3-D Scene Reconstruction for Passive Ranging Using Depth from Defocus and Deep Learning

Indiana University-Purdue University Indianapolis (IUPUI)Depth estimation is increasingly becoming more important in computer vision. The requirement for autonomous systems to gauge their surroundings is of the utmost importance in order to avoid obstacles, preventing damage to itself and/or other systems or people. Depth measuring/estimation systems that use multiple cameras from multiple views can be expensive and extremely complex. And as these autonomous systems decrease in size and available power, the supporting sensors required to estimate depth must also shrink in size and power consumption. This research will concentrate on a single passive method known as Depth from Defocus (DfD), which uses an in-focus and out-of-focus image to infer the depth of objects in a scene. The major contribution of this research is the introduction of a new Deep Learning (DL) architecture to process the the in-focus and out-of-focus images to produce a depth map for the scene improving both speed and performance over a range of lighting conditions. Compared to the previous state-of-the-art multi-label graph cuts algorithms applied to the synthetically blurred dataset the DfD-Net produced a 34.30% improvement in the average Normalized Root Mean Square Error (NRMSE). Similarly the DfD-Net architecture produced a 76.69% improvement in the average Normalized Mean Absolute Error (NMAE). Only the Structural Similarity Index (SSIM) had a small average decrease of 2.68% when compared to the graph cuts algorithm. This slight reduction in the SSIM value is a result of the SSIM metric penalizing images that appear to be noisy. In some instances the DfD-Net output is mottled, which is interpreted as noise by the SSIM metric. This research introduces two methods of deep learning architecture optimization. The first method employs the use of a variant of the Particle Swarm Optimization (PSO) algorithm to improve the performance of the DfD-Net architecture. The PSO algorithm was able to find a combination of the number of convolutional filters, the size of the filters, the activation layers used, the use of a batch normalization layer between filters and the size of the input image used during training to produce a network architecture that resulted in an average NRMSE that was approximately 6.25% better than the baseline DfD-Net average NRMSE. This optimized architecture also resulted in an average NMAE that was 5.25% better than the baseline DfD-Net average NMAE. Only the SSIM metric did not see a gain in performance, dropping by 0.26% when compared to the baseline DfD-Net average SSIM value. The second method illustrates the use of a Self Organizing Map clustering method to reduce the number convolutional filters in the DfD-Net to reduce the overall run time of the architecture while still retaining the network performance exhibited prior to the reduction. This method produces a reduced DfD-Net architecture that has a run time decrease of between 14.91% and 44.85% depending on the hardware architecture that is running the network. The final reduced DfD-Net resulted in a network architecture that had an overall decrease in the average NRMSE value of approximately 3.4% when compared to the baseline, unaltered DfD-Net, mean NRMSE value. The NMAE and the SSIM results for the reduced architecture were 0.65% and 0.13% below the baseline results respectively. This illustrates that reducing the network architecture complexity does not necessarily reduce the reduction in performance. Finally, this research introduced a new, real world dataset that was captured using a camera and a voltage controlled microfluidic lens to capture the visual data and a 2-D scanning LIDAR to capture the ground truth data. The visual data consists of images captured at seven different exposure times and 17 discrete voltage steps per exposure time. The objects in this dataset were divided into four repeating scene patterns in which the same surfaces were used. These scenes were located between 1.5 and 2.5 meters from the camera and LIDAR. This was done so any of the deep learning algorithms tested would see the same texture at multiple depths and multiple blurs. The DfD-Net architecture was employed in two separate tests using the real world dataset. The first test was the synthetic blurring of the real world dataset and assessing the performance of the DfD-Net trained on the Middlebury dataset. The results of the real world dataset for the scenes that were between 1.5 and 2.2 meters from the camera the DfD-Net trained on the Middlebury dataset produced an average NRMSE, NMAE and SSIM value that exceeded the test results of the DfD-Net tested on the Middlebury test set. The second test conducted was the training and testing solely on the real world dataset. Analysis of the camera and lens behavior led to an optimal lens voltage step configuration of 141 and 129. Using this configuration, training the DfD-Net resulted in an average NRMSE, NMAE and SSIM of 0.0660, 0.0517 and 0.8028 with a standard deviation of 0.0173, 0.0186 and 0.0641 respectively

Effects of rarefaction on cavity flow in the slip regime

The Navier-Stokes-Fourier equations, with boundary conditions that account for the effects of velocity-slip and temperature-jump, are compared to the direct simulation Monte Carlo method for the case of a lid-driven micro-cavity. Results are presented for Knudsen numbers within the slip-flow regime where the onset of nonequilibrium effects are usually observed. Good agreement is found in predicting the general features of the velocity field and the recirculating flow. However, although the steady-state pressure distributions along the walls of the driven cavity are generally in good agreement with the Monte Carlo data, there is some indication that the results are starting to show noticeable differences, particularly at the separation and reattachment points. The modified Navier-Stokes-Fourier equations consistently overpredict the maximum and minimum pressure values throughout the slip regime. This highlights the need for alternative boundary formulations or modeling techniques that can provide accurate and computationally economic solutions over a wider range of Knudsen numbers

Micro-scale cavities in the slip - and transition - flow regimes

Differences between Navier-Stokes-Fourier (NSF) slip/jump solutions and direct simulation Monte-Carlo (DSMC) computations are highlighted for a micro lid-driven cavity problem. The results indicate a need for better modelling techniques which at the same time retain low computational cost of NSF models. We also highlight the fact thatmany micro-flows that have been considered are simple planar flows and typical classification systems are defined on such flows. We show that for complex flows, such as thedriven cavity, non-equilibrium effects are more appreciable and their onset occurs at lower Knudsen numbers than expected

Study of localization in the quantum sawtooth map emulated on a quantum information processor

Quantum computers will be unique tools for understanding complex quantum systems. We report an experimental implementation of a sensitive, quantum coherence-dependent localization phenomenon on a quantum information processor (QIP). The localization effect was studied by emulating the dynamics of the quantum sawtooth map in the perturbative regime on a three-qubit QIP. Our results show that the width of the probability distribution in momentum space remained essentially unchanged with successive iterations of the sawtooth map, a result that is consistent with localization. The height of the peak relative to the baseline of the probability distribution did change, a result that is consistent with our QIP being an ensemble of quantum systems with a distribution of errors over the ensemble. We further show that the previously measured distributions of control errors correctly account for the observed changes in the probability distribution.Comment: 20 pages, 9 figure

Simulation of Micro-Electronic FlowFET Systems

A microelectronic fluidic system has been investigated by modeling and 3D simulation of fluid flow controlled by an applied gate voltage. The simulations have helped to characterize a novel FlowFET (a fluidic Field Effect Transistor) device under fault-free conditions. The FlowFET operates by applying a voltage field from a gate electrode in the insulated side wall of a microchannel to modulate the ␣-potential at the shear plane [1]. The change in ␣-potential can be used to control both the magnitude and direction of the electroosmotic flow in the microchannel

Simulation of the head-disk interface gap using a hybrid multi-scale method

We present a hybrid multi-scale method that provides a capability to capture the disparate scales associated with modelling flow in micro- and nano-devices. Our model extends the applicability of an internal-flow multi-scale method by providing a framework to couple the internal (small scale) flow regions to the external (large scale) flow regions. We demonstrate the application of both the original methodology and the new hybrid approach to model the flow field in the vicinity of the head-disk interface gap of a hard disk drive enclosure. The internal flow regions within the gap are modelled by an extended internal-flow multi-scale method that utilises a finite-difference scheme for non-uniform grids. Our proposed hybrid multi-scale method is then employed to couple the internal micro-flow region to the flow external to the gap, to capture entrance/exit effects. We also demonstrate the successful application of the method in capturing other localised phenomena (e.g. those due to localised wall heating)

1976 Base Data for the Dairy Market Policy Simulator

A.E. Res. 80-2

Improving TELEMAC system pre-processing and IO stages

Water Qualit