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Experimental Validation Of Phase Using Nomarski Microscopy With An Extended Fried Algorithm, Scott A. Prahl, Amanda Dayton, Kyle Juedes, Erik J. Sánchez, Rafael Paez López, Donald D. Duncan
Experimental Validation Of Phase Using Nomarski Microscopy With An Extended Fried Algorithm, Scott A. Prahl, Amanda Dayton, Kyle Juedes, Erik J. Sánchez, Rafael Paez López, Donald D. Duncan
Electrical and Computer Engineering Faculty Publications and Presentations
Reconstruction of an image (or shape or wavefront) from measurements of the derivatives of the image in two orthogonal directions is a common problem. We demonstrate how a particular reconstructor, commonly referred to as the Fried algorithm, can be used with megapixel derivative images to recover the original image. Large datasets are handled by breaking the derivative images into smaller tiles, applying the Fried algorithm and stitching the tiles back together. The performance of the algorithm is demonstrated using differential interference contrast microscopy on a known test object.
Aspect-Dependent Radiated Noise Analysis Of An Underway Autonomous Underwater Vehicle, John Gebbie, Martin Siderius, John S. Allen Iii
Aspect-Dependent Radiated Noise Analysis Of An Underway Autonomous Underwater Vehicle, John Gebbie, Martin Siderius, John S. Allen Iii
Electrical and Computer Engineering Faculty Publications and Presentations
This paper presents an analysis of the acoustic emissions emitted by an underway REMUS-100 autonomous underwater vehicle(AUV) that were obtained near Honolulu Harbor, HI using a fixed, bottom-mounted horizontal line array (HLA). Spectral analysis,beamforming, and cross-correlation facilitate identification of independent sources of noise originating from the AUV. Fusion of navigational records from the AUV with acoustic data from the HLA allows for an aspect-dependent presentation of calculated source levels of the strongest propulsion tone.
Bayesian Geoacoustic Inversion Using Wind-Driven Ambient Noise, Jorge E. Quijano, Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, Martin Siderius, Chris H. Harrison
Bayesian Geoacoustic Inversion Using Wind-Driven Ambient Noise, Jorge E. Quijano, Stan E. Dosso, Jan Dettmer, Lisa M. Zurk, Martin Siderius, Chris H. Harrison
Electrical and Computer Engineering Faculty Publications and Presentations
This paper applies Bayesian inversion to bottom-loss data derived from wind-driven ambient noisemeasurements from a vertical line array to quantify the information content constraining seabed geoacoustic parameters. The inversion utilizes a previously proposed ray-based representation of the ambient noise field as a forward model for fast computations of bottom loss data for a layered seabed. This model considers the effect of the array’s finite aperture in the estimation of bottom loss and is extended to include the wind speed as the driving mechanism for the ambient noise field. The strength of this field relative to other unwanted noise mechanisms defines …
Power Characteristics Of Homogeneously Broadened Index-Antiguided Waveguide Lasers, Chaofan Wang, Tsing-Hua Her, Lee W. Casperson
Power Characteristics Of Homogeneously Broadened Index-Antiguided Waveguide Lasers, Chaofan Wang, Tsing-Hua Her, Lee W. Casperson
Electrical and Computer Engineering Faculty Publications and Presentations
A model is reported that describes a bidirectional homogeneously broadened index-antiguided (IAG) slab laser having arbitrary single-pass gain and distributed losses. Maximum extraction efficiency and corresponding optimum output coupling are determined for various values of unsaturated gain and loss per pass. A method is proposed to determine the intrinsic laser parameters from output power measurements.
Emergent Criticality Through Adaptive Information Processing In Boolean Networks, Alireza Goudarzi, Christof Teuscher, Natali Gulbahce, Thimo Rohlf
Emergent Criticality Through Adaptive Information Processing In Boolean Networks, Alireza Goudarzi, Christof Teuscher, Natali Gulbahce, Thimo Rohlf
Electrical and Computer Engineering Faculty Publications and Presentations
We study information processing in populations of Boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity K_{c}=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both …