This paper describes a new first-and second-order delta-sigma modulator concept where the first integrator is extracted and implemented by a frequency modulator with the modulating signal as the input. The result is a simple delta-sigma modulator with no need for digital-to-analog converters, allowing straightforward multi-bit quantization. Without the frequency modulator, the circuit becomes a frequency-to-digital converter with delta-sigma noise shaping. An experimental first-and second-order modulator have been implemented in a 1.2-¼m standard digital CMOS process and the results confirm the theory. For the first-order modulator an input signal amplitude of 150mV resulted in a SQNR of ³115dB at 2MHz sampling frequency and signal bandwidth 500Hz.

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We propose a field programmable gate array (FPGA) implementation for a run-time adaptable evolvable hardware classifier system. Previous implementations have been based on a high-level virtual reconfigurable circuit technique which requires many FPGA resources. We therefore apply an intermediate level reconfiguration technique which consists of using the FPGA lookup tables as shift registers for reconfiguration purposes. This leads to significant resource savings, reducing the classifier circuit size to less than one third of the original implementation. This in turn has made it possible to implement a larger, more accurate classifier than before, giving 97.5% recognition accuracy for a face image application. Experiments also show that a reduction of data element resolution can lead to further resource savings while still maintaining high classification accuracy.

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Type-2 fuzzy logic systems are proposed as an alternative solution in the literature when a system has a large amount of uncertainties and type-1 fuzzy systems come to the limits of their performances. In this study, an adaptive type-2 fuzzy-neuro system is designed for the position control of a servo system with an intelligent sensor. The sensor gives different resistance values with respect to the stretch of it, and it is supposed to be used in an robotic arm position measurement system. These kinds of sensors can be used in human-assistance robots that have soft surfaces in order not to damage the humans. However, these sensors have time-varying gains and uncertainties that are not very easy to handle. Moreover, they generally have a hysteresis on their input-output relations. The simulation results show that the control algorithm developed gives better performances when compared to conventional type-1 fuzzy controllers on such a highly nonlinear, uncertain system.