subject:(pattern recognition)

University of KwaZulu-Natal

1. Schuld, Maria. Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data.

Degree: 2017, University of KwaZulu-Natal

URL: http://hdl.handle.net/10413/15748

► Humans are experts at recognising patterns in past experience and applying them to new tasks. For example, after seeing pictures of a face we can… (more)

▼ Humans are experts at recognising patterns in past experience and applying them to new tasks. For example, after seeing pictures of a face we can usually tell if another image contains the same person or not. Machine learning is a research discipline at the intersection of computer science, statistics and mathematics that investigates how pattern recognition can be performed by machines and for large amounts of data. Since a few years machine learning has come into the focus of quantum computing in which information processing based on the laws of quantum theory is explored. Although large scale quantum computers are still in the first stages of development, their theoretical description is well-understood and can be used to formulate `quantum software' or `quantum algorithms' for pattern recognition. Researchers can therefore analyse the impact quantum computers may have on intelligent data mining. This approach is part of the emerging research discipline of quantum machine learning that harvests synergies between quantum computing and machine learning. The research objective of this thesis is to understand how we can solve a slightly more specific problem called supervised pattern recognition based on the language that has been developed for universal quantum computers. The contribution it makes is twofold: First, it presents a methodology that understands quantum machine learning as the combination of data encoding into quantum systems and quantum optimisation. Second, it proposes several quantum algorithms for supervised pattern recognition. These include algorithms for convex and non-convex optimisation, implementations of distance-based methods through quantum interference, and the preparation of quantum states from which solutions can be derived via sampling. Amongst the machine learning methods considered are least-squares linear regression, gradient descent and Newton's method, k-nearest neighbour, neural networks as well as ensemble methods. Together with the growing body of literature, this thesis demonstrates that quantum computing offers a number of interesting tools for machine learning applications, and has the potential to create new models of how to learn from data. Advisors/Committee Members: Petruccione, Francesco. (advisor), Sinayskiy, Llya. (advisor).

Subjects/Keywords: Pattern.; Recognition.

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APA (6^th Edition):

Schuld, M. (2017). Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data. (Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/15748

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Schuld, Maria. “Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data.” 2017. Thesis, University of KwaZulu-Natal. Accessed February 27, 2021. http://hdl.handle.net/10413/15748.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Schuld, Maria. “Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data.” 2017. Web. 27 Feb 2021.

Vancouver:

Schuld M. Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10413/15748.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Schuld M. Quantum machine learning for supervised pattern recognition.: How quantum computers learn from data. [Thesis]. University of KwaZulu-Natal; 2017. Available from: http://hdl.handle.net/10413/15748

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

De Montfort University

2. Al Rifaee, Mustafa Moh'd Husien. Unconstrained iris recognition.

Degree: PhD, 2014, De Montfort University

URL: http://hdl.handle.net/2086/10949

► This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the… (more)

▼ This research focuses on iris recognition, the most accurate form of biometric identification. The robustness of iris recognition comes from the unique characteristics of the human, and the permanency of the iris texture as it is stable over human life, and the environmental effects cannot easily alter its shape. In most iris recognition systems, ideal image acquisition conditions are assumed. These conditions include a near infrared (NIR) light source to reveal the clear iris texture as well as look and stare constraints and close distance from the capturing device. However, the recognition accuracy of the-state-of-the-art systems decreases significantly when these constraints are relaxed. Recent advances have proposed different methods to process iris images captured in unconstrained environments. While these methods improve the accuracy of the original iris recognition system, they still have segmentation and feature selection problems, which results in high FRR (False Rejection Rate) and FAR (False Acceptance Rate) or in recognition failure. In the first part of this thesis, a novel segmentation algorithm for detecting the limbus and pupillary boundaries of human iris images with a quality assessment process is proposed. The algorithm first searches over the HSV colour space to detect the local maxima sclera region as it is the most easily distinguishable part of the human eye. The parameters from this stage are then used for eye area detection, upper/lower eyelid isolation and for rotation angle correction. The second step is the iris image quality assessment process, as the iris images captured under unconstrained conditions have heterogeneous characteristics. In addition, the probability of getting a mis-segmented sclera portion around the outer ring of the iris is very high, especially in the presence of reflection caused by a visible wavelength light source. Therefore, quality assessment procedures are applied for the classification of images from the first step into seven different categories based on the average of their RGB colour intensity. An appropriate filter is applied based on the detected quality. In the third step, a binarization process is applied to the detected eye portion from the first step for detecting the iris outer ring based on a threshold value defined on the basis of image quality from the second step. Finally, for the pupil area segmentation, the method searches over the HSV colour space for local minima pixels, as the pupil contains the darkest pixels in the human eye. In the second part, a novel discriminating feature extraction and selection based on the Curvelet transform are introduced. Most of the state-of-the-art iris recognition systems use the textural features extracted from the iris images. While these fine tiny features are very robust when extracted from high resolution clear images captured at very close distances, they show major weaknesses when extracted from degraded images captured over long distances. The use of the Curvelet transform to extract 2D geometrical…

Subjects/Keywords: 600; Pattern Recognition

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APA (6^th Edition):

Al Rifaee, M. M. H. (2014). Unconstrained iris recognition. (Doctoral Dissertation). De Montfort University. Retrieved from http://hdl.handle.net/2086/10949

Chicago Manual of Style (16^th Edition):

Al Rifaee, Mustafa Moh'd Husien. “Unconstrained iris recognition.” 2014. Doctoral Dissertation, De Montfort University. Accessed February 27, 2021. http://hdl.handle.net/2086/10949.

MLA Handbook (7^th Edition):

Al Rifaee, Mustafa Moh'd Husien. “Unconstrained iris recognition.” 2014. Web. 27 Feb 2021.

Vancouver:

Al Rifaee MMH. Unconstrained iris recognition. [Internet] [Doctoral dissertation]. De Montfort University; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2086/10949.

Council of Science Editors:

Al Rifaee MMH. Unconstrained iris recognition. [Doctoral Dissertation]. De Montfort University; 2014. Available from: http://hdl.handle.net/2086/10949

NSYSU

3. Chen, Po-ju. A Bometric Verification method based on Knee Accerlation Signal.

Degree: Master, Mechanical and Electro-Mechanical Engineering, 2008, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528

► Abstract With the rapid progress of the MEMs process, the cost and the size of accelerometers are reducing rapidly. As a result, accelerometers have found… (more)

Subjects/Keywords: acceleration; VQ; hypersphere; pattern recognition

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APA (6^th Edition):

Chen, P. (2008). A Bometric Verification method based on Knee Accerlation Signal. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Chen, Po-ju. “A Bometric Verification method based on Knee Accerlation Signal.” 2008. Thesis, NSYSU. Accessed February 27, 2021. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Chen, Po-ju. “A Bometric Verification method based on Knee Accerlation Signal.” 2008. Web. 27 Feb 2021.

Vancouver:

Chen P. A Bometric Verification method based on Knee Accerlation Signal. [Internet] [Thesis]. NSYSU; 2008. [cited 2021 Feb 27]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chen P. A Bometric Verification method based on Knee Accerlation Signal. [Thesis]. NSYSU; 2008. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0721108-160528

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

4. Karunakara, K. Some contributions to the study of pattern recognition;.

Degree: Computer science and engineering, 2014, Kuvempu University

URL: http://shodhganga.inflibnet.ac.in/handle/10603/25143

Abstract available newline

Advisors/Committee Members: Mallikarjunaswamy, B P.

Subjects/Keywords: Pattern recognition

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APA (6^th Edition):

Karunakara, K. (2014). Some contributions to the study of pattern recognition;. (Thesis). Kuvempu University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/25143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Karunakara, K. “Some contributions to the study of pattern recognition;.” 2014. Thesis, Kuvempu University. Accessed February 27, 2021. http://shodhganga.inflibnet.ac.in/handle/10603/25143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Karunakara, K. “Some contributions to the study of pattern recognition;.” 2014. Web. 27 Feb 2021.

Vancouver:

Karunakara K. Some contributions to the study of pattern recognition;. [Internet] [Thesis]. Kuvempu University; 2014. [cited 2021 Feb 27]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/25143.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Karunakara K. Some contributions to the study of pattern recognition;. [Thesis]. Kuvempu University; 2014. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/25143

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Penn State University

5. Lidstone, Stephanie Ann. Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques .

Degree: 2012, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/13827

► Complex manufacturing systems can be challenging to analyze and improve. The intricate nature of these systems makes it difficult to anticipate how modifications in one… (more)

▼ Complex manufacturing systems can be challenging to analyze and improve. The intricate nature of these systems makes it difficult to anticipate how modifications in one part of the system will impact the system as a whole. Simulation modeling is an effective tool for representing the current state of a production system and experimenting with various changes within the production system. The main benefit of simulation modeling is that it allows for the testing of various changes in a virtual environment, thus making it a relatively low-risk and cost-effective method for testing potential modifications to large-scale production systems. While virtual experimentation has many benefits, one major drawback is that it can be challenging to analyze the simulation output and identify meaningful results. The proceeding work focuses on methods for visualizing simulation output and analyzing simulation output to uncover significant patterns. A simulation model representing a complex, large-scale manufacturing facility was used to experiment with modifying the number of operators per work center and the number of daily shifts per work center within the production system. The manufacturing facility consists of four main production areas which can be broken down into 56 individual work centers. Each work center contains one or more machines which are operated by one or more operators. The manufacturing facility produces several distinct products which have all been designed on a single platform. The single product platform enables the manufacturer to produce several product variants using the same production equipment. While all products get routed through nearly-identical work centers, each product will be processed slightly differently at each work center. Thus the raw components used in each work center, and the time to complete the manufacturing steps in each work center will vary slightly from product to product. One of the manufacturer’s primary goals is to ensure that that the facility stays on schedule and is able to ship their final products to their customers on time. In order to keep the facility on schedule, the manufacturer has the ability to add capacity within their facility, either in terms of increasing the number of operators in each work center or increasing the number of shifts that each work center operates. The simulation model representing this real-world manufacturing facility was used to experiment with modifying the facility capacity to uncover its impact in meeting the production schedule. The output from these experiments was then analyzed, and visualizations of relevant data were developed. Historically most simulation analysis has centered around statistical analysis of: i) each workstation’s performance measure and ii) aggregate line measures. In this work the focus is on finding patterns among the performance measures of individual work centers and using these patterns for predictive purposes. Apriori algorithm for uncovering patterns is one of the most used data mining… Advisors/Committee Members: Dr Soundar Kumara, Thesis Advisor/Co-Advisor.

Subjects/Keywords: data visualization; manufacturing; pattern recognition

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APA (6^th Edition):

Lidstone, S. A. (2012). Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/13827

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lidstone, Stephanie Ann. “Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques .” 2012. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/13827.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lidstone, Stephanie Ann. “Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques .” 2012. Web. 27 Feb 2021.

Vancouver:

Lidstone SA. Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques . [Internet] [Thesis]. Penn State University; 2012. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/13827.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lidstone SA. Modeling and Analysis of Complex Manufacturing Systems – Application of Data Visualization and Pattern Recognition Techniques . [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/13827

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Saskatchewan

6. Yu, Zexi 1989-. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images.

Degree: 2016, University of Saskatchewan

URL: http://hdl.handle.net/10388/7624

► Positron emission tomography (PET)-Computed tomography (CT) plays an important role in cancer management. As a multi-modal imaging technique it provides both functional and anatomical information… (more)

Subjects/Keywords: Pattern Recognition; Medical Image Processing

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APA (6^th Edition):

Yu, Z. 1. (2016). Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. (Thesis). University of Saskatchewan. Retrieved from http://hdl.handle.net/10388/7624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Yu, Zexi 1989-. “Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images.” 2016. Thesis, University of Saskatchewan. Accessed February 27, 2021. http://hdl.handle.net/10388/7624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Yu, Zexi 1989-. “Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images.” 2016. Web. 27 Feb 2021.

Vancouver:

Yu Z1. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. [Internet] [Thesis]. University of Saskatchewan; 2016. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10388/7624.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yu Z1. Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images. [Thesis]. University of Saskatchewan; 2016. Available from: http://hdl.handle.net/10388/7624

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Newcastle

7. Miller, Peter. Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy.

Degree: 2009, University of Newcastle

URL: http://hdl.handle.net/1959.13/44629

►

Masters Research - Master of Medical Science

Pattern recognition is a non-analytical clinical reasoning process which has been reported in the medical and allied health… (more)

▼

Masters Research - Master of Medical Science

Pattern recognition is a non-analytical clinical reasoning process which has been reported in the medical and allied health literature for some time. At a time when clinical problem solving was largely considered to consist of the analytical process of hypothetico-deductive reasoning, pattern recognition was introduced in the literature with observations of greater efficiency and accuracy. The research that followed these apparent opposing models of clinical reasoning resulted in significant growth in the understanding of problem solving in healthcare. On commencing this thesis the knowledge surrounding pattern recognition in physiotherapy was insufficient for its inclusion in educational design. Consequently the aims of the study described in this thesis were to clearly identify pattern recognition using high fidelity case methods and observe its relationship with accuracy and efficiency. The study utilised a single case study with multiple participants. A real clinical case with a diagnosis of high grade lumbar spine spondylolisthesis was simulated using a trained actor. This provided a high fidelity case study method allowing the observation of more realistic problem solving practices as compared with the common low fidelity paper case approach. Two participant groups were included in the study to investigate the common belief that pattern recognition is an experience based reasoning process. The expert group comprised ten titled musculoskeletal physiotherapists with a minimum of ten years overall clinical experience and greater than two years experience following the completion of postgraduate study. The novice group included nine physiotherapists in their first year of clinical practice following completion of an undergraduate degree. Qualitative data collection methods included observation of the participant taking a patient history of the simulated client and a stimulated retrospective recall interview with the participant. The mixed method analysis used in the study provided methodological triangulation of the results and supported the presence of pattern recognition in musculoskeletal physiotherapy. The quantitative research findings indicated that pattern recognition was significantly more likely to produce an accurate diagnostic outcome than analytical reasoning strategies during a physiotherapy history. However its use was not a guarantee of success with only three of the four experts using pattern recognition identifying the correct diagnosis. Although four experts utilised pattern recognition as compared with only one novice, no significant overall differences were found in the use of pattern recognition between the expert and novice participant groups. The findings relating to time data found that expert participants took longer to conduct the client history than novices. Similarly those participants identified using pattern recognition also required more time which seemingly contradicts the view of pattern recognition being an efficient clinical…

Advisors/Committee Members: University of Newcastle. Faculty of Health, School of Health Sciences.

Subjects/Keywords: pattern recognition; clinical reasoning; physiotherapy

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Miller, P. (2009). Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy. (Thesis). University of Newcastle. Retrieved from http://hdl.handle.net/1959.13/44629

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Miller, Peter. “Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy.” 2009. Thesis, University of Newcastle. Accessed February 27, 2021. http://hdl.handle.net/1959.13/44629.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Miller, Peter. “Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy.” 2009. Web. 27 Feb 2021.

Vancouver:

Miller P. Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy. [Internet] [Thesis]. University of Newcastle; 2009. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1959.13/44629.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Miller P. Pattern recognition is a clinical reasoning process in musculoskeletal physiotherapy. [Thesis]. University of Newcastle; 2009. Available from: http://hdl.handle.net/1959.13/44629

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Windsor

8. Talaei, Amir Javid. Pattern Recognition Using Spiking Neural Networks.

Degree: MA, Electrical and Computer Engineering, 2020, University of Windsor

URL: https://scholar.uwindsor.ca/etd/8402

► Deep learning believed to be a promising approach for solving specific problems in the field of artificial intelligence whenever a large amount of data and… (more)

Subjects/Keywords: Pattern Recognition; Spiking Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Talaei, A. J. (2020). Pattern Recognition Using Spiking Neural Networks. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/8402

Chicago Manual of Style (16^th Edition):

Talaei, Amir Javid. “Pattern Recognition Using Spiking Neural Networks.” 2020. Masters Thesis, University of Windsor. Accessed February 27, 2021. https://scholar.uwindsor.ca/etd/8402.

MLA Handbook (7^th Edition):

Talaei, Amir Javid. “Pattern Recognition Using Spiking Neural Networks.” 2020. Web. 27 Feb 2021.

Vancouver:

Talaei AJ. Pattern Recognition Using Spiking Neural Networks. [Internet] [Masters thesis]. University of Windsor; 2020. [cited 2021 Feb 27]. Available from: https://scholar.uwindsor.ca/etd/8402.

Council of Science Editors:

Talaei AJ. Pattern Recognition Using Spiking Neural Networks. [Masters Thesis]. University of Windsor; 2020. Available from: https://scholar.uwindsor.ca/etd/8402

University of Cape Town

9. Goss, Ryan Gavin. APIC: A method for automated pattern identification and classification.

Degree: Image, Computer Science, 2017, University of Cape Town

URL: http://hdl.handle.net/11427/27025

► Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from… (more)

▼ Machine Learning (ML) is a transformative technology at the forefront of many modern research endeavours. The technology is generating a tremendous amount of attention from researchers and practitioners, providing new approaches to solving complex classification and regression tasks. While concepts such as Deep Learning have existed for many years, the computational power for realising the utility of these algorithms in real-world applications has only recently become available. This dissertation investigated the efficacy of a novel, general method for deploying ML in a variety of complex tasks, where best feature selection, data-set labelling, model definition and training processes were determined automatically. Models were developed in an iterative fashion, evaluated using both training and validation data sets. The proposed method was evaluated using three distinct case studies, describing complex classification tasks often requiring significant input from human experts. The results achieved demonstrate that the proposed method compares with, and often outperforms, less general, comparable methods designed specifically for each task. Feature selection, data-set annotation, model design and training processes were optimised by the method, where less complex, comparatively accurate classifiers with lower dependency on computational power and human expert intervention were produced. In chapter 4, the proposed method demonstrated improved efficacy over comparable systems, automatically identifying and classifying complex application protocols traversing IP networks. In chapter 5, the proposed method was able to discriminate between normal and anomalous traffic, maintaining accuracy in excess of 99%, while reducing false alarms to a mere 0.08%. Finally, in chapter 6, the proposed method discovered more optimal classifiers than those implemented by comparable methods, with classification scores rivalling those achieved by state-of-the-art systems. The findings of this research concluded that developing a fully automated, general method, exhibiting efficacy in a wide variety of complex classification tasks with minimal expert intervention, was possible. The method and various artefacts produced in each case study of this dissertation are thus significant contributions to the field of ML. Advisors/Committee Members: Nitschke, Geoff Stuart (advisor).

Subjects/Keywords: Pattern Recognition; Machine Learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Goss, R. G. (2017). APIC: A method for automated pattern identification and classification. (Thesis). University of Cape Town. Retrieved from http://hdl.handle.net/11427/27025

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Goss, Ryan Gavin. “APIC: A method for automated pattern identification and classification.” 2017. Thesis, University of Cape Town. Accessed February 27, 2021. http://hdl.handle.net/11427/27025.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Goss, Ryan Gavin. “APIC: A method for automated pattern identification and classification.” 2017. Web. 27 Feb 2021.

Vancouver:

Goss RG. APIC: A method for automated pattern identification and classification. [Internet] [Thesis]. University of Cape Town; 2017. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/11427/27025.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Goss RG. APIC: A method for automated pattern identification and classification. [Thesis]. University of Cape Town; 2017. Available from: http://hdl.handle.net/11427/27025

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Loughborough University

10. Adam, Mohamad Z. Unfamiliar facial identity registration and recognition performance enhancement.

Degree: PhD, 2013, Loughborough University

URL: http://hdl.handle.net/2134/11431

► The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to… (more)

▼ The work in this thesis aims at studying the problems related to the robustness of a face recognition system where specific attention is given to the issues of handling the image variation complexity and inherent limited Unique Characteristic Information (UCI) within the scope of unfamiliar identity recognition environment. These issues will be the main themes in developing a mutual understanding of extraction and classification tasking strategies and are carried out as a two interdependent but related blocks of research work. Naturally, the complexity of the image variation problem is built up from factors including the viewing geometry, illumination, occlusion and other kind of intrinsic and extrinsic image variation. Ideally, the recognition performance will be increased whenever the variation is reduced and/or the UCI is increased. However, the variation reduction on 2D facial images may result in loss of important clues or UCI data for a particular face alternatively increasing the UCI may also increase the image variation. To reduce the lost of information, while reducing or compensating the variation complexity, a hybrid technique is proposed in this thesis. The technique is derived from three conventional approaches for the variation compensation and feature extraction tasks. In this first research block, transformation, modelling and compensation approaches are combined to deal with the variation complexity. The ultimate aim of this combination is to represent (transformation) the UCI without losing the important features by modelling and discard (compensation) and reduce the level of the variation complexity of a given face image. Experimental results have shown that discarding a certain obvious variation will enhance the desired information rather than sceptical in losing the interested UCI. The modelling and compensation stages will benefit both variation reduction and UCI enhancement. Colour, gray level and edge image information are used to manipulate the UCI which involve the analysis on the skin colour, facial texture and features measurement respectively. The Derivative Linear Binary transformation (DLBT) technique is proposed for the features measurement consistency. Prior knowledge of input image with symmetrical properties, the informative region and consistency of some features will be fully utilized in preserving the UCI feature information. As a result, the similarity and dissimilarity representation for identity parameters or classes are obtained from the selected UCI representation which involves the derivative features size and distance measurement, facial texture and skin colour. These are mainly used to accommodate the strategy of unfamiliar identity classification in the second block of the research work. Since all faces share similar structure, classification technique should be able to increase the similarities within the class while increase the dissimilarity between the classes. Furthermore, a smaller class will result on less burden on the identification or recognition processes. The…

Subjects/Keywords: 006.4; Pattern recognition; Image understanding

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APA (6^th Edition):

Adam, M. Z. (2013). Unfamiliar facial identity registration and recognition performance enhancement. (Doctoral Dissertation). Loughborough University. Retrieved from http://hdl.handle.net/2134/11431

Chicago Manual of Style (16^th Edition):

Adam, Mohamad Z. “Unfamiliar facial identity registration and recognition performance enhancement.” 2013. Doctoral Dissertation, Loughborough University. Accessed February 27, 2021. http://hdl.handle.net/2134/11431.

MLA Handbook (7^th Edition):

Adam, Mohamad Z. “Unfamiliar facial identity registration and recognition performance enhancement.” 2013. Web. 27 Feb 2021.

Vancouver:

Adam MZ. Unfamiliar facial identity registration and recognition performance enhancement. [Internet] [Doctoral dissertation]. Loughborough University; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2134/11431.

Council of Science Editors:

Adam MZ. Unfamiliar facial identity registration and recognition performance enhancement. [Doctoral Dissertation]. Loughborough University; 2013. Available from: http://hdl.handle.net/2134/11431

University of New South Wales

11. Xi, Kai. Biometric Security System Design: From Mobile to Cloud Computing Environment.

Degree: Engineering & Information Technology Canberra, 2012, University of New South Wales

URL: http://handle.unsw.edu.au/1959.4/52179 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10849/SOURCE01?view=true

► Worldwide adoption of mobile products and cloud computing services not only continues, but is accelerating. Biometric security technology shows promising in addressing the issue of… (more)

▼ Worldwide adoption of mobile products and cloud computing services not only continues, but is accelerating. Biometric security technology shows promising in addressing the issue of authenticating genuine user that is a fundamental flaw in conventional cryptography. Conventional biometric applications, specifically verification and identification, have been extensively investigated over the past decades, leading to a significant improvement. However, several issues are still unsolved and the related research will continue. In this thesis, we are working on four researchproblems encountered in applying biometric security technology in mobile and cloudcomputing environment.Firstly, designing a secure user-side biometric authentication approach targeting computation-constrained mobile devices is a challenge and draws great attention. Most existing biometric methods, which normally require high-intensive computing power, are not specially designed for operating on mobile platforms. Only a handful of low-end mobile biometric solutions exist in the literature, the performances of which, however, are far from acceptable. In this thesis, a computational efficient CPR-based (Correlation Pattern Recognition) face authentication scheme (HCFA)was developed which suits various camera-equipped and java-enabled mobile devices. The proposed partial correlation output peak analysis (PCOPA) is operated on selected sub-regions of a facial image, in conjunction with the conventional direct cross-correlation method on downsampling images. The statistical experiments on public database show a good verification performance results. Moreover, the maximum memory consumption of such scheme is only around 500 KB. The running-time is acceptable even on lowest-end mobile platforms on the consumer market. The HCFA can be considered as an efficient, accurate, implementable, universal and maintainable mobile authentication solution.Secondly, designing a server-side non-/low intrusive biometric UAC (user access control) solution specifically towards ultra-large-scale network (e.g. cloud computing) is in urgent demand. Conventional biometric characteristics, e.g. face and fingerprint, are so sensitive to privacy that users are often conservative on their use in a distributed cloud scenario. On the other hand, non-/low intrusive biometric techniques, such as keystroke dynamics, are facing the severe scalability (efficiency in large network) problem and authentication accuracy issue. For instance, Gunetti et al. proposed a classical method, namely n-graph-based keystroke verificationmethod (GP method) which achieved a low False Acceptance Rate (FAR). Nevertheless, high False Rejection Rate (FRR) and low efficiency remain the greatest shortcomings of it. The scalability issue is due to the verification of every sample in the database. In this thesis, we addressed the scalability issue as well as verification accuracy issue. We first proposed a CPR-oriented equivalent representation of keystroke n-graph pattern. Then, two innovative CPR-based approaches,… Advisors/Committee Members: Brown, Lawrie, Engineering & Information Technology, UNSW Canberra, UNSW, Hu, Jiankhun, Engineering & Information Technology, UNSW Canberra, UNSW.

Subjects/Keywords: Pattern recognition; Biometrics; Information security

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Xi, K. (2012). Biometric Security System Design: From Mobile to Cloud Computing Environment. (Doctoral Dissertation). University of New South Wales. Retrieved from http://handle.unsw.edu.au/1959.4/52179 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10849/SOURCE01?view=true

Chicago Manual of Style (16^th Edition):

Xi, Kai. “Biometric Security System Design: From Mobile to Cloud Computing Environment.” 2012. Doctoral Dissertation, University of New South Wales. Accessed February 27, 2021. http://handle.unsw.edu.au/1959.4/52179 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10849/SOURCE01?view=true.

MLA Handbook (7^th Edition):

Xi, Kai. “Biometric Security System Design: From Mobile to Cloud Computing Environment.” 2012. Web. 27 Feb 2021.

Vancouver:

Xi K. Biometric Security System Design: From Mobile to Cloud Computing Environment. [Internet] [Doctoral dissertation]. University of New South Wales; 2012. [cited 2021 Feb 27]. Available from: http://handle.unsw.edu.au/1959.4/52179 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10849/SOURCE01?view=true.

Council of Science Editors:

Xi K. Biometric Security System Design: From Mobile to Cloud Computing Environment. [Doctoral Dissertation]. University of New South Wales; 2012. Available from: http://handle.unsw.edu.au/1959.4/52179 ; https://unsworks.unsw.edu.au/fapi/datastream/unsworks:10849/SOURCE01?view=true

Rutgers University

12. Liu, Chuanren. Sequential pattern analysis in dynamic business environments.

Degree: PhD, Management, 2015, Rutgers University

URL: https://rucore.libraries.rutgers.edu/rutgers-lib/47695/

►

Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining… (more)

▼

Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in sequences. This is a fundamental problem in data mining with diversified applications in many science and business fields, such as multimedia analysis (motion gesture/video sequence recognition), marketing analytics (buying path identification), and financial modelling (trend of stock prices). Given the overwhelming scale and the dynamic nature of the sequential data, new techniques for sequential pattern analysis are required to derive competitive advantages and unlock the power of the big data. In this dissertation, we develop novel approaches for sequential pattern analysis with applications in dynamic business environments. Our major contribution is to identify the right granularity for sequential pattern analysis. We first show that the right pattern granularity for sequential pattern mining is often unclear due to the so-called “curse of cardinality”, which corresponds to a variety of difficulties in mining sequential patterns from massive data represented by a huge set of symbolic features. Therefore, pattern mining with the original features may provide few clues on interesting temporal dynamics. To address this challenge, our approach, temporal skeletonization, reduces the representation of the sequential data by uncovering significant, hidden temporal structures. Furthermore, the right granularity is also critical for sequential pattern modelling. Particularly, there are often multiple granularity levels accessible for estimating statistical models with the sequential data. However, on one hand, the patterns at the lowest level may be too complicated for the models to produce application-enabling results; and on the other hand, the patterns at the highest level may be as trivial as common sense, which are already known without analyzing the data. To dig out the most value from the data, we propose to construct the modelling granularity in a data-driven manner balancing between the above two extremes. By identifying the right pattern granularity for both sequential pattern mining and modelling, we have successful applications in B2B (Business-to-Business) marketing analytics, healthcare operation and management, and modeling of the product adoption in digit markets, as three case studies in dynamic business environments.

Advisors/Committee Members: Xiong, Hui (chair), Papadimitriou, Spiros (internal member), Yang, Jian (internal member), Wang, Guiling (outside member).

Subjects/Keywords: Data mining; Pattern recognition systems

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APA (6^th Edition):

Liu, C. (2015). Sequential pattern analysis in dynamic business environments. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/47695/

Chicago Manual of Style (16^th Edition):

Liu, Chuanren. “Sequential pattern analysis in dynamic business environments.” 2015. Doctoral Dissertation, Rutgers University. Accessed February 27, 2021. https://rucore.libraries.rutgers.edu/rutgers-lib/47695/.

MLA Handbook (7^th Edition):

Liu, Chuanren. “Sequential pattern analysis in dynamic business environments.” 2015. Web. 27 Feb 2021.

Vancouver:

Liu C. Sequential pattern analysis in dynamic business environments. [Internet] [Doctoral dissertation]. Rutgers University; 2015. [cited 2021 Feb 27]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47695/.

Council of Science Editors:

Liu C. Sequential pattern analysis in dynamic business environments. [Doctoral Dissertation]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/47695/

Oregon State University

13. Zhang, Wei. Image features and learning algorithms for biological, generic and social object recognition.

Degree: PhD, Electrical and Computer Engineering, 2009, Oregon State University

URL: http://hdl.handle.net/1957/11178

► Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been… (more)

▼ Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been widely employed to tackle the variability of objects in pose, scale, lighting, texture, color, and so on. Different types of object recognition problems usually require different image features and corresponding learning algorithms. This dissertation focuses on the design, evaluation and application of new image features and learning algorithms for the recognition of biological, generic and social objects. The first part of the dissertation introduces a new structure-based interest region detector called the principal curvature-based region detector (PCBR) which detects stable watershed regions that are robust to local intensity perturbations. This detector is specifically designed for region detection for biological objects. Several recognition architectures are then developed that fuse visual information from disparate types of image features for the categorization of complex objects. The described image features and learning algorithms achieve excellent performance on the difficult stonefly larvae dataset. The second part of the dissertation presents studies of methods for visual codebook learning and their application to object recognition. The dissertation first introduces the methodology and application of generative visual codebooks for stonefly recognition and introduces a discriminative evaluation methodology based on a maximum mutual information criterion. Then a new generative/discriminative visual codebook learning algorithm, called iterative discriminative clustering (IDC), is presented that refines the centers and the shapes of the generative codewords for improved discriminative power. It is followed by a novel codebook learning algorithm that builds multiple codebooks that are non-redundant in discriminative power. All these visual codebook learning algorithms achieve high performance on both biological and generic object recognition tasks. The final part of the dissertation describes a socially-driven clothes recognition system for an intelligent fitting-room system. The dissertation presents the results of a user study to identify the key factors for clothes recognition. It then describes learning algorithms for recognizing these key factors from clothes images using various image features. The clothes recognition system successfully enables automated social fashion information retrieval for an enhanced clothes shopping experience. Advisors/Committee Members: Dietterich, Thomas G. (advisor), Lee, Yun-Shik (committee member).

Subjects/Keywords: object recognition; Optical pattern recognition – Mathematical models

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APA (6^th Edition):

Zhang, W. (2009). Image features and learning algorithms for biological, generic and social object recognition. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/11178

Chicago Manual of Style (16^th Edition):

Zhang, Wei. “Image features and learning algorithms for biological, generic and social object recognition.” 2009. Doctoral Dissertation, Oregon State University. Accessed February 27, 2021. http://hdl.handle.net/1957/11178.

MLA Handbook (7^th Edition):

Zhang, Wei. “Image features and learning algorithms for biological, generic and social object recognition.” 2009. Web. 27 Feb 2021.

Vancouver:

Zhang W. Image features and learning algorithms for biological, generic and social object recognition. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1957/11178.

Council of Science Editors:

Zhang W. Image features and learning algorithms for biological, generic and social object recognition. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/11178

North Carolina State University

14. Miao, Shun. 3D face recognition from range images.

Degree: MS, Electrical Engineering, 2010, North Carolina State University

URL: http://www.lib.ncsu.edu/resolver/1840.16/6295

► In this thesis, we explore the statistical and geometrical behavior of uncontrolled parameters of human face, including both rigid transform caused by head pose and… (more)

Subjects/Keywords: geodesic; segmentation; face recognition; pattern recognition

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APA (6^th Edition):

Miao, S. (2010). 3D face recognition from range images. (Thesis). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/6295

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Miao, Shun. “3D face recognition from range images.” 2010. Thesis, North Carolina State University. Accessed February 27, 2021. http://www.lib.ncsu.edu/resolver/1840.16/6295.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Miao, Shun. “3D face recognition from range images.” 2010. Web. 27 Feb 2021.

Vancouver:

Miao S. 3D face recognition from range images. [Internet] [Thesis]. North Carolina State University; 2010. [cited 2021 Feb 27]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/6295.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Miao S. 3D face recognition from range images. [Thesis]. North Carolina State University; 2010. Available from: http://www.lib.ncsu.edu/resolver/1840.16/6295

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Virginia Tech

15. Saha, Deba Pratim. Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals.

Degree: MS, Electrical and Computer Engineering, 2013, Virginia Tech

URL: http://hdl.handle.net/10919/78044

► Finger gesture recognition using glove-like interfaces are very accurate for sensing individual finger positions by employing a gamut of sensors. However, for the same reason,… (more)

Subjects/Keywords: Gesture Recognition; Wearable Joystick; Mechanomyography; Pattern Recognition

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APA (6^th Edition):

Saha, D. P. (2013). Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/78044

Chicago Manual of Style (16^th Edition):

Saha, Deba Pratim. “Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals.” 2013. Masters Thesis, Virginia Tech. Accessed February 27, 2021. http://hdl.handle.net/10919/78044.

MLA Handbook (7^th Edition):

Saha, Deba Pratim. “Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals.” 2013. Web. 27 Feb 2021.

Vancouver:

Saha DP. Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals. [Internet] [Masters thesis]. Virginia Tech; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10919/78044.

Council of Science Editors:

Saha DP. Design of a Wearable Two-Dimensional Joystick as a Muscle-Machine Interface Using Mechanomyographic Signals. [Masters Thesis]. Virginia Tech; 2013. Available from: http://hdl.handle.net/10919/78044

Oregon State University

16. You, Zeyu. A statistical inference framework for finding recurring patterns in large data with applications to energy management.

Degree: MS, Electrical and Computer Engineering, 2014, Oregon State University

URL: http://hdl.handle.net/1957/50023

► We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images… (more)

Subjects/Keywords: recurring pattern recognition; Pattern recognition systems – Mathematical models

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APA (6^th Edition):

You, Z. (2014). A statistical inference framework for finding recurring patterns in large data with applications to energy management. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/50023

Chicago Manual of Style (16^th Edition):

You, Zeyu. “A statistical inference framework for finding recurring patterns in large data with applications to energy management.” 2014. Masters Thesis, Oregon State University. Accessed February 27, 2021. http://hdl.handle.net/1957/50023.

MLA Handbook (7^th Edition):

You, Zeyu. “A statistical inference framework for finding recurring patterns in large data with applications to energy management.” 2014. Web. 27 Feb 2021.

Vancouver:

You Z. A statistical inference framework for finding recurring patterns in large data with applications to energy management. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1957/50023.

Council of Science Editors:

You Z. A statistical inference framework for finding recurring patterns in large data with applications to energy management. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/50023

Portland State University

17. Sharma, Karan. The Link Between Image Segmentation and Image Recognition.

Degree: MS(M.S.) in Computer Science, Computer Science, 2012, Portland State University

URL: https://pdxscholar.library.pdx.edu/open_access_etds/199

► A long standing debate in computer vision community concerns the link between segmentation and recognition. The question I am trying to answer here is,… (more)

Subjects/Keywords: Image segmentation; Scalability; Recognition algorithms; Computer vision; Pattern recognition systems; Image processing; Optical pattern recognition

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APA (6^th Edition):

Sharma, K. (2012). The Link Between Image Segmentation and Image Recognition. (Masters Thesis). Portland State University. Retrieved from https://pdxscholar.library.pdx.edu/open_access_etds/199

Chicago Manual of Style (16^th Edition):

Sharma, Karan. “The Link Between Image Segmentation and Image Recognition.” 2012. Masters Thesis, Portland State University. Accessed February 27, 2021. https://pdxscholar.library.pdx.edu/open_access_etds/199.

MLA Handbook (7^th Edition):

Sharma, Karan. “The Link Between Image Segmentation and Image Recognition.” 2012. Web. 27 Feb 2021.

Vancouver:

Sharma K. The Link Between Image Segmentation and Image Recognition. [Internet] [Masters thesis]. Portland State University; 2012. [cited 2021 Feb 27]. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/199.

Council of Science Editors:

Sharma K. The Link Between Image Segmentation and Image Recognition. [Masters Thesis]. Portland State University; 2012. Available from: https://pdxscholar.library.pdx.edu/open_access_etds/199

Ryerson University

18. Vo, Luan. Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models.

Degree: 2012, Ryerson University

URL: https://digital.library.ryerson.ca/islandora/object/RULA%3A2178

► This thesis applies the time-varying signal processing models to track the multifactor systematic risk in the Fama-French model. The mean reverting, random walk and random… (more)

Subjects/Keywords: Image processing – Digital techniques; Optical pattern recognition

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APA (6^th Edition):

Vo, L. (2012). Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models. (Thesis). Ryerson University. Retrieved from https://digital.library.ryerson.ca/islandora/object/RULA%3A2178

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vo, Luan. “Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models.” 2012. Thesis, Ryerson University. Accessed February 27, 2021. https://digital.library.ryerson.ca/islandora/object/RULA%3A2178.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vo, Luan. “Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models.” 2012. Web. 27 Feb 2021.

Vancouver:

Vo L. Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models. [Internet] [Thesis]. Ryerson University; 2012. [cited 2021 Feb 27]. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2178.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vo L. Multifactor Systematic Risk Analysis Based on Time-varying Signal Processing Models. [Thesis]. Ryerson University; 2012. Available from: https://digital.library.ryerson.ca/islandora/object/RULA%3A2178

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

19. Bibby, Michael C. Special associative preprocessing structures for qualitative feature extraction.

Degree: MS, Electrical and Computer Engineering, 1986, Oregon State University

URL: http://hdl.handle.net/1957/39899

► Existing pattern recognition and classification algorithms in computer vision require vast amounts of computations on input data. As a result, memory access time is a… (more)

Subjects/Keywords: Pattern recognition systems

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APA (6^th Edition):

Bibby, M. C. (1986). Special associative preprocessing structures for qualitative feature extraction. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/39899

Chicago Manual of Style (16^th Edition):

Bibby, Michael C. “Special associative preprocessing structures for qualitative feature extraction.” 1986. Masters Thesis, Oregon State University. Accessed February 27, 2021. http://hdl.handle.net/1957/39899.

MLA Handbook (7^th Edition):

Bibby, Michael C. “Special associative preprocessing structures for qualitative feature extraction.” 1986. Web. 27 Feb 2021.

Vancouver:

Bibby MC. Special associative preprocessing structures for qualitative feature extraction. [Internet] [Masters thesis]. Oregon State University; 1986. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1957/39899.

Council of Science Editors:

Bibby MC. Special associative preprocessing structures for qualitative feature extraction. [Masters Thesis]. Oregon State University; 1986. Available from: http://hdl.handle.net/1957/39899

Oregon State University

20. Levine, Barry Arthur. The automated inference of tree system.

Degree: PhD, Computer Science, 1979, Oregon State University

URL: http://hdl.handle.net/1957/43116

► Tree systems are used in syntactic pattern recognition for describing two-dimensional patterns. We extend results on tree automata with the introduction of the subtree-invariant equivalence… (more)

▼ Tree systems are used in syntactic pattern recognition for describing two-dimensional patterns. We extend results on tree automata with the introduction of the subtree-invariant equivalence relation R. R relates two trees when the appearance of one implies the appearance of the other in similar trees. A new state minimizing algorithm for tree automata is formed using R. We also determine a bound for Brainerd's minimization method. We introduce the Group Unordered Tree Automaton (GUTA) which accepts all orientations of open-line patterns described using directed arc primitives. The specification of a GUTA includes an unordered tree automaton M, which only accepts a standard orientation of a given class of open-line pictures, and a transformation group, which describes how the primitives transform under rotational shifts. The GUTA performs all orientational parses in parallel, reports all successful transformations and operates in the same time complexity as M. The GUTA is much easier to specify than the equivalent non-decomposed unordered tree automaton. The problem of automating the design of unordered and ordered tree automata (grammars) is studied both on a system directed and on a highly interactive level. The system directed method uses Pao's lattice technique to infer tree automata (grammars) from structurally complete samples. It is shown that the method can infer any context-free grammar when provided with skeletal structure descriptions. This extends the results of Pao which only deal with proper subclasses of context-free grammars. The highly interactive inference system is based on the use of tree derivatives, also introduced in this thesis, for determining automaton states and possible state merging. Tree derivatives are sets of tree forms derived by replacing selected subtrees with marked nodes. The derivative sets are used to determine subtree-invariant equivalence relations which characterize tree automata. A minimization algorithm based on tree derivatives is given. We use tree derivatives to prove that a tree automaton with n states can be fully characterized by the set of trees that it accepts of depth at most 2n. The inference method compares tree derivative sets and infers subtree-invariant equivalence relations. A relation is inferred if there is sufficient overlap between the derivative sets. Our method was compared to other tree automata inference schemes, including Crespi-Reghizzi's algorithm. We have shown that our method is applicable to the entire class of context-free grammars and requires a smaller sample than Crespi-Reghizzi's algorithm which can only infer a proper subclass of operator precedence grammars. Furthermore, it appears more general than the other inference systems for tree automata or grammars. Advisors/Committee Members: Cook, Curtis R. (advisor).

Subjects/Keywords: Pattern recognition systems

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APA (6^th Edition):

Levine, B. A. (1979). The automated inference of tree system. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/43116

Chicago Manual of Style (16^th Edition):

Levine, Barry Arthur. “The automated inference of tree system.” 1979. Doctoral Dissertation, Oregon State University. Accessed February 27, 2021. http://hdl.handle.net/1957/43116.

MLA Handbook (7^th Edition):

Levine, Barry Arthur. “The automated inference of tree system.” 1979. Web. 27 Feb 2021.

Vancouver:

Levine BA. The automated inference of tree system. [Internet] [Doctoral dissertation]. Oregon State University; 1979. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1957/43116.

Council of Science Editors:

Levine BA. The automated inference of tree system. [Doctoral Dissertation]. Oregon State University; 1979. Available from: http://hdl.handle.net/1957/43116

Penn State University

21. Wen, Yicheng. Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach .

Degree: 2011, Penn State University

URL: https://submit-etda.libraries.psu.edu/catalog/12213

► This dissertation presents a framework for feature-level heterogeneous sensor data fusion in sensor networks via a language-theoretic approach. Probabilistic finite state automata (PFSA) are used… (more)

▼ This dissertation presents a framework for feature-level heterogeneous sensor data fusion in sensor networks via a language-theoretic approach. Probabilistic finite state automata (PFSA) are used to model the semantic patterns in the observations of the sensors. A novel pattern discovery algorithm is developed to extract the PFSA model from symbol sequences. It is shown that this algorithm can capture semantic structures more effectively than the existing techniques. In order to formulate the data fusion problem for semantic features, a link is established between the formal language theory and functional analysis by constructing a Hilbert space over a class of stochastic regular languages represented by PFSA. New algebraic operations are defined for PFSA with a family of parametrized inner products. The norm induced by the inner product is interpreted as a measure of the information contained in PFSA. Applications of this technique are discussed in the following areas: a) Orthogonal projection in the Hilbert space to solve the model reduction problem of PFSA. Numerical examples and experimental results are provided to elucidate the process of model order reduction. b) Supervised learning of semantic features of heterogeneous sensor data in the product Hilbert space. The semantic features are combined optimally for classification using linear discriminant analysis (LDA). The proposed algorithm has a set of parameters that can be potentially configured by the users to adapt the algorithm to environment changes. The proposed algorithm is validated for object recognition at the US-Mexican border. An architecture of fusion-driven sensor networks is introduced to incorporate the proposed fusion framework in sensor networks. The network protocol, called dynamic time-space clustering (DSTC), and its heterogeneous version, are designed to adapt the network to the fusion algorithms. A sensor network for selectively tracking mobile targets is implemented in the network simulator NS-2 for both homogeneous and heterogeneous sensor fields in an urban scenario for validating the propose architecture. Advisors/Committee Members: Asok Ray, Dissertation Advisor/Co-Advisor, Asok Ray, Committee Chair/Co-Chair, Shashi Phoha, Committee Chair/Co-Chair, Qiang Du, Committee Member, Alok Sinha, Committee Member, Qian Wang, Committee Member, Ishanu Chattopadhyay, Committee Member.

Subjects/Keywords: wireless sensor network; pattern recognition; Information fusion

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APA (6^th Edition):

Wen, Y. (2011). Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach . (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/12213

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wen, Yicheng. “Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach .” 2011. Thesis, Penn State University. Accessed February 27, 2021. https://submit-etda.libraries.psu.edu/catalog/12213.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wen, Yicheng. “Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach .” 2011. Web. 27 Feb 2021.

Vancouver:

Wen Y. Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach . [Internet] [Thesis]. Penn State University; 2011. [cited 2021 Feb 27]. Available from: https://submit-etda.libraries.psu.edu/catalog/12213.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wen Y. Heterogeneous Sensor Fusion in Sensor Networks: A Language-theoretic Approach . [Thesis]. Penn State University; 2011. Available from: https://submit-etda.libraries.psu.edu/catalog/12213

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Johannesburg

22. Findlay, Peter. Intelligent system for automated components recognition and handling.

Degree: 2012, University of Johannesburg

URL: http://hdl.handle.net/10210/4365

►

M.Ing.

A machine vision system must, by definition, be intelligent, adaptable and reliable to satisfY the objectives of a system that is highly interactive with… (more)

▼

M.Ing.

A machine vision system must, by definition, be intelligent, adaptable and reliable to satisfY the objectives of a system that is highly interactive with its dynamic environment and therefore prone to outside error factors. A machine vision system is described that utilizes a 2D captured web cam image for the purpose of intelligent object recognition, gripping and handling. The system is designed to be generic in its application and adaptable to various gripper configurations and handling configurations. This is achieved by using highly adaptable and intelligent recognition algorithms the gathers as much information as possible from a 2D colour web cam image. Numerous error-checking abilities are also built into the system to account for possible anomalies in the working environment. The entire system is designed around four separate but tightly integrated systems, namely the Recognition, Gripping and Handling structures and the Component Database which acts as the backbone of the system. The Recognition system provides all the input data that is then used for the Gripping and Handling systems. This integrated system functions as a single unit but a hierarchical structure has been used so that each of the systems can function as a stand-alone unit. The recognition system is generic in its ability to provide information such as recognized object identification, position and other orientation information that could be used by another handling system or gripper configuration. The Gripping system is based on a single custom designed gripper that provides basic gripping functionality. It is powered by a single motor and is highly functional with respect to the large range of object sizes that it can grip. The Handling Sub-system controls gripper positioning and motion. The Handling System incorporates control of the robot and the execution of both predetermined and online adaptable handling algorithms based on component data. It receives data from the Component database. The database allows the transparent ability to add and remove objects for recognition as well as other basic abilities. Experimental verification of the system is performed using a fully integrated and automated program and hardware control system developed for this purpose. The integration of the proposed system into a flexible and reconfigurable manufacturing system is explained.

Subjects/Keywords: Computer vision; Artificial intelligence; Pattern recognition systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Findlay, P. (2012). Intelligent system for automated components recognition and handling. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/4365

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Findlay, Peter. “Intelligent system for automated components recognition and handling.” 2012. Thesis, University of Johannesburg. Accessed February 27, 2021. http://hdl.handle.net/10210/4365.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Findlay, Peter. “Intelligent system for automated components recognition and handling.” 2012. Web. 27 Feb 2021.

Vancouver:

Findlay P. Intelligent system for automated components recognition and handling. [Internet] [Thesis]. University of Johannesburg; 2012. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10210/4365.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Findlay P. Intelligent system for automated components recognition and handling. [Thesis]. University of Johannesburg; 2012. Available from: http://hdl.handle.net/10210/4365

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Boston University

23. Wang, Joseph. Local learning by partitioning.

Degree: PhD, Electrical & Computer Engineering, 2015, Boston University

URL: http://hdl.handle.net/2144/15204

► In many machine learning applications data is assumed to be locally simple, where examples near each other have similar characteristics such as class labels or… (more)

▼ In many machine learning applications data is assumed to be locally simple, where examples near each other have similar characteristics such as class labels or regression responses. Our goal is to exploit this assumption to construct locally simple yet globally complex systems that improve performance or reduce the cost of common machine learning tasks. To this end, we address three main problems: discovering and separating local non-linear structure in high-dimensional data, learning low-complexity local systems to improve performance of risk-based learning tasks, and exploiting local similarity to reduce the test-time cost of learning algorithms. First, we develop a structure-based similarity metric, where low-dimensional non-linear structure is captured by solving a non-linear, low-rank representation problem. We show that this problem can be kernelized, has a closed-form solution, naturally separates independent manifolds, and is robust to noise. Experimental results indicate that incorporating this structural similarity in well-studied problems such as clustering, anomaly detection, and classification improves performance. Next, we address the problem of local learning, where a partitioning function divides the feature space into regions where independent functions are applied. We focus on the problem of local linear classification using linear partitioning and local decision functions. Under an alternating minimization scheme, learning the partitioning functions can be reduced to solving a weighted supervised learning problem. We then present a novel reformulation that yields a globally convex surrogate, allowing for efficient, joint training of the partitioning functions and local classifiers. We then examine the problem of learning under test-time budgets, where acquiring sensors (features) for each example during test-time has a cost. Our goal is to partition the space into regions, with only a small subset of sensors needed in each region, reducing the average number of sensors required per example. Starting with a cascade structure and expanding to binary trees, we formulate this problem as an empirical risk minimization and construct an upper-bounding surrogate that allows for sequential decision functions to be trained jointly by solving a linear program. Finally, we present preliminary work extending the notion of test-time budgets to the problem of adaptive privacy.

Subjects/Keywords: Electrical engineering; Machine learning; Pattern recognition

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APA (6^th Edition):

Wang, J. (2015). Local learning by partitioning. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/15204

Chicago Manual of Style (16^th Edition):

Wang, Joseph. “Local learning by partitioning.” 2015. Doctoral Dissertation, Boston University. Accessed February 27, 2021. http://hdl.handle.net/2144/15204.

MLA Handbook (7^th Edition):

Wang, Joseph. “Local learning by partitioning.” 2015. Web. 27 Feb 2021.

Vancouver:

Wang J. Local learning by partitioning. [Internet] [Doctoral dissertation]. Boston University; 2015. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2144/15204.

Council of Science Editors:

Wang J. Local learning by partitioning. [Doctoral Dissertation]. Boston University; 2015. Available from: http://hdl.handle.net/2144/15204

University of Southern Queensland

24. Hu, Hong. Accurate and robust algorithms for microarray data classification.

Degree: 2008, University of Southern Queensland

URL: http://eprints.usq.edu.au/6221/1/Hu_2008_front.pdf ; http://eprints.usq.edu.au/6221/2/Hu_2008_whole.pdf

► [Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is… (more)

▼ [Abstract]Microarray data classification is used primarily to predict unseen data using a model built on categorized existing Microarray data. One of the major challenges is that Microarray data contains a large number of genes with a small number of samples. This high dimensionality problem has prevented many existing classification methods from directly dealing with this type of data. Moreover, the small number of samples increases the overfitting problem of Classification, as a result leading to lower accuracy classification performance. Another major challenge is that of the uncertainty of Microarray data quality. Microarray data contains various levels of noise and quite often high levels of noise, and these data lead to unreliable and low accuracy analysis as well as the high dimensionality problem. Most current classification methods are not robust enough to handle these type of data properly. In our research, accuracy and noise resistance or robustness issues are focused on. Our approach is to design a robust classification method for Microarray data classification. An algorithm, called diversified multiple decision trees (DMDT) is proposed, which makes use of a set of unique trees in the decision committee. The DMDT method has increased the diversity of ensemble committees and therefore the accuracy performance has been enhanced by avoiding overlapping genes among alternative trees. Some strategies to eliminate noisy data have been looked at. Our method ensures no overlapping genes among alternative trees in an ensemble committee, so a noise gene included in the ensemble committee can affect one tree only; other trees in the committee are not affected at all. This design increases the robustness of Microarray classification in terms of resistance to noise data, and therefore reduces the instability caused by overlapping genes in current ensemble methods. The effectiveness of gene selection methods for improving the performance of Microarray classification methods are also discussed. We conclude that the proposed method DMDT substantially outperforms the other well-known ensemble methods, such as Bagging, Boosting and Random Forests, in terms of accuracy and robustness performance. DMDT is more tolerant to noise than Cascading-and-Sharing trees (CS4), particulary with increasing levels of noise in the data. The results also indicate that some classification methods are insensitive to gene selection while some methods depend on particular gene selection methods to improve their performance of classification.

Subjects/Keywords: 280207 Pattern Recognition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Hu, H. (2008). Accurate and robust algorithms for microarray data classification. (Thesis). University of Southern Queensland. Retrieved from http://eprints.usq.edu.au/6221/1/Hu_2008_front.pdf ; http://eprints.usq.edu.au/6221/2/Hu_2008_whole.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Hu, Hong. “Accurate and robust algorithms for microarray data classification.” 2008. Thesis, University of Southern Queensland. Accessed February 27, 2021. http://eprints.usq.edu.au/6221/1/Hu_2008_front.pdf ; http://eprints.usq.edu.au/6221/2/Hu_2008_whole.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Hu, Hong. “Accurate and robust algorithms for microarray data classification.” 2008. Web. 27 Feb 2021.

Vancouver:

Hu H. Accurate and robust algorithms for microarray data classification. [Internet] [Thesis]. University of Southern Queensland; 2008. [cited 2021 Feb 27]. Available from: http://eprints.usq.edu.au/6221/1/Hu_2008_front.pdf ; http://eprints.usq.edu.au/6221/2/Hu_2008_whole.pdf.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hu H. Accurate and robust algorithms for microarray data classification. [Thesis]. University of Southern Queensland; 2008. Available from: http://eprints.usq.edu.au/6221/1/Hu_2008_front.pdf ; http://eprints.usq.edu.au/6221/2/Hu_2008_whole.pdf

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University

25. Jiang, Jin. The architecture and design of a neural network classifier.

Degree: MS, Electrical and Computer Engineering, 1990, Oregon State University

URL: http://hdl.handle.net/1957/39875

► The objective of this thesis is to present the architecture and design of a neural network-based pattern classifier. The classifier detects textual characters which have… (more)

Subjects/Keywords: Pattern recognition systems

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APA (6^th Edition):

Jiang, J. (1990). The architecture and design of a neural network classifier. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/39875

Chicago Manual of Style (16^th Edition):

Jiang, Jin. “The architecture and design of a neural network classifier.” 1990. Masters Thesis, Oregon State University. Accessed February 27, 2021. http://hdl.handle.net/1957/39875.

MLA Handbook (7^th Edition):

Jiang, Jin. “The architecture and design of a neural network classifier.” 1990. Web. 27 Feb 2021.

Vancouver:

Jiang J. The architecture and design of a neural network classifier. [Internet] [Masters thesis]. Oregon State University; 1990. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1957/39875.

Council of Science Editors:

Jiang J. The architecture and design of a neural network classifier. [Masters Thesis]. Oregon State University; 1990. Available from: http://hdl.handle.net/1957/39875

Baylor University

26. -1697-5430. Semi-supervised learning for electrocardiography signal classification.

Degree: M.S.E.C.E., Baylor University. Dept. of Electrical & Computer Engineering., 2018, Baylor University

URL: http://hdl.handle.net/2104/10391

► An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected… (more)

Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition

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APA (6^th Edition):

-1697-5430. (2018). Semi-supervised learning for electrocardiography signal classification. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Masters Thesis, Baylor University. Accessed February 27, 2021. http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Web. 27 Feb 2021.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Internet] [Masters thesis]. Baylor University; 2018. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2104/10391.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Masters Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Open Universiteit Nederland

27. Leroux-Andriessen, Malou. Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview.

Degree: Master, Welten Institute, 2019, Open Universiteit Nederland

URL: http://hdl.handle.net/1820/87d6f360-b299-42fa-a8bf-d7ccecbb6269

► Effective doctor-patient communication is a central component in the delivery of healthcare. To direct an efficient and patient centred anamnesis interview, physiotherapy students use the… (more)

▼ Effective doctor-patient communication is a central component in the delivery of healthcare. To direct an efficient and patient centred anamnesis interview, physiotherapy students use the pattern recognition technique. By pattern is meant a complex of data on age, gender, origin, origin factors, course, course influencing factors, symptoms and signs that are characteristic of a disorder, a disease or syndrome. In the anamnesis interview, the physiotherapist student is asked to recognize all these different patterns and to make connections both within and between the different domains of the ICF (International Classification of Functioning, Disability and Health) model , which lead to specific targets for physical examination. The experience of the teachers at Hogeschool Arnhem and Nijmegen (HAN) is that the students have difficulties mastering the pattern recognition, and the strategies used up to now have not solved this issue. A solution to this problem that is presented in this thesis is a Serious Game (SG) that will be conceptually designed. The aim of the SG is to enhance the pattern recognition of physiotherapist students and enabling them to collect an anamnesis in an efficient and effective way. The SG is conceptualized based on requirements in the literature and outcomes of the focus group carried out with physiotherapy experts. For the focus group six people were selected by the use of the inclusion criteria: (1) availability, (2) interest, (3) five years of working experience in the role of teacher, and (4) also five years of working experience in a physiotherapy practice. The participants were between the ages of 39 and 61 (M = 55.8, SD = 9.47) and had an average work experience as physiotherapist teacher of 16 years (SD = 12.04). The design for this qualitative study is the focus group session, the development of the conceptual design of the SG, and an evaluation with the focus group through a questionnaire. The focus group questions list, the demographic questionnaire, and the evaluation questionnaire were all prepared by the student-researcher (see Appendices A to C). The results show that the most important elements included in the conceptual SG must be: 1) all domains of the ICF model, 2) per domain (almost) all possible questions that can be asked during collecting an anamnesis, 3) the story of the patient, 4) additional sources, 5) a note window, and 6) a final test. The conclusion is that the conceptual SG presented in this Master’s thesis is intended to be a starting point for a SG to enhance pattern recognition by physiotherapy students during collecting an anamnesis. This was also reflected in the evaluation questionnaire, in which the participants indicate that they see potential in the game, but also see that the SG still needs further development. Further research will have to be done before the SG can be deployed in physiotherapy education.

Subjects/Keywords: conceptual design; serious game; anamnesis; pattern recognition

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APA (6^th Edition):

Leroux-Andriessen, M. (2019). Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview. (Masters Thesis). Open Universiteit Nederland. Retrieved from http://hdl.handle.net/1820/87d6f360-b299-42fa-a8bf-d7ccecbb6269

Chicago Manual of Style (16^th Edition):

Leroux-Andriessen, Malou. “Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview.” 2019. Masters Thesis, Open Universiteit Nederland. Accessed February 27, 2021. http://hdl.handle.net/1820/87d6f360-b299-42fa-a8bf-d7ccecbb6269.

MLA Handbook (7^th Edition):

Leroux-Andriessen, Malou. “Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview.” 2019. Web. 27 Feb 2021.

Vancouver:

Leroux-Andriessen M. Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview. [Internet] [Masters thesis]. Open Universiteit Nederland; 2019. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/1820/87d6f360-b299-42fa-a8bf-d7ccecbb6269.

Council of Science Editors:

Leroux-Andriessen M. Het Conceptueel Ontwerp van een Serious Game voor Studenten Fysiotherapie om Patroonherkenning te Verbeteren Tijdens het Anamnese-Interview. [Masters Thesis]. Open Universiteit Nederland; 2019. Available from: http://hdl.handle.net/1820/87d6f360-b299-42fa-a8bf-d7ccecbb6269

University of Houston

28. Hoang, Son M. 1985-. A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS.

Degree: PhD, Computer Science, 2013, University of Houston

URL: http://hdl.handle.net/10657/1189

► The rapid development in semiconductor detector technology at CERN has provided the capability to develop an active personal dosimeter for use in space radiation environments.… (more)

Subjects/Keywords: Pattern recognition; Machine learning; Charged particle; Timepix

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APA (6^th Edition):

Hoang, S. M. 1. (2013). A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS. (Doctoral Dissertation). University of Houston. Retrieved from http://hdl.handle.net/10657/1189

Chicago Manual of Style (16^th Edition):

Hoang, Son M 1985-. “A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS.” 2013. Doctoral Dissertation, University of Houston. Accessed February 27, 2021. http://hdl.handle.net/10657/1189.

MLA Handbook (7^th Edition):

Hoang, Son M 1985-. “A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS.” 2013. Web. 27 Feb 2021.

Vancouver:

Hoang SM1. A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS. [Internet] [Doctoral dissertation]. University of Houston; 2013. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/10657/1189.

Council of Science Editors:

Hoang SM1. A PATTERN RECOGNITION APPROACH TO LEARNING TRACKS OF HEAVY-ION PARTICLES IN TIMEPIX DETECTORS. [Doctoral Dissertation]. University of Houston; 2013. Available from: http://hdl.handle.net/10657/1189

University of Adelaide

29. Sherrah, Jamie. Automatic feature extraction for pattern recognition / by Jamie Sherrah.

Degree: 1998, University of Adelaide

URL: http://hdl.handle.net/2440/19264

Proposes a framework for automatic feature extraction called generalised pre-processor. Advisors/Committee Members: Dept. of Electrical and Electronic Engineering (school).

Subjects/Keywords: Pattern recognition system.

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APA (6^th Edition):

Sherrah, J. (1998). Automatic feature extraction for pattern recognition / by Jamie Sherrah. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/19264

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Sherrah, Jamie. “Automatic feature extraction for pattern recognition / by Jamie Sherrah.” 1998. Thesis, University of Adelaide. Accessed February 27, 2021. http://hdl.handle.net/2440/19264.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Sherrah, Jamie. “Automatic feature extraction for pattern recognition / by Jamie Sherrah.” 1998. Web. 27 Feb 2021.

Vancouver:

Sherrah J. Automatic feature extraction for pattern recognition / by Jamie Sherrah. [Internet] [Thesis]. University of Adelaide; 1998. [cited 2021 Feb 27]. Available from: http://hdl.handle.net/2440/19264.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sherrah J. Automatic feature extraction for pattern recognition / by Jamie Sherrah. [Thesis]. University of Adelaide; 1998. Available from: http://hdl.handle.net/2440/19264

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Hong Kong University of Science and Technology

30. Xiao, Xiangye. Efficient co-location pattern discovery.

Degree: 2009, Hong Kong University of Science and Technology

URL: http://repository.ust.hk/ir/Record/1783.1-6349 ; https://doi.org/10.14711/thesis-b1070780 ; http://repository.ust.hk/ir/bitstream/1783.1-6349/1/th_redirect.html

► Co-location pattern discovery is to find classes of objects whose associated spatial locations are frequently in proximity. For example, map search queries, which contain keywords… (more)

▼ Co-location pattern discovery is to find classes of objects whose associated spatial locations are frequently in proximity. For example, map search queries, which contain keywords in text as well as target locations on the map, can be mined for co-located query patterns, i.e., sets of keyword queries that often search for target locations near one another. Such co-located query patterns can be used in location sensitive query suggestion, Point of Interest (POI) recommendation, and local advertising. This thesis investigates ways to improve the efficiency of co-location mining for large data sets, e.g., million-entry map search query logs. In particular, we improve the efficiency of the generate-and-test method, which is commonly used in co-location mining. This method iteratively generates instances of candidate patterns, tests and prunes the false candidates. Through experiments, we find that the major problem in generate-and-test is the excessive number of instances generated for false candidates. This problem causes both the instance generation and the pruning steps to take an unnecessarily long time. Therefore, we propose two orthogonal approaches, DenseMiner and BitmapMiner, to address the problem on instance generation and on pruning respectively. DenseMiner partitions the geographic area of objects and processes the partitions that contain a high density of objects first. This approach works well because the spatial distributions of real-world objects are usually non-uniform. As a result, processing the dense areas first often identifies false candidates early on and avoids generating the instances of these false candidates in the other areas. BitmapMiner also partitions objects by their geographical locations. It further utilizes a bitmap structure to represent the neighboring relationship between classes of objects. The cell size for the bitmap structure is set based on the overall density of the objects. The candidate pruning in each cell can be done on a per-object basis, on a per-cell basis, or in an adaptive, multi-resolution way to match the spatial distribution of the objects in the cell. Consequently, we test and prune false candidates efficiently based on the bitmaps. We have conducted experiments on both synthetic data sets and real-world data sets. The experimental results show that our proposed approaches improve the execution time by up to an order of magnitude over prior work.

Subjects/Keywords: Pattern recognition systems

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APA (6^th Edition):

Xiao, X. (2009). Efficient co-location pattern discovery. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-6349 ; https://doi.org/10.14711/thesis-b1070780 ; http://repository.ust.hk/ir/bitstream/1783.1-6349/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Xiao, Xiangye. “Efficient co-location pattern discovery.” 2009. Thesis, Hong Kong University of Science and Technology. Accessed February 27, 2021. http://repository.ust.hk/ir/Record/1783.1-6349 ; https://doi.org/10.14711/thesis-b1070780 ; http://repository.ust.hk/ir/bitstream/1783.1-6349/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Xiao, Xiangye. “Efficient co-location pattern discovery.” 2009. Web. 27 Feb 2021.

Vancouver:

Xiao X. Efficient co-location pattern discovery. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2009. [cited 2021 Feb 27]. Available from: http://repository.ust.hk/ir/Record/1783.1-6349 ; https://doi.org/10.14711/thesis-b1070780 ; http://repository.ust.hk/ir/bitstream/1783.1-6349/1/th_redirect.html.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Xiao X. Efficient co-location pattern discovery. [Thesis]. Hong Kong University of Science and Technology; 2009. Available from: http://repository.ust.hk/ir/Record/1783.1-6349 ; https://doi.org/10.14711/thesis-b1070780 ; http://repository.ust.hk/ir/bitstream/1783.1-6349/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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Open Access Theses and Dissertations