The response-time curves of several typical dynamic cycles were i

The response-time curves of several typical dynamic cycles were illustrated in Figure 5. It is estimated that the response time is about 3 s, and the recovery time is about 25 s. Here the response time is shorter than the recovery time, which is the same as the general phenomenon of humidity sensors. The MWCNT network has a good reversibility, although its sensitivity decreases about 7% after four cycles of humidity switch between 25% and 75% RH. This result indicates that the interaction between water vapor and MWCNT networks is mainly dominated by physisorption with a weak bond.Figure 5.Time response and recovery curve of the MWCNT network from RH=25 % to 75 %.To determine the effect of temperature on resistances of MWCNT networks, the resistance of the MWCNT network was measured at a temperature range from 293K to 393K.

The resistance linearly decreased with increasing temperatures,
The image formation process through consumer imaging devices is intrinsically noisy. This is especially true using low-cost devices such as mobile-phones, PDAs, etc., mainly in low-light conditions and the absence of flash-guns [1].The final perceived quality of images acquired by digital sensors can be optimized through multi-shot acquisitions (e.g., extending dynamic range [2], increasing resolution [3]) and/or using ad-hoc post-processing techniques [4,5] taking into account the raw data acquired by Bayer matrixed image sensors [6]. These are grayscale sensors covered by CFA (Color Filter Array) to enable color sensitivity, such that each cell of the sensor array is receptive to only one color component.

The final color image is obtained by means of a color reconstruction (demosaicing) algorithm that combines the color information of neighboring pixels [7�C9] and [10]. A useful review of technology and methods in the field can be found in [1] and [11].In this paper we propose a novel spatial noise reduction method that directly processes the raw CFA data, combining together HVS (Human Visual System) heuristics, texture/edges preservation techniques and sensor noise statistics, in order to obtain an effective adaptive denoising.The proposed algorithm introduces the concept of the usage of HVS peculiarities directly on the CFA raw data from the sensor. In addition, the complexity of the algorithm is kept low by using only spatial information and a small fixed-size filter processing window, allowing real-time performance on low cost imaging devices (e.

g., mobile phones, PDAs).The HVS properties, able to characterize or isolate unpleasant artifacts, are complex (highly nonlinear) phenomena not yet completely understood involving AV-951 a lot of complex parameters [12,13]. Several studies in the literature have tried to simulate and code some known aspects in order to find reliable image metrics [14�C16] and heuristics to also be applied for demosaicing [17].

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