IN-SITU APPROACH FOR CHARACTERIZATION AND MODELING OF TRANSPONDER PACKAGING TECHNIQUES IN RADIO FREQUENCY INDENTIFICATION SYSTEMS

In a typical Radio Frequency Identification system, the tag-reader communication is the most important characteristic of success or failure. In this system, the tag represents the weakest link in the equation and must be selected with great care. It is also important to recognize that a passive RFID tag derives its power from the RF energy generated by the reader. In turn, it communicates to the reader by modulation of the incident RF energy to create a backscatter signal, where any power loss between the antenna and the integrated circuit chip limits the maximum distance from which the tag can be read. Because the typical assembly flow of the RFID labels requires multiple steps, different assembly methodologies are being used to lower the final cost of the RFID label. Packaged parasitic components can significantly degrade the performance of the RFID tags. Today, the most insidious problem is the loss of energy due to the mismatch between the antenna and the IC chip. The final cost and fabrication requirements for the RFID tag impose a set of criteria on the assembly of the tag, where the typical methods for extracting and characterizing parasitic components of the packaging are not feasible. This research develops the theoretical mechanism for measuring and modeling the packaging parasitic components of the passive Ultra High Frequency RFID tags. The research is based on proven antenna theory and antenna measurement methods, which in turn will provide a benchmark for the current and future assembly methods for manufacturing of the RFID labels

New Approaches for Monitoring Quality Control in the Clinical Laboratory

Industrial Engineerin

The use of statistics in understanding pharmaceutical manufacturing processes

D.Eng.Industrial manufacturing processes for pharmaceutical products require a high level of understanding and control to demonstrate that the final product will be of the required quality to be taken by the patient. A large amount of data is typically collected throughout manufacture from sensors located around reaction vessels. This data has the potential to provide a significant amount of information about the variation inherent within the process and how it impacts on product quality. However to make use of the data, appropriate statistical methods are required to extract the information that is contained. Industrial process data presents a number of challenges, including large quantities, variable sampling rates, process noise and non-linear relationships. The aim of this thesis is to investigate, develop and apply statistical methodologies to data collected from the manufacture of active pharmaceutical ingredients (API), to increase the level of process and product understanding and to identify potential areas for improvement. Individual case studies are presented of investigations into API manufacture. The first considers prediction methods to estimate the drying times of a batch process using data collected early in the process. Good predictions were achieved by selecting a small number of variables as inputs, rather than data collected throughout the process. A further study considers the particle size distribution (PSD) of a product. Multivariate analysis techniques proved efficient at summarising the PSD data, to provide an understanding of the sources of variation and highlight the difference between two processing plants. Process capability indices (PCIs) are an informative tool to estimate the risk of a process failing a specification limit. PCIs are assessed and developed to be applied to data that does not follow a standard normal distribution. Calculating the capability from the percentiles of the data or the proportion of data outside of the specification limits has the potential to generate information about the capability of the process. Finally, the application of Bayesian statistical methods in pharmaceutical process development are investigated, including experimental design, process validation and process capability. A novel Bayesian method is developed to sequentially calculate the process capability when data is collected in blocks over time, thereby reducing the level of noise caused by small sample sizes

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed