13 research outputs found
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Rapid preconditioning of data for accelerating convex hull algorithms
Given a dataset of two-dimensional points in the plane with integer
coordinates, the method proposed reduces a set of n points down to
a set of s points s ≤ n, such that the convex hull on the set of s
points is the same as the convex hull of the original set of n points.
The method is O(n). It helps any convex hull algorithm run faster.
The empirical analysis of a practical case shows a percentage reduction
in points of over 98%, that is reflected as a faster computation with a
speedup factor of at least 4
Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network
With the globalization of the semiconductor manufacturing process, electronic
devices are powerless against malicious modification of hardware in the supply
chain. The ever-increasing threat of hardware Trojan attacks against integrated
circuits has spurred a need for accurate and efficient detection methods. Ring
oscillator network (RON) is used to detect the Trojan by capturing the
difference in power consumption; the power consumption of a Trojan-free circuit
is different from the Trojan-inserted circuit. However, the process variation
and measurement noise are the major obstacles to detect hardware Trojan with
high accuracy. In this paper, we quantitatively compare four supervised machine
learning algorithms and classifier optimization strategies for maximizing
accuracy and minimizing the false positive rate (FPR). These supervised
learning techniques show an improved false positive rate compared to principal
component analysis (PCA) and convex hull classification by nearly 40% while
maintaining > 90\% binary classification accuracy