Control systems theory is a wide area covering a range of artificial and physical phenomena. Control systems are systems that are designed to operate under strict specifications, to satisfy certain aims, like safety regulations in the industry, optimal production of goods, disturbance rejection in vehicles, smooth movement and placement of objects in warehousing, regulation of drug administration in medical operations, level control in chemical processes and many more. The present work provides an introduction to the fundamental principles of control system's analysis and design through the programming environment of Matlab and Simulink. Analysis of transfer function models is carried out though multiple examples in Matlab and Simulink, analyzing the dynamics of 1st and 2nd order systems, the role of the poles and zeros in the system's dynamic response, the effects of delay and the possibility to approximate higher order systems by lower order ones. In addition, examples are given from the fields of mechanical systems, medically induced anesthesia, neuroprosthetics and water level control, showcasing the use of controllers that satisfy certain design specifications.
In this work, a chaotification technique is proposed that can be used to enhance the complexity of any one-dimensional map by adding the remainder operator to it. It is shown that by an appropriate parameter choice, the resulting map can achieve a higher Lyapunov exponent compared to its seed map, and all periodic orbits of any period will be unstable, leading to robust chaos. The technique is tested on several maps from the literature, yielding increased chaotic behavior in all cases, as indicated by comparison of the bifurcation and Lyapunov exponent diagrams of the original and resulting maps. Moreover, the effect of the proposed technique in the problem of pseudo-random bit generation is studied. Using a standard bit generation technique, it is shown that the proposed maps demonstrate increased statistical randomness compared to their seed ones, when used as a source for the bit generator. This study illustrates that the proposed method is an efficient chaotification technique for maps that can be used in chaos-based encryption and other relevant applications.
Background and study aims
Measurement of colorectal polyp size during endoscopy is mainly performed visually. In this work, we propose a novel polyp size measurement system (Poseidon) based on artificial intelligence (AI) using the auxiliary water jet as a measurement reference.
Methods
Visual estimation, biopsy forceps-based estimation, and Poseidon were compared using a CT-colonography-based silicone model with 28 polyps of defined sizes. Four experienced gastroenterologists estimated polyp sizes visually and with biopsy forceps. Furthermore, the gastroenterologists recorded images of each polyp with the water jet in proximity for the application of Poseidon. Additionally, Poseidon's measurements of 29 colorectal polyps during clinical routine were compared to visual estimates.
Results
Visual estimation had the largest median percentage error (PE) of 25.2% (95% confidence interval (CI95%): 19.1, 30.4), followed by biopsy forceps-based estimation with median 20% (14.4, 25.6) in the silicone model. Poseidon presented a significantly lower median PE of 7.4% (5, 9.4; p<0.001) than other methods. During routine colonoscopies, Poseidon presented a significantly lower median PE (7.7% 6.1, 9.3) than visual estimation (22.1% 15.1, 26.9; p<0.001).
Conclusion
In this work, we present a novel AI-based method for measuring colorectal polyp size with significantly higher accuracy than other common sizing methods.
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