We introduce a novel workflow, QCue, for providing textual stimulation during mind-mapping. Mind-mapping is a powerful tool whose intent is to allow one to externalize ideas and their relationships surrounding a central problem. The key challenge in mind-mapping is the difficulty in balancing the exploration of different aspects of the problem (breadth) with a detailed exploration of each of those aspects (depth). Our idea behind QCue is based on two mechanisms: (1) computer-generated automatic cues to stimulate the user to explore the breadth of topics based on the temporal and topological evolution of a mind-map and (2) user-elicited queries for helping the user explore the depth for a given topic. We present a two-phase study wherein the first phase provided insights that led to the development of our work-flow for stimulating the user through cues and queries. In the second phase, we present a between-subjects evaluation comparing QCue with a digital mind-mapping work-flow without computer intervention. Finally, we present an expert rater evaluation of the mind-maps created by users in conjunction with user feedback.

Drawing curves in mid-air with fingers is a fundamental task with applications to 3D sketching, geometric modeling, handwriting recognition, and authentication. Mid-air curve input is most commonly accomplished through explicit user input; akin to click-and-drag, the user may use a hand posture (e.g. pinch) or a button-press on an instrumented controller to express the intention to start and stop sketching. In this paper, we present a novel approach to recognize the user's intention to draw or not to draw in a mid-air sketching task without the use of postures or controllers. For every new point recorded in the user's finger trajectory, the idea is to simply classify this point as either hover or stroke. Our work is motivated by a behavioral study that demonstrates the need for such an approach due to the lack of robustness and intuitiveness while using hand postures and instrumented devices. We captured sketch data from users using a haptics device and trained multiple binary classifiers using feature vectors based on the local geometric and motion profile of the trajectory. We present a systematic comparison of these classifiers and discuss the advantages of our approach to spatial curve input applications.

Built-in self-repair (BISR) techniques are widely used for the repair of embedded memories. One of the key components of a BISR circuit is the built-in redundancy-analysis (BIRA) module, which allocates redundancies according to the designed redundancy analysis algorithm. Thus, the BIRA module affects the repair rate of the BISR circuit. Existing BIRA schemes for RAMs can provide the optimal repair rate (the ratio of the number of repaired RAMs to the number of defective RAMs), but they require either high area cost or multiple test runs. This paper proposes a BIRA scheme for RAMs, which can provide the optimal repair rate using very low area cost and single test run. Furthermore, the BIRA is designed as reconfigurable such that it can be shared by multiple RAMs. Experimental results show that the area cost for implementing the proposed BIRA scheme is much lower than that of existing BIRA schemes with optimal repair rate. A test chip is also implemented to demonstrate the proposed BIRA scheme.