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Industrial control systems are distributed hierarchical networks that share information via an assortment of communication protocols. Such systems are vulnerable to attacks that can cause disastrous consequences. This article focuses on time delay switch (TDS) attacks and shows that cryptographic solutions are ineffective in recovering from the denial of service component of TDS attacks. Therefore, a cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is developed that leverages adaptive channel redundancy techniques and a novel state estimator, to detect and recover from the destabilizing effects of TDS attacks. Simulation results are conducted to prove that CF-TDSR ensures the control stability of linear time invariant systems and to show the efficacy of CF-TDSR against attacks deployed on multi-area load frequency control components of a distributed power grid.

The paper deals with defaultable markets, one of the main research areas of mathematical finance. It proposes a new approach to the theory of such markets using techniques from the calculus of optional stochastic processes on unusual probability spaces, which was not presented before. The paper is a foundation paper and contains a number of fundamental results on modeling of defaultable markets, pricing and hedging of defaultable claims and results on the probability of default under such conditions. Moreover, several important examples are presented: a new pricing formula for a defaultable bond and a new pricing formula for credit default swap. Furthermore, some results on the absence of arbitrage for markets on unusual probability spaces and markets with default are also provided.

Decoding motor intent from recorded neural signals is essential for the development of effective neural-controlled prostheses. To facilitate the development of online decoding algorithms we have developed a software platform to simulate neural motor signals recorded with peripheral nerve electrodes, such as longitudinal intrafascicular electrodes (LIFEs). The simulator uses stored motor intent signals to drive a pool of simulated motoneurons with various spike shapes, recruitment characteristics, and firing frequencies. Each electrode records a weighted sum of a subset of simulated motoneuron activity patterns. As designed, the simulator facilitates development of a suite of test scenarios that would not be possible with actual data sets because, unlike with actual recordings, in the simulator the individual contributions to the simulated composite recordings are known and can be methodically varied across a set of simulation runs. In this manner, the simulation tool is suitable for iterative development of real-time decoding algorithms prior to definitive evaluation in amputee subjects with implanted electrodes. The simulation tool was used to produce data sets that demonstrate its ability to capture some features of neural recordings that pose challenges for decoding algorithms.