19 research outputs found
Computing a Minimum-Cost -hop Steiner Tree in Tree-Like Metrics
We consider the problem of computing a Steiner tree of minimum cost under a
-hop constraint which requires the depth of the tree to be at most . Our
main result is an exact algorithm for metrics induced by graphs of bounded
treewidth that runs in time . For the special case of a path, we give
a simple algorithm that solves the problem in polynomial time, even if is
part of the input. The main result can be used to obtain, in quasi-polynomial
time, a near-optimal solution that violates the -hop constraint by at most
one hop for more general metrics induced by graphs of bounded highway
dimension
Speed-Robust Scheduling
The speed-robust scheduling problem is a two-stage problem where given
machines, jobs must be grouped into at most bags while the processing
speeds of the given machines are unknown. After the speeds are revealed,
the grouped jobs must be assigned to the machines without being separated. To
evaluate the performance of algorithms, we determine upper bounds on the
worst-case ratio of the algorithm's makespan and the optimal makespan given
full information. We refer to this ratio as the robustness factor. We give an
algorithm with a robustness factor for the most general setting and
improve this to for equal-size jobs. For the special case of
infinitesimal jobs, we give an algorithm with an optimal robustness factor
equal to . The particular machine environment in which
all machines have either speed or was studied before by Stein and Zhong
(SODA 2019). For this setting, we provide an algorithm for scheduling
infinitesimal jobs with an optimal robustness factor of . It lays the foundation for an algorithm matching the lower bound of
for equal-size jobs
Skilog: A Smart Sensor System for Performance Analysis and Biofeedback in Ski Jumping
In ski jumping, low repetition rates of jumps limit the effectiveness of
training. Thus, increasing learning rate within every single jump is key to
success. A critical element of athlete training is motor learning, which has
been shown to be accelerated by feedback methods. In particular, a fine-grained
control of the center of gravity in the in-run is essential. This is because
the actual takeoff occurs within a blink of an eye (300ms), thus any
unbalanced body posture during the in-run will affect flight. This paper
presents a smart, compact, and energy-efficient wireless sensor system for
real-time performance analysis and biofeedback during ski jumping. The system
operates by gauging foot pressures at three distinct points on the insoles of
the ski boot at 100Hz. Foot pressure data can either be directly sent to
coaches to improve their feedback, or fed into a ML model to give athletes
instantaneous in-action feedback using a vibration motor in the ski boot. In
the biofeedback scenario, foot pressures act as input variables for an
optimized XGBoost model. We achieve a high predictive accuracy of 92.7% for
center of mass predictions (dorsal shift, neutral stand, ventral shift).
Subsequently, we parallelized and fine-tuned our XGBoost model for a RISC-V
based low power parallel processor (GAP9), based on the PULP architecture. We
demonstrate real-time detection and feedback (0.0109ms/inference) using our
on-chip deployment. The proposed smart system is unobtrusive with a slim form
factor (13mm baseboard, 3.2mm antenna) and a lightweight build (26g). Power
consumption analysis reveals that the system's energy-efficient design enables
sustained operation over multiple days (up to 300 hours) without requiring
recharge.Comment: 5 pages, 2 tables, 4 figure, Accepted at IEEE BioCAS 202