Formalization of Transform Methods using HOL Light

Transform methods, like Laplace and Fourier, are frequently used for analyzing the dynamical behaviour of engineering and physical systems, based on their transfer function, and frequency response or the solutions of their corresponding differential equations. In this paper, we present an ongoing project, which focuses on the higher-order logic formalization of transform methods using HOL Light theorem prover. In particular, we present the motivation of the formalization, which is followed by the related work. Next, we present the task completed so far while highlighting some of the challenges faced during the formalization. Finally, we present a roadmap to achieve our objectives, the current status and the future goals for this project.Comment: 15 Pages, CICM 201

A systematic review of the effects of residency training on patient outcomes

<p>Abstract</p> <p>Background</p> <p>Residents are vital to the clinical workforce of today and tomorrow. Although in training to become specialists, they also provide much of the daily patient care. Residency training aims to prepare residents to provide a high quality of care. It is essential to assess the patient outcome aspects of residency training, to evaluate the effect or impact of global investments made in training programs. Therefore, we conducted a systematic review to evaluate the effects of relevant aspects of residency training on patient outcomes.</p> <p>Methods</p> <p>The literature was searched from December 2004 to February 2011 using MEDLINE, Cochrane, Embase and the Education Resources Information Center databases with terms related to residency training and (post) graduate medical education and patient outcomes, including mortality, morbidity, complications, length of stay and patient satisfaction. Included studies evaluated the impact of residency training on patient outcomes.</p> <p>Results</p> <p>Ninety-seven articles were included from 182 full-text articles of the initial 2,001 hits. All studies were of average or good quality and the majority had an observational study design.Ninety-six studies provided insight into the effect of 'the level of experience of residents' on patient outcomes during residency training. Within these studies, the start of the academic year was not without risk (five out of 19 studies), but individual progression of residents (seven studies) as well as progression through residency training (nine out of 10 studies) had a positive effect on patient outcomes. Compared with faculty, residents' care resulted mostly in similar patient outcomes when dedicated supervision and additional operation time were arranged for (34 out of 43 studies). After new, modified or improved training programs, patient outcomes remained unchanged or improved (16 out of 17 studies). Only one study focused on physicians' prior training site when assessing the quality of patient care. In this study, training programs were ranked by complication rates of their graduates, thus linking patient outcomes back to where physicians were trained.</p> <p>Conclusions</p> <p>The majority of studies included in this systematic review drew attention to the fact that patient care appears safe and of equal quality when delivered by residents. A minority of results pointed to some negative patient outcomes from the involvement of residents. Adequate supervision, room for extra operation time, and evaluation of and attention to the individual competence of residents throughout residency training could positively serve patient outcomes. Limited evidence is available on the effect of residency training on later practice. Both qualitative and quantitative research designs are needed to clarify which aspects of residency training best prepare doctors to deliver high quality care.</p

Filters

A transfer function is a mathematical description of how an input signal is changed into an output signal. Transfer functions can be expressed in either the time or frequency domain. Filtering is perhaps the most common type of transfer function and possibly the most common type of signal processing operation. The aim of filtering is to reject (attenuate) unwanted parts of the recorded signal and enhance (amplify) the part of the signal that contains the information of interest. The mathematical description of these changes in the signal is the transfer function of the effects of the filter. Filtering can be performed on both analog and digital signals and can be operated in both the time and frequency domains. In this chapter we will discuss how filtering is performed for the purposes of improving signal-to-noise ratio, prevention of aliasing when sampling, and reconstruction of digital signals into analog form

Analog and Digital Signals

“Digital” has been the buzz word for the last couple of decades. The latest and greatest electronic devices have been marketed as digital and cardiology equipment has been no exception. “Analog” has the connotation of being old and outdated, while “digital” has been associated with new and advanced. What do these terms actually mean and is one really better than the other? By the end of the chapter, the reader should know what analog and digital signals are, their respective characteristics, and the advantages and disadvantages of both signal types. The reader will also understand the fundamentals of sampling, including the trade-offs of high sampling rates and high amplitude resolution, and the distortion that is possible with low sampling rates and low amplitude resolution

Signals in the Frequency Domain

Interpreting signals in the time domain is intuitively grasped by most people as time plots are commonly encountered in everyday life. Time domain plots represent the variable of interest in the y-axis and time in the x-axis. Stock charts show the up and down trends of your investment’s value over time. Your electric company bill will show your power usage month-to-month over the course of the year. Signals in cardiology are also most commonly displayed in the time domain. ECG and blood pressure monitors, for example, continuously plot voltages or pressure vs. time