SUMMARYThe subject of synthesis of critical-monotonic low-pass amplitude characteristics will be revisited. Several new contributions will be given in order to: facilitate the choice of the proper transfer function, to allow cataloguing the transfer functions, to simplify the circuit synthesis procedure, and to perform synthesis in the form of a statevariable continuous time active filter. Four main criteria for transfer function synthesis will be implemented: maximally flat at the origin, maximum slope at the band-edge, maximal asymptotic attenuation, and minimal amplitude distortion in the pass-band. For every criterion, a class of filters will be generated and the coefficients of the transfer functions will be calculated and published for the first time (with one exception). Properties of the classes so generated will be quantitatively compared for the first time. The state-variable structure will be advised as the one with the simplest synthesis procedure. The procedure will be explained and the design process will be exemplified. Statistical tolerance analysis will be performed for the example solutions in order to complete the information for comparison.

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Oscillation Based Testing (OBT) is an effective and simple solution to the testing problem of continuous time analogue electronic filters. In this paper, diagnosis based on OBT is described for the first time. It will be referred to as OBD. A fault dictionary is created and used to perform diagnosis with artificial neural networks (ANNs) implemented as classifiers. The robustness of the ANN diagnostic concept is also demonstrated by the addition of white noise to the “measured” signals. The implementation of the new concept is demonstrated by testing and diagnosis of a second order notch cell realized with one operational amplifier. Single soft and catastrophic faults are considered in detail and an example of the diagnosis of double soft faults is also given.

Electronic devices are complex circuits, consisting of analog, switching, and digital subsystems that require direct current (DC) for polarization. Since they are connected to the mains delivering alternating current (AC), however, AC-to-DC converters are to be introduced between the mains and the electronics to be fed. A converter is an electric circuit containing several subsystems, the most important being the switch-mode power supply, drawing power from the mains in pulses hence it is highly nonlinear. That happens, in reduced amplitude, even when the electronics to be fed is switched off. The process of AC-to-DC conversion is not restricted to feeding electronic equipment only. It is more and more frequently encountered in modern smart-grid facilities giving rise to the importance of the studies referred hereafter. The converter can be studied (theoretically or by measurements) as two-port network with reactive and nonlinear port-impedances. Characterization is performed after determining the port electrical quantities which are voltages and currents. Based on these data power and power quality parameterspower factor and total harmonic distortion-may be extracted. When nonlinear loads are present, one should introduce new ways of thinking into the considerations due to the existence of harmonics and related power components. In that way the power factor can be generalized to total or true power factor where the apparent power, involved in its calculations, includes all harmonic components. After introducing a wide range of definitions used in contemporary literature, here we describe our measurement setup both as hardware and a software solution. The results reported unequivocally confirm the importance of the subject of characterization of small nonlinear loads to the grid having in mind their number which is rising without saturation seen in the near and even far future.

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Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points, hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating to the fault code and the circuit response for a given input signal. ANNs were used to model the fault dictionaries. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for mapping the measured data into fault code, which is equivalent to searching the fault dictionary performed by some other diagnostic procedures. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. A voting system was created at the topmost level in order to distinguish which ANN's output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter, and only one test point was used, i.e. the digital output. Full diversity of faults was considered in both digital (stuck-at and delay faults) and analog (parametric and catastrophic faults) parts of the diagnosed system. Special attention was paid to the faults related to the A/D and D/A interfaces within the circuit.

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