We propose HaptoFloater, a low-latency mid-air visuo-haptic augmented reality (VHAR) system that utilizes imperceptible color vibrations. When adding tactile stimuli to the visual information of a mid-air image, the user should not perceive the latency between the tactile and visual information. However, conventional tactile presentation methods for mid-air images, based on camera-detected fingertip positioning, introduce latency due to image processing and communication. To mitigate this latency, we use a color vibration technique; humans cannot perceive the vibration when the display alternates between two different color stimuli at a frequency of 25 Hz or higher. In our system, we embed this imperceptible color vibration into the mid-air image formed by a micromirror array plate, and a photodiode on the fingertip device directly detects this color vibration to provide tactile stimulation. Thus, our system allows for the tactile perception of multiple patterns on a mid-air image in 59.5 ms. In addition, we evaluate the visual-haptic delay tolerance on a mid-air display using our VHAR system and a tactile actuator with a single pattern and faster response time. The results of our user study indicate a visual-haptic delay tolerance of 110.6 ms, which is considerably larger than the latency associated with systems using multiple tactile patterns.

The Mining Software Repositories (MSR) field integrates and analyzes data stored in repositories such as source control and bug repositories to support practitioners. Given the abundance of repository data, scaling up MSR analyses has become a major challenge. Recently, researchers have experimented with conventional techniques like a supercomputer or cloud computing, but these are either too expensive or too hard to configure. This paper proposes to scale up MSR analysis using “general-purpose computing on graphics processing units” (GPGPU) on off-the-shelf video cards. In a representative MSR case study to measure co-change on version history of the Eclipse project, we find that the GPU approach is up to a factor of 43.9 faster than a CPU-only approach.