the deblurring process. Second, we will introduce new point-spread function (PSF) estimation algorithms by employing the motion information captured by the two-camera imaging device. Third, we will develop new deconvolution algorithms suitable for mobile devices.
he goal of this project is to achieve high quality real-time motion deblurring for images captured by cameras mounted on mobile devices. First, we will propose a novel two-camera technique that exploits the trade-off between spatial and temporal resolutions in capturing the photo with the camera movement information to be used in the deblurring process. Second, we will introduce new point-spread function (PSF) estimation algorithms by employing the motion information captured by the two-camera imaging device. Third, we will develop new deconvolution algorithms suitable for mobile devices.
This study proposes to evaluate the quality of movement in patients with low back pain using a novel device. We are going to measure and compare the movement of people with back pain to that of healthy people. In addition, we plan on correlating the psychological factors associated with low back pain to tissue pathology. Many previous studies have used self-report measures of movement, and now we will determine whether our method of measuring movement correlates with these self-reports.
This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as a curve; the dynamic evolution of this curve in time represents an individual’s spinal movements.
This project uses machine learning algorithms to better understand back movement and low back pain. We apply supervised learning time series algorithms to data collected from Backtracks’ wearable de-vice — which consists of a malleable think curve that reads data collected from the participants’ spine movements. At each time step, such movements are represented as a curve; the dynamic evolution of this curve in time represents an individual’s spinal movements.
The project is about removing noise from video signals (for example, those taken by a mobile phones or professional cinema camera). Our current software first detects the type and power of the noise and based on that remove the noise from video. This proposal has three objectives. The first is to handle a specific type of noise called structured noise, not handled yet in our denoising software developed in previous MITACS projects.
