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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
71

Training visual pattern recognition : using worked examples to aid schema acquisition

Nagel, Karin Lynne 08 1900 (has links)
No description available.
72

Analyzing the component processes of visual enumeration

Peterson, Scott 12 1900 (has links)
No description available.
73

Is subitizing simply canonical pattern matching

Lunken, Eugene Jonah 12 1900 (has links)
No description available.
74

Pen-Chant : Acoustic Emissions of Handwriting and Drawing

Seniuk, Andrew G. 27 September 2009 (has links)
The sounds generated by a writing instrument ("pen-chant") provide a rich and under-utilized source of information for pattern recognition. We examine the feasibility of recognition of handwritten cursive text, exclusively through an analysis of acoustic emissions. We design and implement a family of recognizers using a template matching approach, with templates and similarity measures derived variously from: smoothed amplitude signal with fixed resolution, discrete sequence of magnitudes obtained from peaks in the smoothed amplitude signal, and ordered tree obtained from a scale space signal representation. Test results are presented for recognition of isolated lowercase cursive characters and for whole words. We also present qualitative results for recognizing gestures such as circling, scratch-out, check-marks, and hatching. Our first set of results, using samples provided by the author, yield recognition rates of over 70% (alphabet) and 90% (26 words), with a confidence of 8%, based solely on acoustic emissions. Our second set of results uses data gathered from nine writers. These results demonstrate that acoustic emissions are a rich source of information, usable - on their own or in conjunction with image-based features - to solve pattern recognition problems. In future work, this approach can be applied to writer identification, handwriting and gesture-based computer input technology, emotion recognition, and temporal analysis of sketches. / Thesis (Master, Computing) -- Queen's University, 2009-09-27 08:56:53.895
75

Trace inference, curvature consistency, and curve detection

Parent, Pierre, 1953- January 1986 (has links)
No description available.
76

Cut scheduling for optimum fabric utilization in apparel production

Coff, Howard Steven 12 1900 (has links)
No description available.
77

Tactile sensing : a case study of the Lord Corporation LTS-300T

Taylor, Jack Rodney 08 1900 (has links)
No description available.
78

A computational model of gas transport in the human lung

Nixon, William January 1977 (has links)
No description available.
79

Cube technique for Nearest Neighbour(s) search

Shehu, Usman Gulumbe January 2002 (has links)
No description available.
80

Localization of Stroke Using Microwave Technology and Inner product Subspace Classifier

Prabahar, Jasila January 2014 (has links)
Stroke or “brain attack” occurs when a blood clot carried by the blood vessels from other part of the body blocks the cerebral artery in the brain or when a blood vessel breaks and interrupts the blood flow to parts of the brain. Depending on which part of the brain is being damaged functional abilities controlled by that region of the brain is lost. By interpreting the patient’s symptoms it is possible to make a coarse estimate of the location of the stroke, e.g. if it is on the left or right hemisphere of the brain. The aim of this study was to evaluate if microwave technology can be used to estimate the location of haemorrhagic stroke. In the first part of the thesis, CT images of the patients for whom the microwave measurement are taken is analysed and are used as a reference to know the location of bleeding in the brain. The X, Y and Z coordinates are calculated from the target slice (where the bleeding is more prominent). Based on the bleeding coordinated the datasets are divided into classes. Under supervised learning method the ISC algorithm is trained to classify stroke in the left and right hemispheres; stroke in the anterior and posterior part of the brain and the stroke in the inferior and superior region of the brain. The second part of the thesis is to analyse the classification result in order to identify the patients that were being misclassified. The classification results to classify the location of bleeding were promising with a high sensitivity and specificity that are indicated by the area under the ROC curve (AUC). AUC of 0.86 was obtained for bleedings in the left and right brain and an AUC of 0.94 was obtained for bleeding in the inferior and superior brain. The main constraint was the small size of the dataset and few availability of dataset with bleeding in the front brain that leads to imbalance between classes. After analysis it was found that bleedings that were close to the skull and few small bleedings that are deep inside the brain are being misclassified. Many factors can be responsible for misclassification like the antenna position, head size, amount of hair etc. The overall results indicate that SDD using ISC algorithm has high potential to distinguish bleedings in different locations. It is expected that the results will be more stable with increased patient dataset for training.

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