Master of Science

thesisDespite their many uses, fine clay particles such as kaolinite are a nuisance in management of tailings in various industries such as the oil sands and phosphate processing industry. The effective flocculation, sedimentation, and consolidation of these fine particles are a major challenge. In industries, polymers are added to tailings suspension to facilitate formation and eventual sedimentation of flocs. The structure of floc and the water entrapped within the floc determine floc behavior and settling characteristics. The quantification of water entrapped within the kaolinite flocs has not been reported before. The information on kaolinite floc size and shape is also limited due to the challenges in experimental procedures for these delicate structures. In this thesis research, operating conditions for kaolinite flocculation were determined and a suitable polymer was chosen by settling experiments. Further investigation of the floc formed was done in suspended state as well as in sedimented state. The flocs were analyzed for their size, shape, water content, and microstructure. A pool of analytical techniques like the Particle Vision & Measurement (PVM), Dynamic Image Analysis (DIA), Scanning Electron Microscopy (SEM), High Resolution X-ray Microtomography (HRXMT), and image processing software like Fiji, Medical Image Processing Analysis & Visualization (MIPAV), and Drishti were used. The analysis of suspended flocs by PVM and DIA revealed a mean floc size of about 225 µm for high molecular weight, 5% anionic polyacrylamide-induced flocs. The low molecular weight, 70% cationic polymer-induced flocs were found to be smaller in size (145 µm). DIA was used to analyze the flocs at different solid concentration. It was found that the increase in solid concentration leads to increase in floc size. Floc circularity was also analyzed by using both these methods. Most flocs were irregular in shape with circularity ranging between 0.2-0.3. However, the circularity results from both these methods do not agree well due to the difference in methods of detection and different definitions used for circularity/sphericity. Major contribution of this thesis work includes development of a new technique for water content and size analysis of sedimented kaolinite flocs. The sediment bed was segmented into about 13 thousand individual flocs and each floc was analyzed for its size and water content. The results suggest a normal distribution of water content for these flocs, with mean water content of 53.9% and standard deviation of 11.8%. About 98% of the flocs have water content in the range 30-80%. The size analysis revealed that about 90% of the flocs are less than 1.5 mm in size. The water content was found to decrease with increase in size of the floc. The flocs were found to be fairly irregular, with sphericity values around 0.1. The floc shape analysis was also done but limited to 10 flocs. In addition to macroscopic analysis of individual flocs, flocs were also analyzed for their microstructure. Visualization of floc microstructure and polymer chain was done with the help of SEM. Microstructures of up to 10 µm in size were revealed along with the web formed by polymer chain

Neural Plausibility of Bayesian Inference

Behavioral studies have shown that humans account for uncertainty in a way that is nearly optimal in the Bayesian sense. Probabilistic models based on Bayes' theorem have been widely used for understanding human cognition, and have been applied to behaviors that range from perception and motor control to higher level reasoning and inference. However, whether the brain actually uses Bayesian reasoning or such reasoning is just an approximate description of human behavior is an open question. In this thesis, I aim to address this question by exploring the neural plausibility of Bayesian inference. I first present a spiking neural model for learning priors (beliefs) from experiences of the natural world. Through this model, I address the question of how humans might be learning the priors needed for the inferences they make in their daily lives. I propose neural mechanisms for continuous learning and updating of priors - cognitive processes that are critical for many aspects of higher-level cognition. Next, I propose neural mechanisms for performing Bayesian inference by combining the learned prior with the likelihood that is based on the observed information. Through the process of building these models, I address the issue of representing probability distributions in neural populations by deploying an efficient neural coding scheme. I show how these representations can be used in meaningful ways to learn beliefs (priors) over time and to perform inference using those beliefs. The final model is generalizable to various psychological tasks, and I show that it converges to the near optimal priors with very few training examples. The model is validated using a life span inference task, and the results from the model match human performance on this task better than an ideal Bayesian model due to the use of neuron tuning curves. This provides an initial step towards better understanding how Bayesian computations may be implemented in a biologically plausible neural network. Finally, I discuss the limitations and suggest future work on both theoretical and experimental fronts

The Swiss Cheese Mutant Causes Glial Hyperwrapping and Brain Degeneration in Drosophila

Swiss cheese (sws) mutant flies develop normally during larval life but show age-dependent neurodegeneration in the pupa and adult and have reduced life span. In late pupae, glial processes form abnormal, multilayered wrappings around neurons and axons. Degeneration first becomes evident in young flies as apoptosis in single scattered cells in the CNS, but later it becomes severe and widespread. In the adult, the number of glial wrappings increases with age. The sws gene is expressed in neurons in the brain cortex. The conceptual 1425 amino acid protein shows two domains with homology to the regulatory subunits of protein kinase A and to conceptual proteins of yet unknown function in yeast, worm, and human. Sequencing of two sws alleles shows amino acid substitutions in these two conserved domains. It is suggested that the novel SWS protein plays a role in a signaling mechanism between neurons and glia that regulates glial wrapping during development of the adult brain

The swiss cheese mutant causes glial hyperwrapping and brain degeneration in Drosophila

Swiss cheese (sws) mutant flies develop normally during larval life but show age-dependent neurodegeneration in the pupa and adult and have reduced life span. In late pupae, glial processes form abnormal, multilayered wrappings around neurons and axons. Degeneration first becomes evident in young flies as apoptosis in single scattered cells in the CNS, but later it becomes severe and widespread. In the adult, the number of glial wrappings increases with age. The sws gene is expressed in neurons in the brain cortex. The conceptual 1425 amino acid protein shows two domains with homology to the regulatory subunits of protein kinase A and to conceptual proteins of yet unknown function in yeast, worm, and human. Sequencing of two sws alleles shows amino acid substitutions in these two conserved domains. It is suggested that the novel SWS protein plays a role in a signaling mechanism between neurons and glia that regulates glial wrapping during development of the adult brain

Power Rotational Interleaver on an Idma System

In this paper we are proposing an interleaver design i.e. power rotational interleaver. The basic purpose of this design is to reduce the bandwidth occupied by the interleaver. This approach provides an efficient result for multiple users. The complexity of this design is same as that of master random interleaver while the bandwidth requirement is reduced up to a great extent. On the basis of simulation results it is concluded that the performance of power rotational interleaver is as good as that is of random interleaver. Keywords: Master random interleaver, a posteriori probability, tree based interleaver