No description available.

Splotch (Sp) is a semidominant mouse mutant which maps to the proximal portion of chromosome 1 and is phenotypically expressed as a pleiotropic defect during neurogenesis, resulting in spina bifida, exencephaly and dysgenesis of neural crest cell derivatives. To identify the aberrant gene underlying the defects observed in the Sp mouse mutant we initiated positional cloning strategy. Our preliminary efforts were directed at establishing the boundaries of a deleted chromosomal segment found in the Sp$ sp{r}$ allele, using nine gene probes that were assigned to that region of chromosome 1. Four of these genes, Vil, Des, Inha, and Akp-3, spanning a genetic distance of approximately 15 cM, were found to map within the Sp$ sp{r}$ deletion. In order to further delineate the subchromosomal location of the Sp gene, the proximal segment of mouse chromosome 1 was saturated with microclones isolated from a library of microdissected genomic fragments generated from this region. An additional eight markers were found to map within the confines of the Sp$ sp{r}$ deletion. / During the course of this work a member of the paired box gene family, Pax-3, was described as a candidate for Sp. The striking similarity between the tissue distribution of Pax-3 mRNA in normal developing embryos, and the neural structures affected in Sp mice, together with the chromosome 1 location of Pax-3 led us to examine whether Pax-3 was mutated in three alleles at this locus Sp$ sp{r}$, Sp$ sp{2H}$ and Sp. The entire Pax-3 gene was determined to be deleted in the Sp$ sp{r}$ allele. Analysis of genomic DNA and cDNA clones constructed from RNA isolated from $Sp sp{2H}/Sp sp{2H}$ embryos identified a deletion of 32 nucleotides within the paired type homeobox and is predicted to produce a truncated protein as a result of a newly created termination codon at the deletion breakpoint. The original Sp allele was also characterized and found to contain an A to T transversion at position -2 in the third intron of Pax-3 which abrogates the normal splicing of this intron due to the loss of its natural 3$ sp prime$ splice acceptor. Taken together, these studies indicate that the severe defect in neural tube formation detected in Sp and its allelic variants is linked to the inactivation of the paired box gene Pax-3, and provides direct genetic evidence of a key role for Pax-3 in normal neural development.

Forest growth models are important to the forestry community because they provide means for predicting future yields and exploring different forest management practices. The purpose of this thesis is to develop an individual tree forest growth model applicable for the province of Nova Scotia. The Acadian forest of Nova Scotia is a prime example a mixed species forest which is best modelled with individual tree models. Individual tree models also permit modelling variable-density management regimes, which are important as the Province investigates new silviculture options. Rather than use the conventional regression techniques, our individual tree growth and yield model was developed using neural networks. The growth and yield model was comprised of three different neural networks: a network for each survivability, diameter increment and height increment. In general, the neural network modelling approach fit the provincial data reasonably well. In order to have a model applicable to each species in the Province, species was included as a model input; the models were able to distinguish between species and to perform nearly as well as species-specific models. It was also found that including site and stocking level indicators as model inputs improved the model. Furthermore, it was found that the GIS-based site quality index developed at UNB could be used as a site indicator rather than land capability. Finally, the trained neural networks were used to create a growth and yield model which would be limited to shorter prediction periods and a larger scale.

This thesis presents two experimental microelectronic prototypes for neurophysiological applications. Both systems target diagnostics and treatment of neurological disorders, and they are experimentally validated in vivo by online intracranial EEG recording in freely moving rats. The first prototype is a 56-channel chopper-stabilized low-noise neural recording interface IC with programmable mixed-signal DC cancellation feedback, fabricated in a 0.13μm CMOS process. Each recording channel has a low-noise fully-differential amplifier, and a digital integrator and a delta-sigma DAC in the feedback to cancel DC offsets of up to ±50mV. Chopper stabilization technique is used to reduce the amplifier flicker noise. The recorded signals are digitized by 7 column-parallel SAR ADCs. The second prototype is a compact headset for multi-site neuromonitoring and neurostimulation in rodent brain. A stack of 2 mini-PCBs was designed and experimentally validated. It includes a previously fabricated 0.35μm CMOS recording and stimulation IC, a low-power FPGA, and the IC peripherals.

Multiple-Input Multiple-Output (MIMO) systems have gained an enormous amount of attention as one of the most promising research areas in wireless communications. However, while MIMO systems have been extensively explored over the past decade, few schemes acknowledge the nonlinearity caused by the use of high power amplifiers (HPAs) in the communication chain. When HPAs operate near their saturation points, nonlinear distortions are introduced in the transmitted signals, and the resulting MIMO channel will be nonlinear. The nonlinear distortion is further exacerbated by the fading caused by the propagation channel. The goal of this thesis is: 1) to use neural networks (NNs) to model and identify nonlinear MIMO channels; and 2) to employ the proposed NN model in designing efficient detection techniques for these types of MIMO channels. In the first part of the thesis, we follow a previous work on modeling and identification of nonlinear MIMO channels, where it has been shown that a proposed block-oriented NN scheme allows not only good identification of the overall MIMO input-output transfer function but also good characterization of each component of the system. The proposed scheme employs an ordinary gradient descent based algorithm to update the NN weights during the learning process and it assumes only real-valued inputs. In this thesis, natural gradient (NG) descent is used for training the NN. Moreover, we derive an improved variation of the previously proposed NN scheme to avoid the input type restriction and allow for complex modulated inputs as well. We also investigate the scheme tracking capabilities of time-varying nonlinear MIMO channels. Simulation results show that NG descent learning significantly outperforms the ordinary gradient descent in terms of convergence speed, mean squared error (MSE) performance, and nonlinearity approximation. Moreover, the NG descent based NN provides better tracking capabilities than the previously proposed NN. The second part of the thesis focuses on signal detection. We propose a receiver that employs the neural network channel estimator (NNCE) proposed in part one, and uses the Zero-Forcing Vertical Bell Laboratories Layered Space-Time (ZF V-BLAST) detection algorithm to retrieve the transmitted signals. Computer simulations show that in slow time-varying environments the performance of our receiver is close to the ideal V-BLAST receiver in which the channel is perfectly known. We also present a NN based linearization technique for HPAs, which takes advantage of the channel information provided by the NNCE. Such linearization technique can be used for adaptive data predistortion at the transmitter side or adaptive nonlinear equalization at the receiver side. Simulation results show that, when higher modulation schemes (>16-QAM) are used, the nonlinear distortion caused by the use of HPAs is greatly minimized by our proposed NN predistorter and the performance of the communication system is significantly improved. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-08-08 14:55:50.489

This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models. / Thesis (Master, Mining Engineering) -- Queen's University, 2007-11-28 15:22:17.454 / I would like to thank the financial support provided by the George C. Bateman and J. J. Denny Graduate Fellowship, as well as funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) provided via NSERC grant support to Dr. Daneshmend.

This thesis investigated the abilities of artificial neural networks as rock classifiers in an open-pit hard rock environment using monitoring-while-drilling (MWD) data. Blast hole drilling data has been collected from an open-pit taconite mine. The data was smoothed with respect to depth and filtered for non-drilling data. Preliminary analysis was performed to determine classifier input variables and a method of labelling training data. Results obtained from principal component analysis suggested that the best set of possible classifier input variables was: penetration rate, torque, specific fracture energy, vertical vibration, horizontal vibration, penetration rate deviation and thrust deviation. Specific fracture energy and self-organizing-maps were explored as a means of labelling training data and found to be inadequate. Several backpropagation neural networks were trained and tested with various combinations of input parameters and training sets. Input sets that included all seven parameters achieved the best overall performances. 7-input neural networks that were trained with and tested on the entire data set achieved an average overall performance of 81%. A sensitivity analysis was performed to test the generalization abilities of the neural networks as rock classifiers. The best overall neural network performance on data not included in the training set was 67%. The results indicated that neural networks by themselves are not capable rock classifiers on MWD data in such a hard rock iron ore environment. / Thesis (Master, Mining Engineering) -- Queen's University, 2009-04-23 11:59:07.806

Genetic linkage studies of the spontaneously arising splotch allele, Sp, were conducted to identify closely linked molecular markers as a preliminary step for the isolation of the mutant gene. Restriction fragment length polymorphism and microsatellite size variation analyses were employed to follow the segregation of Sp in relation to eight or ten loci previously assigned to the proximal portion of mouse chromosome 1. Although results from an interspecific ((Sp/+ x Mus spretus)F1-Sp x C57BL/6J) backcross study were inconclusive, a panel of 125 intraspecific ((Sp/+ x CBA/J)F1-Sp x CBA/J) backcross mice positioned the Sp gene 0.8 $ pm$ 0.8 centiMorgans distal to the Vil/Des/Inha loci and detected no recombinant between the mutant allele and the murine paired box gene, Pax-3, positioning this locus within 2.9 centiMorgans of Sp (95% confidence limits). Concurrent research has identified alterations in Pax-3 in several Sp allelic variants; thus, this study provides additional genetic evidence in support of the candidacy of Pax-3 for the Sp locus. Effects of genetic background on the penetrance and expression of Sp were also observed.

We explore the training of deep neural networks to produce vector representations using weakly labelled information in the form of binary similarity labels for pairs of training images. Previous methods such as siamese networks, IMAX and others, have used fixed cost functions such as $L_1$, $L_2$-norms and mutual information to drive the representations of similar images together and different images apart. In this work, we formulate learning as maximizing the likelihood of binary similarity labels for pairs of input images, under a parameterized probabilistic similarity model. We describe and evaluate several forms of the similarity model that account for false positives and false negatives differently. We extract representations of MNIST, AT\&T ORL and COIL-100 images and use them to obtain classification results. We compare these results with state-of-the-art techniques such as deep neural networks and convolutional neural networks. We also study our method from a dimensionality reduction prospective.

We explore the training of deep neural networks to produce vector representations using weakly labelled information in the form of binary similarity labels for pairs of training images. Previous methods such as siamese networks, IMAX and others, have used fixed cost functions such as $L_1$, $L_2$-norms and mutual information to drive the representations of similar images together and different images apart. In this work, we formulate learning as maximizing the likelihood of binary similarity labels for pairs of input images, under a parameterized probabilistic similarity model. We describe and evaluate several forms of the similarity model that account for false positives and false negatives differently. We extract representations of MNIST, AT\&T ORL and COIL-100 images and use them to obtain classification results. We compare these results with state-of-the-art techniques such as deep neural networks and convolutional neural networks. We also study our method from a dimensionality reduction prospective.