Virtual reality training simulations to acquire surgical skills are important for increasing patient safety and save valuable resources, e.g., cadavers, supervision and operating room time. However, as surgery is a craft, simulators must not only provide a high degree of visual realism, but especially a realistic haptic behavior. While such simulators exist for surgeries like laparoscopy or arthroscopy, other surgical fields, especially where large forces need to be exerted, like total hip arthroplasty (THA; implantation of a hip joint protheses), lack realistic VR training simulations. In this paper we present for the first time a novel VR training simulation for the five steps of THA (from femur head resection to stem implantation) with realis-tic haptic feedback. To achieve this, a novel haptic hammering device, an upgraded version of the Virtuose 6D haptic device from Haption, novel algorithms for collision detection, haptic rendering, and material removal are introduced. In a study with 17 surgeons of diverse experience levels, we confirmed the realism, usefulness and usability of our novel methods.

Dataflow networks play a vital role in modeling and analyzing stream-processing systems in an analytic way, including digital signal and image processing systems. In this paper, we first present a system-level approach to synthesize such dataflow networks automatically to systems of communicating hardware actors connected by FIFO buffers. Although such data-triggered networks of (internally clocked) actors can achieve very high throughputs, the potential to power actors down in times of unavailability of data has not been addressed so far in any research. Here, we show that by refinement of the firing state machine of each actor in a given network, we enable the design of self-powering dataflow networks while exploiting either clock gating or power gating as a means to save power in times of inactivity of each individual actor in a network. The gains of self-powering dataflow networks in terms of power and energy savings when powering down and up actors dynamically is shown for different data arrival patterns and rates in detailed experiments for multiple IoT system applications. These systems are often working in normally-off mode and woken up only upon the availability of data. For these, drastic energy savings are reported.