ABSTRACT
A method for distortion-tolerant recognition of objects in water using three-dimensional (3D) integral imaging with a neural network classification architecture is presented. Recognition algorithms are developed and experimental results are presented with rotation-variable 3D objects. To test the robustness of the system, objects are placed under a variety of water conditions, including variable Maalox-induced scattering levels and occlusion using pine needles. Neural networks have long been used for two-dimensional recognition and have recently been used for 3D digital holographic recognition. To the best of our knowledge, this is the first use of neural networks for passive 3D integral imaging and recognition of underwater objects.
ABSTRACT
A new (to our knowledge) multiperspective 3D imaging architecture is proposed that uses imagers distributed along a common optical axis. In this axially distributed sensing method, either a single imager is translated along its optical axis or objects are moved parallel to the optical axis of a single imager. The 3D information collection capability of the proposed architecture is analyzed and a computational 3D reconstruction algorithm based on ray back-projection is proposed. It is shown analytically and experimentally that the collection capacity of this architecture is not uniform over the field of view. Experimental results are presented to verify the proposed approach. We believe this is the first report on 3D sensing and imaging with axially distributed sensing.
ABSTRACT
We present a systematic study of Mach-Zehnder silicon optical modulators based on carrier-injection. Detailed comparisons between modeling and measurement results are made with good agreement obtained for both DC and AC characteristics. A figure of merit, static VpiL, as low as 0.24Vmm is achieved. The effect of carrier lifetime variation with doping concentration is explored and found to be important for the modulator characteristics.