Next-Generation Sequencing technologies generate a vast and exponentially increasing amount of sequence data. The Interleaved Bloom Filter (IBF) is a novel indexing data structure which is state-of-the-art for distributing approximate queries with an in-memory data structure. With it, a main task of sequence analysis pipelines, (approximately) searching large reference data sets for sequencing reads or short sequence patterns like genes, can be significantly accelerated. To meet performance and energy-efficiency requirements, we chose a co-design approach of the IBF data structure on the FPGA platform. Further, our OpenCL-based implementation allows a seamless integration into the widely used SeqAn C++ library for biological sequence analysis. Our algorithmic design and optimization strategy takes advantage of FPGA-specific features like shift register and the parallelization potential of many bitwise operations. We designed a well-chosen schema to partition data across the different memory domains on the FPGA platform using the Shared Virtual Memory concept. We can demonstrate significant improvements in energy efficiency of up to 19 times and in performance of up to 5.6 times, respectively, compared to a well-tuned, multithreaded CPU reference.

Recent mobile phones integrate fingerprint scanners to authenticate users biometrically and replace passwords, making authentication more convenient for users. However, due to their cost, capacitive fingerprint scanners have been limited to top-of-the-line phones, a result of the required resolution and quality of the sensor. We present Bodyprint, a biometric authentication system that detects users' biometric features using the same type of capacitive sensing, but uses the touchscreen as the image sensor instead. While the input resolution of a touchscreen is ~6 dpi, the surface area is larger, allowing the touch sensor to scan users' body parts, such as ears, fingers, fists, and palms by pressing them against the display. Bodyprint compensates for the low input resolution with an increased false rejection rate, but does not compromise on authentication precision: In our evaluation with 12 participants, Bodyprint classified body parts with 99.98% accuracy and identifies users with 99.52% accuracy with a retry likelihood of 26.82% to prevent false positives, thereby bringing reliable biometric user authentication to a vast number of commodity devices.

Current touch devices separate user authentication from regular interaction, for example by displaying modal login screens before device usage or prompting for in-app passwords, which interrupts the interaction flow. We propose biometric touch sensing, a new approach to representing touch events that enables commodity devices to seamlessly integrate authentication into interaction: From each touch, the touchscreen senses the 2D input coordinates and at the same time obtains biometric features that identify the user. Our approach makes authentication during interaction transparent to the user, yet ensures secure interaction at all times. To implement this on today's devices, our watch prototype Bioamp senses the impedance profile of the user's wrist and modulates a signal onto the user's body through skin using a periodic electric signal. This signal affects the capacitive values touchscreens measure upon touch, allowing devices to identify users on each touch. We integrate our approach into Windows 8 and discuss and demonstrate it in the context of various use cases, including access permissions and protecting private screen contents on personal and shared devices.

We explore how to track people and furniture based on a high-resolution pressure-sensitive floor. Gravity pushes people and objects against the floor, causing them to leave imprints of pressure distributions across the surface. While the sensor is limited to sensing direct contact with the surface, we can sometimes conclude what takes place above the surface, such as users' poses or collisions with virtual objects. We demonstrate how to extend the range of this approach by sensing through passive furniture that propagates pressure to the floor. To explore our approach, we have created an 8 m2 back-projected floor prototype, termed GravitySpace, a set of passive touch-sensitive furniture, as well as algorithms for identifying users, furniture, and poses. Pressure-based sensing on the floor offers four potential benefits over camera-based solutions: (1) it provides consistent coverage of rooms wall-to-wall, (2) is less susceptible to occlusion between users, (3) allows for the use of simpler recognition algorithms, and (4) intrudes less on users' privacy.

We present imaginary reality games, i.e., games that mimic the respective real world sport, such as basketball or soccer, except that there is no visible ball. The ball is virtual and players learn about its position only from watching each other act and a small amount of occasional auditory feed-back, e.g., when a person is receiving the ball. Imaginary reality games maintain many of the properties of physical sports, such as unencumbered play, physical exertion, and immediate social interaction between players. At the same time, they allow introducing game elements from video games, such as power-ups, non-realistic physics, and player balancing. Most importantly, they create a new game dynamic around the notion of the invisible ball. To allow players to successfully interact with the invisible ball, we have created a physics engine that evaluates all plausible ball trajectories in parallel, allowing the game engine to select the trajectory that leads to the most enjoyable game play while still favoring skillful play.