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This paper provides a resource management (RM) framework for electronic support (ES) receivers. The resource manager estimates the number of interference signals and the bandwidths of each interference signal in the electromagnetic (EM) environment. Environment estimates are used to select an appropriate adaptive digital beamforming (DBF) algorithm from a predefined look-up table (LUT) of adaptive DBF algorithms. Algorithms are selected from the LUT based on their ability to increase the signal-to-interference plus noise level of the desired signal by a desired amount; the algorithms are also selected based upon their computational complexity. A study of the resource manager computational complexity demonstrates that applying the new architecture does not increase the receiver computational complexity above standard ES receivers. The resulting adaptive receiver is capable of operating with reduced computational loading over standard support receivers. The framework allows for the use of a single receiver for both narrowband and wideband operation without imposing unrequited computational complexity in the narrowband environments.

Evolutionary algorithms can be applied to a variety of constrained network communication problems with centric type models. This paper shows that with real-world complex network communication problems of this type, sophisticated statistical search is required. This situation occurs due to the fact that these optimization problems are at least NP-complete. In order to appreciate the formal modeling of realistic communication networks, historical network design problems are presented and evolved into more complex real-world models with associated deterministic and stochastic solution approaches discussed. This discussion leads into the design of an innovative multiobjective evolutionary algorithm (MOEA) to solve a very complex network design problem variation called the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid real-world communication architecture as reflected in real-world network centric models with directional communications, multiple objectives including costs, delays, robustness, vulnerability, and operating reliability within network constraints. Nodes in such centric systems can have multiple and varying link capacities as well as rates and information (commodity) quantities to be sent and received. Each commodity can also have independent prioritized bandwidth and spectrum requirements. The nondominated sorting genetic algorithm (NSGA-II) is selected as the MOEA framework which is modified and parallelized to solve the generic MCNDP. Since the MCNDP is highly constrained but with an enormous number of possible network communication topologies, a novel initialization procedure and mutation method are integrated resulting in reduced search space. Empirical results and analysis indicate that effective topological Pareto solutions are generated for use in highly constrained, communication-based network design.

Developing multiobjective evolutionary algorithms (MOEAs) involves thoroughly addressing the issues of efficiency and effectiveness. Once convinced of an MOEA's effectiveness the researcher often desires to reduce execution time and/or resource expenditure, which naturally leads to considering the MOEA's parallelization. Parallel MOEAs (pMOEAs) or distributed MOEAs are relatively new developments with few associated publications. pMOEA creation is not a simple task, involving analyzing various parallel paradigms and associated parameters. Thus, a thorough discussion of the major parallelized MOEA paradigms is included in this paper and succinct observations are made regarding an analysis of the current literature. Specifically, a previous MOEA notation is extended into the pMOEA domain to enable precise description and identification of various sets of interest. Innovative concepts for pMOEA migration, replacement and niching schemes are discussed, as well as presenting the first known generic pMOEA formulation. Taken together, this paper's analyses in conjunction with an original pMOEA design serve as a pedagogical framework and example of the necessary process to implement an efficient and effective pMOEA.

With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID.

Recent trends have led to the expanded use of digital computers in large complex system applications. The major burden of meeting the requirements for these systems has fallen on the flexibility of software. This has placed many demands on all areas associated with digital systems development. A significant impact has been on the development of software personnel required to develop and manage these complex systems. The purpose of this paper is to present a three level program for software education of executives, managers, and engineers which is being offered at the Air Force Institute of Technology (AFIT). The needs and approaches in satisfying software engineering education requirements are presented. Educational objectives for executives, managers, and engineers serve as the foundation of the curricula, courses, and topics presented.

It is well known that there are several ways to define a sensitivity function in the time domain. The results are reported of a study in which these definitions are compared. Controllers designed on the basis of these sensitivity functions are related to each other and conclusions are drawn about their usefulness in control system design.