Incorporating Uncertain Costs within a Series of Sequential Probability Ratio Tests

We consider an extension to Sequential Probability Ratio Tests for when we have uncertain costs, but also opportunity to learn about these in an adaptive manner. In doing so we demonstrate the effects that allowing uncertainty has on observation cost, and the costs associated with Type I and Type II error. The value of information relating to modelled uncertainties is derived and the case of statistical dependence between the parameter affecting decision outcome and the parameter affecting unknown cost is also examined. Numerical examples of the derived theory are provided, along with a simulation comparing this adaptive learning framework to the classical one.

Multiple hypothesis tracking based on the Shiryayev sequential probability ratio test

To date, Wald sequential probability ratio test(WSPRT) has been widely applied to track management of multiple hypothesis tracking(MHT). But in a real situation, if the false alarm spatial density is much larger than the new target spatial density, the original track score will be very close to the deletion threshold of the WSPRT. Consequently, all tracks, including target tracks, may easily be deleted, which means that the tracking performance is sensitive to the tracking environment. Meanwhile, if a target exists for a long time, its track will have a high score, which will make the track survive for a long time even after the target has disappeared. In this paper, to consider the relationship between the hypotheses of the test, we adopt the Shiryayev SPRT(SSPRT) for track management in MHT. By introducing a hypothesis transition probability, the original track score can increase faster, which solves the first problem. In addition, by setting an independent SSPRT for track deletion, the track score can decrease faster, which solves the second problem. The simulation results show that the proposed SSPRT-based MHT can achieve better tracking performance than MHT based on the WSPRT under a high false alarm spatial density.

Application of Analytic Redundancy-based Fault Diagnosis of Sensors to Onboard Maintenance System

Analytic redundancy-based fault diagnosis technique （ARFDT） is applied to onboard maintenance system （OMS）. The principle of the proposed ARFDT scheme is to design a redundancy configuration using ARFDT to enhance the functions of redundancy management and built in test equipment （BITE） monitor. Redundancy configuration for dual-redundancy and analytic redundancy is proposed, in which, the fault diagnosis includes detection and isolation. In order to keep the balance between rapid diagnosis and binary hypothesis, a filter together with an elapsed time limit is designed for sequential probability ratio test （SPRT） in the process of isolation. Diagnosis results would be submitted to central maintenance computer （CMC） together with BITE information. Moreover, by adopting reconstruction, the designed method not only provides analytic redundancy to help redundancy management, but also compensates the output when both of the sensors of the same type are faulty. Our scheme is applied to an aircraft’s sensors in a simulation experiment, and the results show that the proposed filter SPRT （FSPRT） saves at least 50% of isolation time than Wald SPRT （WSPRT）. Also, effectiveness, practicability and rapidity of the proposed scheme can be successfully achieved in OMS.

Utilizing Probabilistic Linear Equations in Cube Attacks

Cube attacks, proposed by Dinur and Shamir at EUROCRYPT 2009, have shown huge power against stream ciphers. In the original cube attacks, a linear system of secret key bits is exploited for key recovery attacks. However, we find a number of equations claimed linear in previous literature actually nonlinear and not fit into the theoretical framework of cube attacks. Moreover, cube attacks are hard to apply if linear equations are rare. Therefore, it is of significance to make use of probabilistic linear equations, namely nonlinear superpolys that can be approximated by linear expressions effectively. In this paper, we suggest a way to test out and utilize these probabilistic linear equations, thus extending cube attacks to a wider scope. Concretely, we employ the standard parameter estimation approach and the sequential probability ratio test （SPRT） for linearity test in the preprocessing phase, and use maximum likelihood decoding （MLD） for solving the probabilistic linear equations in the online phase. As an application, we exhibit our new attack against 672 rounds of Trivium and reduce the number of key bits to search by 7.