Development Of An 8-Bit Fpga-Based Asynchronous Risc Pipelined Processor For Data Encryption

Microprocessors are widely used in various applications. One of the application is in the area of data security where data are encrypted and decrypted before and after transfer via communication channel. The microprocessor design can be categorized into two types, which are synchronous and asynchronous processors. The asynchronous processor may offer better speed improvement because it is self-timed where a control circuit will generate enable signals for all instruction executions based on the request and acknowledgement signals. Unlike the asynchronous design, synchronous design requires global clock. The clock must be long enough to accommodate the worst-case delay. In this work, an 8-bit asynchronous processor is designed based on a synchronous RISC pipe lined processor architecture. The synchronous processor consists of three stages. They are instruction fetch stage, instruction decode stage and execution stage. The reduce instruction set computer (RISC) architecture is used to minimize the instruction and to perform specific operation. To design the asynchronous processor, an asynchronous control circuit is added to synchronous design. The asynchronous control circuit is designed based on handshake protocol. Both the synchronous and asynchronous designs are applied fully using VHDL. The MAX+PLUS II is used as the simulation tools to design and for design verification. The UP1 education board that contains the FLEX10K chip is used to observe the hardware operation. The asynchronous processor was successfully designed with higher million instructions per second (MIPS) and higher operation frequency as compared to synchronous processor. The asynchronous processor has 10.772 MIPS and operated under frequency of 11. 16MHz. The asynchronous processor design consumed 63% of the total logic cells in FLEX10K chip. The processor fits in FLEX10K and provides extra spaces for future expansion

DESIGN OF A 2x2 LINEAR MPC SCHEME FOR A SOLID OXIDE FUEL CELL

Fuel cell is one of the promising energy sources that produce electrical energy with almost zero pollutant. Although fuel cell had been invented for quite some time, it is only recently that fuel cell garners the attention in the energy industry for their clean electricity generation. Among all the available fuel cell, solid oxide fuel cell (SOFC) is one of the most interesting fuel cells types due to high energy efficiency, low emission from the chemical reaction, long-term stability, flexibility in options for fuel and low cost. Since the SOFC is to be used as an electrical source, there is a need to keep the fuel cell in a state of constant power output. Hence, maintaining a fuel cell system in correct operating conditions and a good control system is required. Model Predictive Control (MPC) proves to be an effective control strategy to control the power output of the SOFC. In this paper, the problem statement is defined and an objective is developed. In literature review, the more in depth review will be done on SOFC and MPC. Other than that, literature review also discusses the application of MPC to fuel cell in general, not limiting to Solid Oxide fuel cell. A detailed methodology on how the project will be simulated is included in Chapter 3.. In Chapter 4, the results of the simulation of scenarios will be discussed. Conclusion for the overall activities which have been carried out for this project will be in Chapter 5

Embedding Decision Heuristics in Discrete Choice Models: Assessing the MERITS of Majority of Confirming Dimensions, Extremeness Aversion, and Reference Revision

Contrary to the usual assumption of fixed, well-defined preferences, it is increasingly evident that individuals are likely to approach a choice task using decision heuristics that depend on the choice environment. These include heuristics defined by the local choice context, such as the gains or losses of an attribute value relative to the other attributes. Recent empirical findings also demonstrate that previous choices and previously encountered choice tasks can affect the current choice outcome, indicating a form of inter-dependence across choice sets. A number of these heuristics, namely the majority of confirming dimensions (MCD), the extremeness aversion and the reference revision heuristics, are analysed. These heuristics are not new, but their application, using the discrete choice modelling framework, to the transportation field has only barely begun. In particular, arising from the extremeness aversion heuristic, three models are discussed. The first is a recently developed model of context dependence known as the random regret minimisation (RRM) model. The second model is a non-linear utility model that makes reference to the worst attribute level in a choice set. The third model is a “relative advantage maximisation” (RAM) model, with an updated version of the existing RAM model introduced in this thesis. All these models are compared against one another and with the standard random utility maximisation (RUM) model. The results strongly indicate that incorporating context dependency into existing models should be a key consideration for the practitioner. Moreover, having identified some heuristics of special interest, the role of multiple heuristics in choice behaviour is also analysed. Interestingly, the heuristics themselves can be embedded directly into the utility functions by means of heuristic weighting functions, which weight the contribution of each heuristic to overall utility. The thesis examines the validity of such an approach

Development of Advanced Materials Using Microwaves

Ph.DDOCTOR OF PHILOSOPH

The formation of identities and art museum education: the Singapore case

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Managing Epistemic Uncertainties in the Underlying Models of Safety Assessment for Safety-Critical Systems

When conducting safety assessment for safety-critical systems, epistemic uncertainty is an ever-present challenge when reasoning about the safety concerns and causal relationships related to hazards. Uncertainty around this causation thus needs to be managed well. Unfortunately, existing safety assessment tends to ignore unknown uncertainties, and stakeholders rarely track known uncertainties well through the system lifecycle. In this thesis, an approach is described for managing epistemic uncertainties about the system and safety causal models that are applied in a safety assessment. First, the principles that define the requirements for the approach are introduced. Next, these principles are used to construct three distinct steps that constitute an approach to manage such uncertainties. These three steps involve identifying, documenting and tracking the uncertainties throughout the system lifecycle so as to enable intervention to address the uncertainties. The approach is evaluated by integrating it with two existing safety assessment techniques, one using models from a system viewpoint and the other with models from a component viewpoint. This approach is also evaluated through peer reviews, semi-structured interviews with practitioners, and by review against requirements derived from the principles. Based on the evaluation results, it is plausible that our approach can provide a feasible and systematic way to manage epistemic uncertainties in safety assessment for safety-critical systems