Intelligent Hardware-Enabled Sensor and Software Safety and Health Management for Autonomous UAS

Unmanned Aerial Systems (UAS) can only be deployed if they can effectively complete their mission and respond to failures and uncertain environmental conditions while maintaining safety with respect to other aircraft as well as humans and property on the ground. We propose to design a real-time, onboard system health management (SHM) capability to continuously monitor essential system components such as sensors, software, and hardware systems for detection and diagnosis of failures and violations of safety or performance rules during the ight of a UAS. Our approach to SHM is three-pronged, providing: (1) real-time monitoring of sensor and software signals; (2) signal analysis, preprocessing, and advanced on-the- y temporal and Bayesian probabilistic fault diagnosis; (3) an unobtrusive, lightweight, read-only, low-power hardware realization using Field Programmable Gate Arrays (FPGAs) in order to avoid overburdening limited computing resources or costly re-certi cation of ight software due to instrumentation. No currently available SHM capabilities (or combinations of currently existing SHM capabilities) come anywhere close to satisfying these three criteria yet NASA will require such intelligent, hardwareenabled sensor and software safety and health management for introducing autonomous UAS into the National Airspace System (NAS). We propose a novel approach of creating modular building blocks for combining responsive runtime monitoring of temporal logic system safety requirements with model-based diagnosis and Bayesian network-based probabilistic analysis. Our proposed research program includes both developing this novel approach and demonstrating its capabilities using the NASA Swift UAS as a demonstration platform

The treatment of time in distributed simulation

Simulation is one of the most important tools to analyse, design, and operate complex processes and systems. Simulation allows us to make a 'trial and error' in order to understand a system and describe a problem. Therefore, it is of great interest to use simulation easily and practically. The advent of parallel processors and languages help simulation studies. A recent simulation trend is distributed simulation which may be called discrete- event simulation, because distributed simulation has a great potential for the speed-up. This thesis will survey discrete-event simulation and examine one particular algorithm. It will first survey simulation in general and secondly, distributed simulation. Distributed simulation has broadly two mechanisms: conservative and optimistic. The treatment of time in these mechanisms is different, we will look into both mechanisms. Finally, we will examine the conservative mechanism on a network of transputers using Occam. We will conclude with the result of the experiments and the perspective of distributed simulation

Modularity vs. Reusability: Code Generation from Synchronous Block Diagrams

We present several methods to generate modular code from synchronous hierarchical block diagrams. Modularity means code is generated for a given macro (i.e., composite) block independently from context, that is, without knowing where this block is to be used, and also with minimal knowl-edge about its sub-blocks. We achieve this by generating a set of interface functions for each block and a set of depen-dencies between these functions that is exported along with the interface. The main trade-off is the degree of modular-ity (number of interface functions) vs. reusability (the set of diagrams that the block can be used in without creating dependency cycles).

IST Austria Thesis

The scalability of concurrent data structures and distributed algorithms strongly depends on reducing the contention for shared resources and the costs of synchronization and communication. We show how such cost reductions can be attained by relaxing the strict consistency conditions required by sequential implementations. In the first part of the thesis, we consider relaxation in the context of concurrent data structures. Specifically, in data structures such as priority queues, imposing strong semantics renders scalability impossible, since a correct implementation of the remove operation should return only the element with highest priority. Intuitively, attempting to invoke remove operations concurrently creates a race condition. This bottleneck can be circumvented by relaxing semantics of the affected data structure, thus allowing removal of the elements which are no longer required to have the highest priority. We prove that the randomized implementations of relaxed data structures provide provable guarantees on the priority of the removed elements even under concurrency. Additionally, we show that in some cases the relaxed data structures can be used to scale the classical algorithms which are usually implemented with the exact ones. In the second part, we study parallel variants of the stochastic gradient descent (SGD) algorithm, which distribute computation among the multiple processors, thus reducing the running time. Unfortunately, in order for standard parallel SGD to succeed, each processor has to maintain a local copy of the necessary model parameter, which is identical to the local copies of other processors; the overheads from this perfect consistency in terms of communication and synchronization can negate the speedup gained by distributing the computation. We show that the consistency conditions required by SGD can be relaxed, allowing the algorithm to be more flexible in terms of tolerating quantized communication, asynchrony, or even crash faults, while its convergence remains asymptotically the same