In this study, we investigated the way users memorize, analyze, collaborate, and learn new concepts on a Tangible User Interface (TUI). Twenty-seven pairs of apprentices in logistics ( N = 54) interacted with an interactive simulation of a warehouse. Their task was to discover efficient design principles for building storehouses. In a between-subjects experimental design, half of the participants used 3D physical shelves, whereas the other half used 2D paper shelves. This manipulation allowed us to control for the “representational effect” of 3D tangibles: the first group saw the warehouse as a small-scale model with realistic shelves, whereas the second group had access to a more abstract layout with rectangular pieces of paper. Both groups interacted with the system in the same way. We found that participants in the first group (i.e., who used 3D realistic shelves) better memorized a warehouse layout, built a more efficient model, and scored higher on a learning test. Additionally, students wore eye-tracking goggles while completing those tasks; preliminary results suggest that 3D interfaces increased joint visual attention, which was found to be a significant predictor for participants’ task performance and learning gains. Implications for designing TUIs in collaborative settings are discussed.

Wireless Sensor Networks (WSNs) allow the monitoring of activity or environmental conditions over a large area, from homes to industrial plants, from agriculture fields to forests and glaciers. They can support a variety of applications, from assisted living to natural disaster prevention. WSNs can, however, be challenging to setup and maintain, reducing the potential for real-world adoption. To address this limitation, this paper introduces SensorTune, a novel mobile interface to support non-expert users in iteratively setting up a WSN. SensorTune uses non-speech audio to present to its users information regarding the connectivity of the network they are setting up, allowing them to decide how to extend it. To simplify the interpretation of the data presented, the system adopts the metaphor of tuning a consumer analog radio, a very common and well known operation. A user study was conducted in which 20 subjects setup real multi-hop networks inside a large building using a limited number of wireless nodes. Subjects repeated the task with SensorTune and with a comparable mobile GUI interface. Experimental results show a statistically significant difference in the task completion time and a clear preference of users for the auditory interface.

Tabletop systems have become quite popular in recent years, during which there was considerable enthusiasm for the development of new interfaces. In this paper, we establish a comparison between touch and tangible interfaces. We set up an experiment involving several actions like translation and rotation. We recruited 40 participants to take part in a user study and we present our results with a discussion on the design of touch and tangible interfaces. Our contribution is an empirical study showing that overall, the tangible interface is much faster but under certain conditions, the touch interface could gain the upper hand.

This paper describes TinkerSheets, a paper-based interface to tangible simulations. The proposed interface combines the advantages of form-based input and paper. Form-based input allows to set an arbitrary number of parameters. Using paper as a medium for the interface keeps the interaction modality consistently physical. TinkerSheets are also used as an output screen to display summarized information about the simulation. A user study conducted in an authentic context shows how the characteristics of the interface shape real world usage. We also describe how the affordances of this control and visualization interface support the co-design of interaction with end-users.

In this article, we describe the application of an enhanced genetic algorithm to the problem of hardware-software codesign. Starting from a source code written in a high level language our algorithm determines, using a dynamically-weighted fitness function, the most interesting code parts of the program to be implemented in hardware, given a limited amount of resources, in order to achieve the greatest overall execution speedup. The novelty of our approach resides in the tremendous reduction of the search space obtained by specific optimizations passes that are conducted on each generation. Moreover, by considering different granularities during the evolution process, very fast and effective convergence (in the order of a few seconds) can thus be attained. The partitioning obtained can then be used to build the different functional units of a processor well suited for a large customization, thanks to its architecture that uses only one instruction, Move.