The concrete maturity method can be utilized to determine in situ strength of concrete. It uses the temperature of concrete to determine a maturity index that can then be used to determine strength of concrete. However, monitoring the concrete temperature using thermocouples brings up a wiring issue, which is not advisable in an equipment and human intensive area like a construction site. One of the ways to get around this wiring issue is to use Radio Frequency Identification (RFID) technology, which is capable of transmitting information wirelessly. Previous research implemented using ultra high frequency RFID tags embedded in fresh concrete found that water could be the impediment for transmitting RFID signal from within concrete during early stages of curing. From literature it was found that lower the frequency, better the chances of the wave penetrating water. The objective of the research was to figure out whether the frequency of RFID tags has any relationship with the readability of RFID tags embedded in fresh concrete. For this investigation, low frequency, high frequency, and ultra high frequency RFID tags were tested within fresh concrete to see any difference between tags in terms of transmitting information. This experiment was carried out in a controlled space to reduce the number of variables affecting the experiment outcome. The low frequency, high frequency, and ultra high frequency RFID tags were placed within 2 in x 3 in x 2 in wooden formwork at a depth of 4 in, 8 in, and 12 in. Ready mix concrete was poured into the formwork and 3 concrete cubes were cast with the tags embedded within them. Readers that could be connected to a laptop were used to monitor and collect the time at which these RFID tags can be detected. The test showed that the RFID signals from the low frequency tags at all depths were detected as soon as concrete was poured. The Ultra High Frequency tags placed at the 4" level could be detected 15 minutes after concrete was poured. The UHF tags at the 8" level could be detected after 30 minutes. The UHF tags at the 12" level took on an average 2 hours to be detected from the vicinity of the formwork. The greater the depth at which the ultra high frequency tag was buried the longer it took for it to be detected. The high frequency tags could be detected only at the 4" level. The reason the performance of the HF card degraded in concrete could be because it uses an aluminum foil antenna which is more susceptible to the environment changing the relative permeability. A copper wire antenna could have fared better in this condition, increasing the chances of detecting the tag. Moreover a passive tag was used. The read range and chances of detection could have been increased had an active tag been used. The power of the reader that was used was also very less which might have contributed to the tag not being detected. Among the tags that were used in the experiment it was found that low frequency tags was the tag that could be detected the earliest after concrete was poured into the forms. However, the maximum read range of the tag observed in the experiment was 20" which is too small a distance to be used on an actual construction site.

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The thesis "Four Essays on the Econometric Modelling of Volatility and Durations" consists of four research papers in the area of financial econometrics on topics of the modelling of financial market volatility and the econometrics of ultra-high-frequency data. The aim of the thesis is to develop new econometric methods for modelling and hypothesis testing in these areas. The second chapter introduces a new model, the time-varying GARCH (TV-GARCH) model, in which volatility has a smooth time-varying structure of either additive or multiplicative type. To characterize smooth changes in the (un)conditional variance we assume that the parameters vary smoothly over time according to the logistic transition function. A data-based modelling technique is used for specifying the parametric structure of the TV-GARCH models. This is done by testing a sequence of hypotheses by Lagrange multiplier tests presented in the chapter. Misspecification tests are also provided for evaluating the adequacy of the estimated model. The third chapter addresses the issue of modelling deterministic changes in the unconditional variance over a long return series. The modelling strategy is illustrated with an application to the daily returns of the Dow Jones Industrial Average (DJIA) index from 1920 until 2003. The empirical results sustain the hypothesis that the assumption of constancy of the unconditional variance is not adequate over long return series and indicate that deterministic changes in the unconditional variance may be associated with macroeconomic factors. In the fourth chapter we propose an extension of the univariate multiplicative TV-GARCH model to the multivariate Conditional Correlation GARCH (CC-GARCH) framework. The variance equations are parameterized such that they combine the long-run and the short-run dynamic behaviour of the volatilities. In this framework, the long-run behaviour is described by the individual unconditional variances, and it is allowed to vary smoothly over time according to the logistic transition function. The effects of modelling the nonstationary variance component are examined empirically in several CC-GARCH models using pairs of seven daily stock return series from the S&P 500 index. The results show that the magnitude of such effect varies across different stock series and depends on the structure of the conditional correlation matrix. An important feature of financial durations is the evidence of a strong diurnal variation over the trading day. In the fifth chapter we propose a new parameterization for describing the diurnal pattern of trading activity. The parametric structure of the diurnal component allows the duration process to change smoothly over the time-of-day according to the logistic transition function. The empirical results suggest that the diurnal variation may not always have the inverted U-shaped pattern for the trade durations as documented in earlier studies.

The availability of ultra high-frequency (UHF) data on transactions has revolutionised data processing and statistical modelling techniques in finance. The unique characteristics of such data, e.g. discrete structure of price change, unequally spaced time intervals and multiple transactions have introduced new theoretical and computational challenges. In this study, we develop a Bayesian framework for modelling integer-valued variables to capture the fundamental properties of price change. We propose the application of the zero inflated Poisson difference (ZPD) distribution for modelling UHF data and assess the effect of covariates on the behaviour of price change. For this purpose, we present two modelling schemes; the first one is based on the analysis of the data after the market closes for the day and is referred to as off-line data processing. In this case, the Bayesian interpretation and analysis are undertaken using Markov chain Monte Carlo methods. The second modelling scheme introduces the dynamic ZPD model which is implemented through Sequential Monte Carlo methods (also known as particle filters). This procedure enables us to update our inference from data as new transactions take place and is known as online data processing. We apply our models to a set of FTSE100 index changes. Based on the probability integral transform, modified for the case of integer-valued random variables, we show that our models are capable of explaining well the observed distribution of price change. We then apply the deviance information criterion and introduce its sequential version for the purpose of model comparison for off-line and online modelling, respectively. Moreover, in order to add more flexibility to the tails of the ZPD distribution, we introduce the zero inflated generalised Poisson difference distribution and outline its possible application for modelling UHF data.

Over the years, RFID (radio frequency identification) technology has gained popularity in a number of applications. The decreased cost of hardware components along with the recognition and implementation of international RFID standards have led to the rise of this technology. One of the major factors associated with the implementation of RFID infrastructure is the cost of tags. Low frequency (LF) RFID tags are widely used because they are the least expensive. The drawbacks of LF RFID tags include low data rate and low range. Most studies that have been carried out focus on one frequency band only. This thesis presents an analysis of RFID tags across low frequency (LF), high frequency (HF), and ultra-high frequency (UHF) environments. Analysis was carried out using a simulation model created using OPNET Modeler 17. The simulation model is based on the Basic Frame Slotted ALOHA (BFSA) protocol for non-unique tags. As this is a theoretical study, environmental disturbances have been assumed to be null. The total census delay and the network throughput have been measure for tags ranging from 0 to 1500 for each environment. A statistical analysis has been conducted in order to compare the results obtained for the three different sets.

The goal of this thesis is to detect pacing pulses in ultra high-frequency ECG so as to remove these pacing pulses. It makes evaluation of higher frequency components of QRS complex possible. This evaluation is impossible while pacing pulses are present. Chosen issue is solved using heuristic algorithm. Algorithm uses spacing of signal by line in the area which is not influenced by pacing pulses. Subsequently this line is made longer and using differences between line and signal (or another rules) edges of pacing pulses are detected. The top of the stimulation tip is detected by thresholding envelope of original signal´s first difference. More algorithms are tested in this thesis. Several methods of removing pacing pulses are suggested in thesis. Envelopes of high-frequency components are created. Envelopes are analyzed subsequently and suggested methods of removing pacing pulses are compared on the basis of these analysis. Finally the detection efficiency is evaluated.