Optimization-Based Methodology for the Exploration of Cyber-Physical System Architectures

In this thesis, we address the design space exploration of cyber-physical system architectures to select correct-by-construction configuration and interconnection of system components taken from pre-defined libraries. We formulate the exploration problem as a mapping problem and use optimization to solve it by searching for a minimum cost architecture that meets system requirements. Using a graph-based representation of a system architecture, we define a set of generic mixed integer linear constraints over graph vertices, edges and paths, and use these constraints to instantiate a variety of design requirements (e.g., interconnection, flow, workload, timing, reliability, routing). We implement a comprehensive toolbox that supports all steps of the proposed methodology. It provides a pattern-based formal language to facilitate requirements specification and a set of scalable algorithms for encoding and solving exploration problems. We prove our concepts on a set of case studies for different cyber-physical system domains, such as electrical power distribution networks, reconfigurable industrial production lines and wireless sensor networks

Intelligent performance inference: A graph neural network approach to modeling maximum achievable throughput in optical networks

One of the key performance metrics for optical networks is the maximum achievable throughput for a given network. Determining it, however, is a nondeterministic polynomial time (NP) hard optimization problem, often solved via computationally expensive integer linear programming (ILP) formulations. These are infeasible to implement as objectives, even on very small node scales of a few tens of nodes. Alternatively, heuristics are used although these, too, require considerable computation time for a large number of networks. There is, thus, a need for an ultra-fast and accurate performance evaluation of optical networks. For the first time, we propose the use of a geometric deep learning model, message passing neural networks (MPNNs), to learn the relationship between node and edge features, the network structure, and the maximum achievable network throughput. We demonstrate that MPNNs can accurately predict the maximum achievable throughput while reducing the computational time by up to five-orders of magnitude compared to the ILP for small networks (10–15 nodes) and compared to a heuristic for large networks (25–100 nodes)—proving their suitability for the design and optimization of optical networks on different time- and distance-scales

System data communication structures for active-control transport aircraft, volume 2

The application of communication structures to advanced transport aircraft are addressed. First, a set of avionic functional requirements is established, and a baseline set of avionics equipment is defined that will meet the requirements. Three alternative configurations for this equipment are then identified that represent the evolution toward more dispersed systems. Candidate communication structures are proposed for each system configuration, and these are compared using trade off analyses; these analyses emphasize reliability but also address complexity. Multiplex buses are recognized as the likely near term choice with mesh networks being desirable for advanced, highly dispersed systems

Dynamic Dependability Analysis using HOL Theorem Proving with Application in Multiprocessor Systems

Dynamic dependability analysis has become an essential step in the design process of safety-critical systems to ensure the delivery of a trusted service without failures. Dependability usually encompasses several attributes, such as reliability and availability. A dynamic dependability model is created using one of the dependability modeling techniques, such as Dynamic Fault Trees (DFTs) and Dynamic Reliability Block Diagrams (DRBDs). Several analysis methods, including paper-and-pencil or simulation, exist for analyzing these models to ascertain various dependability related parameters. However, their results cannot be always trusted since they may involve some approximations, truncations or even errors. Formal methods, such as model checking and theorem proving, can be used to overcome these inaccuracy limitations due to their inherent soundness and completeness. However, model checking suffers from state-space explosion if the state space is large. While, theorem proving was used only for the static dependability analysis without considering the system dynamics. In order to conduct the formal dependability analysis of systems that exhibit dynamic failure behaviors within a theorem prover, these models need to be captured formally, where their structures, operators and properties are properly formalized. In this thesis, we provide a complete framework for the formal dependability analysis of systems modeled as DFTs and DRBDs in the HOL4 higher-order logic theorem prover. We provide the formalization of DFT gates and verify important simplification theorems based on well-known DFT algebra. In addition, our framework allows both qualitative and quantitative DFT analyses to be conducted using theorem proving. We use this formalization to formally verify the DFT rewrite rules, that are used by automated DFT analysis tools, to ascertain their correctness. Due to the lack of a DRBD algebra that allows the analysis using a theorem prover, in this thesis, we develop and formalize a novel algebra that includes operators and simplification theorems to formalize traditional RBD structures, such as the series and parallel, besides the DRBD spare construct. We formally verify their reliability expressions, which allows conducting both the qualitative and quantitative analyses of a given system. Leveraging upon the complementary nature of DFTs and DRBDs, our proposed framework provides the possibility of formally converting one model to the other, which allows reasoning about both the success and failure of a given system. Our framework provides generic expressions of probability of failure and reliability that are independent of the failure distribution of an arbitrary number of system components, which cannot be obtained using other formal tools, such as model checking. In order to demonstrate the usefulness of the proposed framework, we formally model and analyze the dependability of the terminal, broadcast and network reliability of shuffle-exchange networks, which are multistage interconnections networks that are used to connect the elements of multiprocessor systems. Conducting a sound analysis with generic expressions is essential in these systems, where it is required to accurately capture and analyze the failure behavior

CES-513 Stages for Developing Control Systems using EMG and EEG Signals: A survey

Bio-signals such as EMG (Electromyography), EEG (Electroencephalography), EOG (Electrooculogram), ECG (Electrocardiogram) have been deployed recently to develop control systems for improving the quality of life of disabled and elderly people. This technical report aims to review the current deployment of these state of the art control systems and explain some challenge issues. In particular, the stages for developing EMG and EEG based control systems are categorized, namely data acquisition, data segmentation, feature extraction, classification, and controller. Some related Bio-control applications are outlined. Finally a brief conclusion is summarized.