We examine the problem of simultaneous drive and capacitance sensing, on a microelectromechanical systems (MEMS)
device, where the drive is a bipolar AC waveform. The attention of this paper is particularly focused on wavelength
calibration of the microspectrometer, a MEMS micromachined Fabry Perot filter monolithically integrated with a
photodetector. However, this work is also very pertinent to other bipolar AC driven MEMS devices, which presently use
separate measurement MEMS structures. To avoid charging effects, the microspectrometer must be driven by an
AC waveform and, the only option for capacitance measurement is to do so simultaneously, on the same terminals, as
the drive waveform is applied. We propose a novel differential capacitive sensing circuit to determine the centre
wavelength of the MEMS-based micro-spectrometer, allowing closed-loop control of the microspectrometer's centre
wavelength. Automatic calibration can be realized with the addition of a known light source.
In this paper, a current-mode VLSI architecture enabling on read-out skin detection without the need for any on-chip memory elements is proposed. An important feature of the proposed architecture is that it removes the need for demosaicing. Color separation is achieved using the strong wavelength dependence of the absorption coefficient in silicon. This wavelength dependence causes a very shallow absorption of blue light and enables red light to penetrate deeply in silicon. A triple-well process, allowing a P-well to be placed inside an N-well, is chosen to fabricate three vertically integrated photodiodes acting as the RGB color detector for each pixel. Pixels of an input RGB image are classified as skin or non-skin pixels using a statistical skin color model, chosen to offer an acceptable trade-off between skin detection performance and implementation complexity. A single processing unit is used to classify all pixels of the input RGB image. This results in reduced mismatch and also in an increased pixel fill-factor. Furthermore, the proposed current-mode architecture is programmable, allowing external control of all classifier parameters to compensate for mismatch and changing lighting conditions.
In this paper an image enhancing technique is described. It is based on Shunting Inhibitory Cellular Neural Networks. As the limitation of the linear approaches to image coding, enhancement, and feature extraction became apparent, research in image processing began to disperse into the three goal-driven directions. However SICNNs model simultaneously addresses the three problems of coding, enhancement, and extraction as it acts to compress the dynamic range, reorganize the signal to improve visibility, suppress noise, and identify local features. The algorithm we are describing is simple and cost-effective, and can be easily applied in real-time processing for digital still camera application.