A number of functions for the autofocusing of microscopes and other optical instruments are to be found in the literature. In this article we compare 11 of them to determine, in an objective manner, which functions are most suitable for implementation with real-time video acquisition systems. Three different images, each representing a typical class of imAutofocus algorithms are of particular importance in scanning microscope systems. The focus may have to be adjusted when the system mechanically moves from field to field, or when there is mechanical drift between two scans of the same field. In general algorithms that determine optimal focus for an image are based upon maximizing or minimizing some function that represents a "figure-of-merit." Algorithms have to be fast, as the total scan time is usually important. Thus the focus criterion functions used in the algorithms must be easy to calculate as, for example, with hardware and the video signal from a TV camera.A number of focus functions have been proposed in the literature. Some of these deal only with special applications, while others are more generally applicable. The purpose of this study is to compare different proposed focus functions and determine their domain of general or special applicability. A literature search was conducted for focus functions which could be derived from the video signal. Such functions are at a n extremum when the system is in focus. A general model for such a function is given in Figure 1 as well as some definitions of properties of the function.The following criteria are used in the selection and evaluation of the focus functions: a) Unimodality. The focus function must be unimodal; i.e., only a single maximum or minimum should be present. This simplifies considerably an autofocus algorithm and avoids potential errors from local extrema. b) Accuracy. The extremum must be present when the system is in focus. c) Reproducibility. A sharp top of the extremum results in good reproducibility. d) Range. The range over which an in-focus image can age, are used in the comparison. Among the best focus functions found in our study are the squared magnitude gradient, the squared Laplacian, and the normalized image standard deviation.

A smart charging profile was implemented on 39 public charging stations in Amsterdam on which the current level available for electric vehicle (EV) charging was limited during peak hours on the electricity grid (07:00-08:30 and 17:00-20:00) and was increased during the rest of the day. The impact of this profile was measured on three indicators: average charging power, amount of transferred energy and share of positively and negatively affected sessions. The results are distinguished for different categories of electric vehicles with different charging characteristics (number of phases and maximum current). The results depend heavily on this categorisation and are a realistic measurement of the impact of smart charging under real world conditions. The average charging power increased as a result of the new profile and a reduction in the amount of transferred energy was detected during the evening hours, causing outstanding demand which was solved at an accelerated rate after limitations were lifted. For the whole population, 4% of the sessions were positively affected (charged a larger volume of energy) and 5% were negatively affected. These numbers are dominated by the large share of plug-in hybrid electric vehicles (PHEVs) in Amsterdam which are technically not able to profit from the higher current levels. For new generation electric vehicles, 14% of the sessions were positively affected and the percentage of negatively affected sessions was 5%.