On the settling speed of dilute arrays of spheres

A method is developed to calculate the settling speed of dilute arrays of spheres for the three cases of: I, a random array of freely moving particles; II, a random array of rigidly held particles; and III, a cubic array of particles. The basic idea of the technique is to give a formal representation for the solution and then manipulate this representation in a straightforward manner to obtain the result. For infinite arrays of spheres, our results agree with the results previously found by other authors, and the analysis here appears to be simpler. This method is able to obtain more terms in the answer than was possible by Saffman's unified treatment for point particles. Some results for arbitrary two sphere distributions are presented, and an analysis of the wall effect for particles settling in a tube is given. It is expected that the method presented here can be generalized to solve other types of problems.

Various algorithms for optimization and learning in adaptive systems

This thesis discusses various methods for learning and optimization in adaptive systems. Overall, it emphasizes the relationship between optimization, learning, and adaptive systems; and it illustrates the influence of underlying hardware upon the construction of efficient algorithms for learning and optimization. Chapter 1 provides a summary and an overview.

Chapter 2 discusses a method for using feed-forward neural networks to filter the noise out of noise-corrupted signals. The networks use back-propagation learning, but they use it in a way that qualifies as unsupervised learning. The networks adapt based only on the raw input data-there are no external teachers providing information on correct operation during training. The chapter contains an analysis of the learning and develops a simple expression that, based only on the geometry of the network, predicts performance.

Chapter 3 explains a simple model of the piriform cortex, an area in the brain involved in the processing of olfactory information. The model was used to explore the possible effect of acetylcholine on learning and on odor classification. According to the model, the piriform cortex can classify odors better when acetylcholine is present during learning but not present during recall. This is interesting since it suggests that learning and recall might be separate neurochemical modes (corresponding to whether or not acetylcholine is present). When acetylcholine is turned off at all times, even during learning, the model exhibits behavior somewhat similar to Alzheimer's disease, a disease associated with the degeneration of cells that distribute acetylcholine.

Chapters 4, 5, and 6 discuss algorithms appropriate for adaptive systems implemented entirely in analog hardware. The algorithms inject noise into the systems and correlate the noise with the outputs of the systems. This allows them to estimate gradients and to implement noisy versions of gradient descent, without having to calculate gradients explicitly. The methods require only noise generators, adders, multipliers, integrators, and differentiators; and the number of devices needed scales linearly with the number of adjustable parameters in the adaptive systems. With the exception of one global signal, the algorithms require only local information exchange.

Sunyaev-Zel'dovich observations using large-format millimeter arrays

Galaxy clusters are the largest gravitationally bound objects in the observable universe, and they are formed from the largest perturbations of the primordial matter power spectrum. During initial cluster collapse, matter is accelerated to supersonic velocities, and the baryonic component is heated as it passes through accretion shocks. This process stabilizes when the pressure of the bound matter prevents further gravitational collapse. Galaxy clusters are useful cosmological probes, because their formation progressively freezes out at the epoch when dark energy begins to dominate the expansion and energy density of the universe. A diverse set of observables, from radio through X-ray wavelengths, are sourced from galaxy clusters, and this is useful for self-calibration. The distributions of these observables trace a cluster's dark matter halo, which represents more than 80% of the cluster's gravitational potential. One such observable is the Sunyaev-Zel'dovich effect (SZE), which results when the ionized intercluster medium blueshifts the cosmic microwave background via Compton scattering. Great technical advances in the last several decades have made regular observation of the SZE possible. Resolved SZE science, such as is explored in this analysis, has benefitted from the construction of large-format camera arrays consisting of highly sensitive millimeter-wave detectors, such as Bolocam. Bolocam is a submillimeter camera, sensitive to 140 GHz and 268 GHz radiation, located at one of the best observing sites in the world: the Caltech Submillimeter Observatory on Mauna Kea in Hawaii. Bolocam fielded 144 of the original spider web NTD bolometers used in an entire generation of ground-based, balloon-borne, and satellite-borne millimeter wave instrumention. Over approximately six years, our group at Caltech has developed a mature galaxy cluster observational program with Bolocam. This thesis describes the construction of the instrument's full cluster catalog: BOXSZ. Using this catalog, I have scaled the Bolocam SZE measurements with X-ray mass approximations in an effort to characterize the SZE signal as a viable mass probe for cosmology. This work has confirmed the SZE to be a low-scatter tracer of cluster mass. The analysis has also revealed how sensitive the SZE-mass scaling is to small biases in the adopted mass approximation. Future Bolocam analysis efforts are set on resolving these discrepancies by approximating cluster mass jointly with different observational probes.

Magnetic alignment of high-aspect ratio microwires into vertical arrays

Fundamental studies of magnetic alignment of highly anisotropic mesostructures can enable the clean-room-free fabrication of flexible, array-based solar and electronic devices, in which preferential orientation of nano- or microwire-type objects is desired. In this study, ensembles of 100 micron long Si microwires with ferromagnetic Ni and Co coatings are oriented vertically in the presence of magnetic fields. The degree of vertical alignment and threshold field strength depend on geometric factors, such as microwire length and ferromagnetic coating thickness, as well as interfacial interactions, which are modulated by varying solvent and substrate surface chemistry. Microwire ensembles with vertical alignment over 97% within 10 degrees of normal, as measured by X-ray diffraction, are achieved over square cm scale areas and set into flexible polymer films. A force balance model has been developed as a predictive tool for magnetic alignment, incorporating magnetic torque and empirically derived surface adhesion parameters. As supported by these calculations, microwires are shown to detach from the surface and align vertically in the presence of magnetic fields on the order of 100 gauss. Microwires aligned in this manner are set into a polydimethylsiloxane film where they retain their vertical alignment after the field has been removed and can subsequently be used as a flexible solar absorber layer. Finally, these microwires arrays can be protected for use in electrochemical cells by the conformal deposition of a graphene layer.