Design of digital differentiators

A digital differentiator simply involves the derivation of an input signal. This work includes the presentation of first-degree and second-degree differentiators, which are designed as both infinite-impulse-response (IIR) filters and finite-impulse-response (FIR) filters. The proposed differentiators have low-pass magnitude response characteristics, thereby rejecting noise frequencies higher than the cut-off frequency. Both steady-state frequency-domain characteristics and Time-domain analyses are given for the proposed differentiators. It is shown that the proposed differentiators perform well when compared to previously proposed filters. When considering the time-domain characteristics of the differentiators, the processing of quantized signals proved especially enlightening, in terms of the filtering effects of the proposed differentiators. The coefficients of the proposed differentiators are obtained using an optimization algorithm, while the optimization objectives include magnitude and phase response. The low-pass characteristic of the proposed differentiators is achieved by minimizing the filter variance. The low-pass differentiators designed show the steep roll-off, as well as having highly accurate magnitude response in the pass-band. While having a history of over three hundred years, the design of fractional differentiator has become a ‘hot topic’ in recent decades. One challenging problem in this area is that there are many different definitions to describe the fractional model, such as the Riemann-Liouville and Caputo definitions. Through use of a feedback structure, based on the Riemann-Liouville definition. It is shown that the performance of the fractional differentiator can be improved in both the frequency-domain and time-domain. Two applications based on the proposed differentiators are described in the thesis. Specifically, the first of these involves the application of second degree differentiators in the estimation of the frequency components of a power system. The second example concerns for an image processing, edge detection application.

The Trier Social Stress Test: principles and practice

Researchers interested in the neurobiology of the acute stress response in humans require a valid and reliable acute stressor that can be used under experimental conditions. The Trier Social Stress Test (TSST) provides such a testing platform. It induces stress by requiring participants to make an interview-style presentation, followed by a surprise mental arithmetic test, in front of an interview panel who do not provide feedback or encouragement. In this review, we outline the methodology of the TSST, and discuss key findings under conditions of health and stress-related disorder. The TSST has unveiled differences in males and females, as well as different age groups, in their neurobiological response to acute stress. The TSST has also deepened our understanding of how genotype may moderate the cognitive neurobiology of acute stress, and exciting new inroads have been made in understanding epigenetic contributions to the biological regulation of the acute stress response using the TSST. A number of innovative adaptations have been developed which allow for the TSST to be used in group settings, with children, in combination with brain imaging, and with virtual committees. Future applications may incorporate the emerging links between the gut microbiome and the stress response. Future research should also maximise use of behavioural data generated by the TSST. Alternative acute stress paradigms may have utility over the TSST in certain situations, such as those that require repeat testing. Nonetheless, we expect that the TSST remains the gold standard for examining the cognitive neurobiology of acute stress in humans.