The knee flexion-extension angle is an important criterion for the rehabilitation process after a rupture of the anterior cruciate ligament (ACL), which is a typical sports injury. This paper describes the development and evaluation of a smart knee sleeve, which is capable of calculating the knee flexion-extension angle based on inertial sensors. The output is to be used to supervise and speed up rehabilitation by guiding patients through exercises and giving feedback about the execution. The accuracy of the knee angle calculation was evaluated in a study with twelve healthy subjects performing six different exercises with an optical system as reference. The effects of different functional alignment movements, knee sleeve material and recalibration on the final knee angle were evaluated. The mean absolute error (MAE) of all maxima and minima was calculated and averaged over all subjects and exercises. An MAE of 5.2° compared to the gold standard was achieved while the Pearson correlation was 0.96 for 30 minutes training without recalibration or restart of the system.

The use of weather sensing radar measurements along with corresponding gauge data in multisensor applications seek to provide reliable estimates of rainfall rate and accumulation versus single radar. Radar rainfall estimators have a number of advantages over gauges including the ability to observe precipitation over wider areas within shorter timeframes and providing advanced warning of impending precipitation events. The radar reflectivity-rainfall (Z-R) relations are traditionally used for quantitative precipitation estimation (QPE).

With the escalation of attacks that target mobile devices there is an increasing need for efficient monitoring tools. Due to computation, storage, and power constraints of the devices, these monitoring tools may overload the entire system causing severe performance degradation for the running applications. Moreover, they can be pushed by resource-intensive applications and may not perform an adequate monitoring job. To that end, in this project we develop adaptive monitoring tools that are capable of monitoring a wide range of information while efficiently utilizing the available resources on the mobile device. We apply control theoretic techniques to design monitoring functionalities, such as process, integrity and network monitors, that utilize the underlying resources efficiently. This is achieved through controllers that dynamically select the duty cycles of the monitors. Our models and results are validated on the Android platform through emulation and real implementation on the Nexus 7 tablet.

Efficient hardware implementations of statistical inference continue to grow in importance for a wide range of computing applications. While CPU cycles are increasingly being used for statistical inference, transistors are also becoming increasingly statistical. For implementing statistical algorithms, could it be that statistical electronic substrates are a feature rather than a bug?