No abstract

Due to the increasing complexity in systems development as well the highly competitive environment, it has become apparent that new ways of reducing system development time, minimize cost, enhance organizational efficiency, increase customer satisfaction, and improve the quality of the final systems are required. The Transdisciplinary Quality System development lifecycle (TQSDL) Model is one of the new systematic approaches models used as a design management tool for developing the complex systems based on scientific principles. This model is applied across the whole systems development lifecycle. The model processes are based on the Axiomatic Design (AD) and Quality Function Deployment (QFD) tool. One of the factors that influence the quality and the cost of the final systems is the supply chain. Also, one of the largest difficulties during the development of complex systems is tracking and expecting the changes in development cost due to changes in customer needs or function requirements. In this paper, the TQSDL model is extended to cover the supply chain process by adding a new domain to TQSDL domains. This new domain is introduced to manage the relationships between system components and all suppliers by using the QFD tool. As well as, Dependency Structure Matrix (DSM) will be integrated into the TQSDL process to improve information management and to address the interdependency between the system components and the interrelation between activities during systems development. Moreover, DSM with a new characteristic vector will aid to capture the changes in the development cost due to the changes in customer needs or function requirements. The enhancement in TQSDL model aims to provide a complete framework and systematic thinking to the designers and technical managers during the whole system development lifecycle. Moreover, it will also support the decision makers on whether or not to implement changes to a design.

Due to the increasing complexity in modern radar systems, it has become apparent that new ways of classify received signals are required. In this paper, the performance of a proposed method for radar system identification using hidden Markov model (HMM) is evaluated. In this method, a given radar system is modeled as a finite state automaton. In so, doing, it is possible to uncover the underlying system processes in a probabilistic fashion using hidden Markov models. Artificial deterministic signals are used to show that HMMs can provide adequate signal recognition. It will be shown that a perfect exists that is directly related to the number of symbols per period. A slightly adjusted version of this perfect model provides low false recognition rates in a threat library consisting of three models so long as the observation errors do not become too frequent. The simulation results show that if the observation errors are introduced, then, it becomes necessary to retrain the HMM with an error corrupted version of the original training sequence in order to improve the model's robustness.