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Export citation of selected articles as: Sensors9 12; https: We present circular architecture bioimplant strain sensors that facilitate a strong resonance frequency shift with mechanical deformation. The clinical application area of these sensors is for in vivo assessment of bone fractures. Using a rectangular geometry, we obtain a resonance shift of MHz for a single device and MHz for its triplet configuration with three side-by-side resonators on chip under an applied load of 3, N.
Using the same device parameters with a circular isotropic architecture, we achieve a resonance frequency shift of MHz for the single device and MHz for its triplet configuration, demonstrating substantially increased sensitivity.
The partial charge transfer technique can expand the dynamic range of a CMOS image sensor by synthesizing two types of signal, namely the long and short accumulation time signals. However the short accumulation time signal obtained from partial transfer operation suffers of non-linearity with respect to the incident light. In this paper, an analysis of the non-linearity in partial charge transfer technique has been carried, and the relationship between dynamic range and the non-linearity is studied.
The results show that the non-linearity is caused by two factors, namely the current diffusion, which has an exponential relation with the potential barrier, and the initial condition of photodiodes in which it shows that the error in the high illumination region increases as the ratio of the long to the short accumulation time raises. Moreover, the increment of the saturation level of photodiodes also increases the error in the high illumination region.
This article belongs to the Special Issue Image Sensors Cruz 1 and Fernando Montes 4. This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses.
The stereo analysis problem consists of the following steps: Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching.
In the second step the features are matched based on the application of the following four well known matching epipolarsimilarityordering and uniqueness.
The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion.