Approved for public release, distribution is unlimited / A hardware interface is designed, developed, constructed, and tested to interface a naval radar to the SRC 6E reconfigurable computer. The U.S. Navy AN/SPS 65 radar provides in-phase (I) and quadrature (Q) channels along with the AGC voltage to the hardware interface in analog form. The hardware interface receives a sampling clock from the SRC 6E and in turn performs the requisite attenuation and digital conversion before presenting the signals to the SRC 6E through its CHAIN port. The results show that the SRC 6E can effectively generate a sampling clock to drive the analog-to-digital converters and that real- time radar data can be brought into the SRC 6E via its high speed CHAIN port for performing high speed digital signal processing. / Lieutenant, United States Naval Reserve

Approved for public release, distribution is unlimited / The delivery of supply in combat operations is very important and often results in success or failure of a mission. This activity, as well as other transportation problems, has traditionally been modeled using global optimization techniques, such as linear programming. However, the goal of this thesis is to examine the feasibility of an agent-based solution to study the movement of material through a road network. The requirement is to build an agent-based system that finds the optimal route through a given road network and is capable of adapting to disruptions introduced to the network and then find alternative routes through the network. The agents act from a local perspective, and can represent more realistically the decisions being made throughout the delivery process. This thesis implements an analysis environment for road networks and develops an agent-based model to build truck-driver agents that are capable of delivering supplies through a changing road network. / Captain, German Army

The performance of a program can sometimes greatly improve if it was known in advance the features of the input the program is supposed to process, the actual operating parameters it is supposed to work with, or the specific environment it is to run on. However, this information is typically not available until too late in the program’s operation to take advantage of it. This is especially true for simulation algorithms, which are sensitive to this late-arriving information, and whose role in the solution of decision-making, inference and valuation problems is crucial. To overcome this limitation we need to provide the flexibility for a program to adapt its behaviour to late-arriving information once it becomes available. In this thesis, I study three adaptation mechanisms: run-time code generation, model-specific (quasi) Monte Carlo sampling and dynamic computation offloading, and evaluate their benefits on Monte Carlo algorithms. First, run-time code generation is studied in the context of Monte Carlo algorithms for time-series filtering in the form of the Input-Adaptive Kalman filter, a dynamically generated state estimator for non-linear, non-Gaussian dynamic systems. The second adaptation mechanism consists of the application of the functional-ANOVA decomposition to generate model-specific QMC-samplers which can then be used to improve Monte Carlo-based integration. The third adaptive mechanism treated here, dynamic computation offloading, is applied to wireless communication management, where network conditions are assessed via option valuation techniques to determine whether a program should offload computations or carry them out locally in order to achieve higher run-time (and correspondingly battery-usage) efficiency. This ability makes the program well suited for operation in mobile environments. At their core, all these applications carry out or make use of (quasi) Monte Carlo simulations on dynamic Bayesian networks (DBNs). The DBN formalism and its associated simulation-based algorithms are of great value in the solution to problems with a large uncertainty component. This characteristic makes adaptation techniques like those studied here likely to gain relevance in a world where computers are endowed with perception capabilities and are expected to deal with an ever-increasing stream of sensor and time-series data.

The performance of a program can sometimes greatly improve if it was known in advance the features of the input the program is supposed to process, the actual operating parameters it is supposed to work with, or the specific environment it is to run on. However, this information is typically not available until too late in the program’s operation to take advantage of it. This is especially true for simulation algorithms, which are sensitive to this late-arriving information, and whose role in the solution of decision-making, inference and valuation problems is crucial. To overcome this limitation we need to provide the flexibility for a program to adapt its behaviour to late-arriving information once it becomes available. In this thesis, I study three adaptation mechanisms: run-time code generation, model-specific (quasi) Monte Carlo sampling and dynamic computation offloading, and evaluate their benefits on Monte Carlo algorithms. First, run-time code generation is studied in the context of Monte Carlo algorithms for time-series filtering in the form of the Input-Adaptive Kalman filter, a dynamically generated state estimator for non-linear, non-Gaussian dynamic systems. The second adaptation mechanism consists of the application of the functional-ANOVA decomposition to generate model-specific QMC-samplers which can then be used to improve Monte Carlo-based integration. The third adaptive mechanism treated here, dynamic computation offloading, is applied to wireless communication management, where network conditions are assessed via option valuation techniques to determine whether a program should offload computations or carry them out locally in order to achieve higher run-time (and correspondingly battery-usage) efficiency. This ability makes the program well suited for operation in mobile environments. At their core, all these applications carry out or make use of (quasi) Monte Carlo simulations on dynamic Bayesian networks (DBNs). The DBN formalism and its associated simulation-based algorithms are of great value in the solution to problems with a large uncertainty component. This characteristic makes adaptation techniques like those studied here likely to gain relevance in a world where computers are endowed with perception capabilities and are expected to deal with an ever-increasing stream of sensor and time-series data.

Thesis (M.S.)--University of Texas at El Paso, 2008. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

M.Sc. (Information Technology) / Making changes to a complex system, such as a cellular network, without being well informed about the potential effects of the change could prove to have negative and costly outcomes. In order to diminish the risk of making changes to a real system, a simulation of the real system is a viable and valuable alternative. By creating a simulation of the real system, the effects of changes made can be tested in the simulation instead of the real system. The dissertation presents a model by which subscriber behaviour within a cellular network can be simulated. The model that is introduced to this end is called the Mobile Network Subscriber-Behaviour Simulation (MNSS). Subscriber behaviour is important as it is largely responsible for the variation in traffic within the cellular network. Thus, the research presented in the dissertation seeks to provide a means by which the planning and operation of cellular networks can be enhanced through a better comprehension of network traffic, which is influenced by subscriber behaviour. In the dissertation, specific focus is given to voice traffic, and, as such, the cellular network architecture that was chosen for the model is the GSM mobile network architecture due to its ubiquitous use in carrying voice traffic. The research presented in the dissertation begins with a discussion on the GSM architecture. Subscriber behaviour is simulated through the use of intelligent agents that are configured to interact with one another to form a Multi-agent System. In addition, the subscriber agents interact with, and adapt to, the mobile network component of the MNSS model, which constitutes the formation of a Complex Adaptive System. Therefore, the discussion is subsequently directed towards Artificial Intelligence, intelligent agents, Multi-agent Systems and Complex Adaptive Systems. Using these aforementioned concepts, the dissertation presents the MNSS model and a prototype implementation, as well as a discussion on the results obtained from this. The main purpose of the dissertation is to examine a means by which subscriber behaviour can be simulated in a cellular network, through the use of multiple intelligent agents that interact in a complex system. This is conducted for the express purpose of analysing the change in behaviour of the subscriber agents in relation to changes made to external conditions, such as the underlying cellular network or the behaviour of other subscriber agents.

Reconfigurable computing architectures combine the flexibility of software with the performance of custom hardware. Such architectures are of particular interest at the nanoscale level. We argue that a bottom-up self-assembled fabric of nodes will be easier and cheaper to manufacture, however, one has to make compromises with regards to the device regularity, homogeneity, and reliability. The goal of this thesis is to evaluate the performance and cost of a self-configurable computing architecture composed of simple reconfigurable nodes for unstructured and unknown fabrics. We built a software and hardware framework for this purpose. The framework enables creating an irregular network of compute nodes where each node can be configured as a simple 2-input, 4-bit logic gate. The compute nodes are organized hierarchically by sending a packet through a top anchor node that recruits compute nodes with a chemically-inspired algorithm. The nodes are then self-configured by means of a gate-level netlist describing any digital logic circuit. A topology-agnostic optimization algorithm inspired by simulated annealing is then initiated to self-optimize the circuit for latency. Latency comparisons between non-optimized, brute-force optimized and our optimization algorithm are made. We further implement the architecture in VHDL and evaluate hardware cost, area, and energy consumption. The simple on-chip topology-agnostic optimization algorithm we propose results in a significant (up to 50\%) performance improvement compared to the non-optimized circuits. Our findings are of particular interest for emerging nano and molecular-scale circuits.

Thesis (M. Tech. (Information Technology)) -- Central University of technology, Free State, 2011 / Traditional designs for component-handling platforms are rigidly linked to the product being produced. Control and monitoring methods for these platforms consist of various proprietary hardware controllers containing the control logic for the production process. Should the configuration of the component handling platform change, the controllers need to be taken offline and reprogrammed to take the changes into account. The current thinking in component-handling system design is the notion of re-configurability. Reconfigurability means that with minimum or no downtime the system can be adapted to produce another product type or overcome a device failure. The re-configurable component handling platform is built-up from groups of independent devices. These groups or cells are each responsible for some aspect of the overall production process. By moving or swopping different versions of these cells within the component-handling platform, re-configurability is achieved. Such a dynamic system requires a flexible communications platform and high-level software control architecture to accommodate the reconfigurable nature of the system. This work represents the design and testing of the core of a re-configurable production control software platform. Multiple software components work together to control and monitor a re-configurable component handling platform. The design and implementation of a production database, production ontology, communications architecture and the core multi-agent control application linking all these components together is presented.

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, [2014] / The project is aimed at exploring the application of Machine Vision in a Reconfigurable Manufacturing System (RMS) Environment. The Machine Vision System interfaces with the RMS to verify the reconfiguration and positioning of devices within the assembly system, and inspects the product for defects that infringe on the quality of that product. The vision system interfaces to the Multi-agent System (MAS), which is in charge of scheduling and allocating resources of the RMS, in order to communicate and exchange data regarding the quality of the product. The vision system is comprised of a Compact Vision System (CVS) device with fire-wire cameras to aid in the image acquisition, inspection and verification process. Various hardware and software manufacturers offer a platform to implement this with a multiple array of vision equipment and software packages. The most appropriate devices and software platform were identified for the implementation of the project. An investigation into illumination was also undertaken in order to determine whether external lighting sources would be required at the point of inspection. Integration into the assembly system involved the establishment communication between the vision system and assembly system controller.

Thesis (M. Tech. (Engineering Electrical)) -- Central University of Technology, Free State, [2014] / This dissertation evaluates the software and hardware components used to develop a Reconfigurable Assembly System with an Integrated Information Management System. The assembly system consists of a modular Cartesian robot and vision system. The research focuses on the reconfigurability, modularity, scalability and flexibility that can be achieved in terms of the software and hardware components used within the system. The assembly system can be divided into high-level control and low-level control components. All information related to the product, Cartesian positioning and processes to follow resides in the Information Management System. The Information Management System is the high-level component and consists of a database, web services and low-levelcontrol drivers. The high-level system responds to the data received from the low-level systems and determines the next process to take place. The low-level systems consist of the PLC (Programmable Logic Controller) and the vision system. The PLC controls the Cartesian robot’s motor controllers and handles all events raised by field devices (e g. sensors or push buttons). The vision system contains a number of pre-loaded inspections used to identify barcodes and parts, obtain positioning data and verify the products’ build quality. The Cartesian robot’s positioning data and the vision system’s inspections are controlled by the Information Management System. The results showed that the high-level control software components are able to add more modularity and reconfigurability to the system, as it can easily adapt to changes in the product. The high-level control components also have the ability to be reconfigured while the assembly system is online without affecting the assembly system. The low-level control system is better suited to handling the control of motor controllers, field devices and vision inspections over an industrial network.