Blood pressure is one of the fundamental clinical measures. For more than 100 years, clinicians and researchers have used the mercury sphygmomanometer for blood pressure measurement. Environmental concern about mercury contamination has highlighted the need to find a replacement for traditional mercury sphygmomanometers. A number of currently used non-invasive blood pressure measurement methods have been studied in this research. The most commonly used automatic pressure monitoring method nowadays is the Oscillometric method. Height-based and Slope-based criteria are the two general means used to determine the systolic and diastolic pressures. However, these two criteria have many disputed points, making them debatable as a good standard for blood pressure measurement. For this reason, the auscultatory method continues to be the gold-standard for non-invasive blood pressure measurement. Current research uses a newly developed cuff with three different lengths of piezo film sensors and a pressure sensor to collect signals from the brachial artery. The objectives of the research are to process the measured signal from the sensors and develop a blood pressure measurement algorithm that will accurately determine the blood pressure noninvasively. Signal processing and heart beat / heart rate detection software have been developed. The best algorithm has been selected from three developed algorithms for further modification and validation. The final algorithm used two feed-forward Neural Networks to classify the acquired pressure signals into various regions of the pressure signals. The final algorithm has been tested on 258 measurements from 86 subjects. The testing result showed that the algorithm achieved grade A for both systolic and diastolic pressures according to the British Hypertension Society protocol. The mean differences (SD) between the observers and the developed algorithm were 1.44 (5.27) mmHg and 1.77 (6.17) mmHg for systolic and diastolic pressures, respectively, which also fulfilled the Association for the Advancement of Medical Instrumentation protocol. In conclusion, this algorithm was successfully developed and it is recommended for further clinical trial in a wider adult population. Further development of this algorithm also includes extending to other subgroups such as pregnant women, arrhythmia, diabetics and other subjects with diseases.

The problem addressed by this thesis is the need for a software measurement tool that enforces a uniform measurement algorithm on several programming languages. The introductory chapter discusses the concern for software measurement and provides background for the specific models and metrics that are studied. A multilingual software measurement tool is then introduced, that analyzes programs written in Ada, C, Pascal, or PL/I, and quantifies over thirty different program attributes. Metrics computed by the program include McCabe's measure of cyclomatic complexity and Halstead's software science metrics. Some results and conclusions of preliminary data analysis, using the tool, are also given. The appendices contain exhaustive counting algorithms for obtaining the metrics in each language.