DRUM Community: Institute for Systems Researchhttp://hdl.handle.net/1903/43752014-04-23T09:12:06Z2014-04-23T09:12:06ZObtaining Statistically Random Information from Silicon Physical Unclonable FunctionsYin, Chi-EnQu, Ganghttp://hdl.handle.net/1903/150002014-03-15T02:30:23Z2013-01-01T00:00:00ZTitle: Obtaining Statistically Random Information from Silicon Physical Unclonable Functions
Authors: Yin, Chi-En; Qu, Gang
Abstract: Silicon physical unclonable functions (PUF) uti- lize the variation during silicon fabrication process to extract information that will be unique for each chip. There have been many recent approaches to how PUF can be used to improve security related applications. However, it is well-known that the fabrication variation has very strong spatial correlation1 and this has been pointed out as a security threat to silicon PUF. In fact, when we apply NIST’s statistical test suite for randomness [1] against the random sequences generated from a population of 125 ring oscillator (RO) PUFs [2] using classic 1-out-of-8 Coding [3], [4] and Neighbor Coding [5], none of them can pass all the tests. In this paper, we propose to decouple the unwanted systematic variation from the desired random variation through a regression-based distiller, where the basic idea is to build a model for the systematic variation so we can generate the random sequences only from the true random variation. Applying Neighbor Coding to the same benchmark data [2], our experiment shows that 2nd and 3rd order polynomials distill random sequences that pass all the NIST randomness tests. So does 4th order polynomial in the case of 1-out-of-8 Coding. Finally, we introduce two generic random sequence generation methods. The sequences they generate fail all the randomness tests, but with the help of our proposed polynomial distiller, all but one tests are passed. These results demonstrate that our method can provide statistically random PUF information and thus bolster the security characteristics of existing PUF schemes.2013-01-01T00:00:00ZPredicting the Performance of Teams of Bounded Rational Decision-makers Using a Markov Chain ModelHerrmann, Jeffreyhttp://hdl.handle.net/1903/144202013-09-04T02:31:45Z2013-08-01T00:00:00ZTitle: Predicting the Performance of Teams of Bounded Rational Decision-makers Using a Markov Chain Model
Authors: Herrmann, Jeffrey
Abstract: In practice, when faced with a complex optimization problem, teams of human decision-makers often separate it into subproblems and then solve each subproblem instead of tackling the complete problem. It would be useful to know the conditions in which separating the problem is the superior approach and how the subproblems should be assigned to members of the teams. This paper describes a mathematical model of a search that represents a bounded rational decision-maker (“agent”) solving a generic optimization problem. The agent’s search can be modeled as a discrete-time Markov chain, which allows one to calculate the probability distribution of the value of the solution that the agent will find. We compared the distributions generated by the model to the distribution of results from searches of solutions to traveling salesman problems. Using this model, we evaluated the performance of two- and three-agent teams who used different solution approaches to solve generic optimization problems. In the “all-at-once” approach, the agents collaborate to search the entire set of solutions in a sequential manner: the next agent begins where the previous agent stopped. In the “separation” approach, the agents separate the problem into two subproblems: (1) find the best set of solutions, and (2) find the best solution in that set. The results show that teams found better solutions using separation when high-value solutions are less likely. Using the all-at-once approach yielded better results when the values were uniformly distributed. The optimal assignment of subproblems to teams also depended upon the distribution of values in the solution space.2013-08-01T00:00:00ZSeparating the Searches of Bounded Rational Decision-MakersHerrmann, Jeffreyhttp://hdl.handle.net/1903/139672013-06-28T02:31:09Z2013-06-01T00:00:00ZTitle: Separating the Searches of Bounded Rational Decision-Makers
Authors: Herrmann, Jeffrey
Abstract: In practice, when faced with a complex optimization problem, human decision-makers often separate it into subproblems and then solve each subproblem instead of tackling the complete problem. This paper describes a study that simulated small teams of bounded rational decision-makers (“agents”) who try different approaches to solve optimization problems. In the “all-at-once” approaches, the agents collaborate to search the entire set of solutions in a sequential manner: each agent begins where the previous agent stopped. In other approaches, the agents separate the problem into subproblems, and each agent solves a different subproblem. Finally, in the hybrid approaches, the agents separate the problem but two agents will collaborate to solve one subproblem while another agent solves a different subproblem. In some cases, the subproblems are solved in parallel; in others, the subproblems are solved sequentially. The results show that the teams generally found better solutions when they separated the problem.2013-06-01T00:00:00ZREU: Improving straight line travel in a miniature wheeled robotGessler, KatieSabelhaus, Andrewhttp://hdl.handle.net/1903/139452013-06-14T02:30:27Z2012-08-10T00:00:00ZTitle: REU: Improving straight line travel in a miniature wheeled robot
Authors: Gessler, Katie; Sabelhaus, Andrew
Abstract: The TinyTeRP is a miniature robotics platform
with modular sensing capabilities. Prior generations of the
TinyTeRP have experienced various problems in assembly
process, materials selection, and their fundamental design.
These problems are addressed by choosing 3D printing as the
new manufacturing method and steel wire for the new axle. The
TinyTeRP’s ability to travel in a straight line using open loop
control is studied. After 1.37 m of travel in the x direction, the
TinyTeRP was as close as 4.69 cm to or as far as 31.9 cm from
the ideal ending position (a straight line), indicating that open
loop control is a poor method for controlling a straight line
trajectory. Comparing data on the angle of the trajectory
collected from position data from the vision table to data
collected from the gyroscope indicated that the gyroscope tracks
the robot’s angle of motion well. Hence, using the gyroscope for
closed loop control of the TinyTeRP’s motion is possible.2012-08-10T00:00:00Z