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Title: A security architecture for IPv6 enabled wireless medical sensor networks.
Contributors: M. Abdul Alim (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: We present the design of an IPv6 enabled wireless sensor network based on the IEEE 802.15.4 standard for medical monitoring. We design a routing mechanism for efficient flooding, a hop-by-hop error recovery and congestion control mechanism for reliable packet delivery and a lightweight security architecture for the medical monitoring system. We extend the widely used Extensible Authentication Protocol (EAP) to employ the Generalized Pre-shared Key (GPSK) authentication method with some optimizations for securing the system. We use the 3-party EAP model with the Personal Area Network Coordinator (PAN coordinator) of IEEE 802.15.4 standard as the EAP authenticator for authenticating sensor nodes within the radio range of the PAN coordinator. In order to use EAP authentication for a sensor node several hops away from the PAN coordinator, we define a new role (relay authenticator) for its coordinator which tunnels EAP messages to the PAN coordinator securely. We define EAP message encapsulation for IEEE 802.15.4 networks and a key hierarchy for the security architecture. We have simulated the system and shown that EAP based authentication is feasible in wireless sensor networks.
Discipline: Computer Science
Date added: 2017-03-30T17:06:02.1Z

Title: Experimental study of multi-level regional voting scheme and its application in human face recognition.
Contributors: Jian Zhang (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: Based on previous regional voting scheme model studies and assumptions, we extend studies from two-level voting scheme models to multi-level voting scheme models. Due to mathematical complexity, we use the Monte Carlo approach in studying the stability of the multi-level regional voting scheme with respect to region sizes and levels. Using this model, we are able to obtain the stability characteristics of the national voting scheme, the two-level regional voting scheme, and the multi-level regional voting scheme, and apply it in FERET human face database. According to our study, we verify again that the regional voting scheme (including the two-level regional voting scheme and multi-level regional voting scheme) is always more stable than the national voting scheme. We find that the stability of the multi-level regional voting scheme is not as good as the stability of the two-level regional voting scheme when region size is within a certain range. Out of this range, the multi-level regional voting scheme may compete with the two-level voting scheme. We conclude that the multi-level regional voting scheme may be comparable to the two-level regional voting scheme and prove our conclusion in the face recognition application.
Discipline: Computer Science
Date added: 2017-03-30T17:03:44.07Z

Title: A software framework for simulation studies of interaction models in agent teamwork.
Contributors: Omid Alemi (author), Jernej Polajnar (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: This thesis proposes a new software framework that facilitates the study of agent interaction models in early development stages from a designer's perspective. Its purpose is to help reduced the design decision space through simulation experiments that provide early feedback on comparative performance of alternative solutions. This is achieved through interactive concurrent simulation of multiple teams in a representative microworld context. The generic simulator's architecture accommodates an open class of different microworlds and permits multiple communication mechanisms. It also supports interoperability with other software tools, distributed simulation, and various extensions. The framework was validated in the context of two different research projects on helpful behavior in agent teams: the Mutual Assistance Protocol, based on rational criteria for help, and the Empathic Help Model, based on a concept of empathy for artificial agents. The results show that the framework meets its design objectives and provides the flexibility needed for research experimentation. --Leaf i.
Discipline: Computer Science
Date added: 2017-03-30T17:12:21.252Z

Title: Regional displacement matching scheme for LBP based face recognition.
Contributors: Ling Yan (author), Uang Chen (Thesis advisor), David Casperson (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: In face recognition, alignment of the face images has been a known open issue. This thesis proposes a displacement based local aligning scheme to construct a structural descriptive image template for comparison. To conquer the registration difficulties caused by the non-rigidity of human face images, a block displacement strategy is introduced to apply the regional voting scheme to face recognition field. Local Binary Pattern (LBP) is adopted to construct this block LBP displacement-based local matching approach, we name LBP-DLMA. Experiments are performed and have demonstrated the outstanding performances of this LBP-DLMA over the original LBP approach. It is expected and shown by experiments that this approach applies to both large and small sized images, and that it also applies to descriptor approaches other than LBP. --Leaf ii.
Discipline: Computer Science
Date added: 2017-04-11T21:12:44.531Z

Title: Inverse scale invariant feature transform models for object recognition and image tagging.
Contributors: Md. Kamrul Hasan (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: This thesis presents three novel image models based on Scale Invariant Feature Transform (SIFT) features and the k-Nearest Neighbors (k-NN) machine learning methodology. While SIFT features characterize an image with distinctive keypoints, the k-NN filters away and normalizes the keypoints with a two-fold goal: (i) compressing the image size, and (ii) reducing the bias that is induced by the variance of keypoint numbers among object classes. Object recognition is approached as a supervised machine learning problem, and the models have been formulated using Support Vector Machines (SVMs). These object recognition models have been tested for single and multiple object detection, and for asymmetrical rotational recognition. Finally, a hierarchical probabilistic framework with basic object classification methodology is formulated as a multi-class learning framework. This framework has been tested for automatic image annotation generation. Object recognition models were evaluated using recognition rate (rank 1) whereas the annotation task was evaluated using the well-known Information Retrieval measures: precision, recall, average precision and average recall.
Discipline: Computer Science
Date added: 2017-03-30T17:06:52.164Z

Title: A neural network model of the primary visual cortex.
Contributors: Alan Spara (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: Many problems in modern computing require a visual component. That is to say, it is fairly common for applications to have a need to see their environments. These applications will typically employ techniques designed specifically to solve the particular task needed for the application, and have little or no relation to the human visual system. Humans generally do not have difficulty interpreting the world around us. When traveling through known environments, we can easily recognize particular walls, doors and other objects in our view. We are not confused by the huge number factors that can complicate an image. The generalization and robustness of the human system would provide a huge benefit to any system that requires more advanced vision than is capable with the ad-hoc methods developed previously. If the underlying principles that make the human visual system so powerful can be identified and implemented programmatically, then a machine could reap the benefits obtained by humans. The purpose of this thesis is to demonstrate that a visual system modeled after the human visual system will be robust and accurate enough to solve real world problems - and to be useful in a non-trivial application. By developing neural networks that directly model the most primitive image processing cells of the human visual system, a platform can be built on which advanced vision systems can be developed.
Discipline: Computer Science
Date added: 2017-03-30T17:06:00.019Z

Title: Information retrieval by multi-perspective representation.
Contributors: Jia Zeng (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Date added: 2017-03-30T17:01:51.318Z

Title: Neighbouring proximity
Contributors: Hongyuan Shi (author), Jueyi Sui (thesis advisor), Jernej Polajnar (committee member)
Abstract: Deep Learning has become increasingly popular since 2006. It has an outstanding capability to extract and represent the features of raw data and it has been applied to many domains, such as image processing, pattern recognition, computer vision, machine translation, natural language processing, and autopilot. While the advantages of deep learning methods are widely accepted, the limitations are not well studied. This thesis studies cases where deep learning methods lose their advantages over traditional methods. Our experiments show that, when the neighbouring proximity disappears, deep learning methods are significantly less powerful than traditional methods. Our work not only clearly indicates that deep structure methods cannot fully replace traditional shallow methods but also shows the potential risks of applying deep learning to autopilot.
Discipline: Computer Science
Date added: 2019-09-04T21:14:09.159Z

Title: A framework to study the performance of the group mutual exclusion algorithms
Contributors: Darshik Shirodaria (author), Alex Aravind (thesis advisor), Waqar Haque (committee member), Balbinder Deo (committee member), University of Northern British Columbia (Degree granting institution)
Abstract: Group mutual exclusion problem generalizes the classical mutual exclusion problem, a fundamental problem in concurrent programming. It arises in applications involving sharing resources such as memory and data. In group mutual exclusion, a process requests for a “forum”; processes requesting the same forum may access the critical section simultaneously. Several algorithms have been proposed for the group mutual exclusion problem, but very few studies have been conducted to compare the performances of these algorithms by means of execution on actual machines. Besides the studies conducted have been a mere one-on-one comparison. Also, there exists no testing environment that accommodates multiple algorithms and compare their executions. This work aims at testing the performance of group mutual exclusion algorithms extensively by executing multiple such algorithms in a test framework. We propose to build an automated test framework to execute these algorithms, both individually and collectively under various experimental setups and observe their performances graphically using several performance metrics. Our experiments would constitute several collective comparison studies of algorithms along with replicating a few one-on-one comparison experiments from the literature. To use the algorithms into our framework, we intend to translate them from pseudo codes to source codes. The aim is to eventually creating a repository of these source codes such that they could be used for other applications besides our framework.
Discipline: Computer Science
Date added: 2017-08-30T16:40:07.607Z

Title: Ergodic Flocking of Swarm Robots
Contributors: Conan Veitch (author), Alex A Aravind (thesis advisor), Mark Dale (committee member), Matt Reid (committee member), Edward Dobrowolski (committee member)
Abstract: The design of efficient control strategies is a well studied problem. Due to recent technological advancements and applications in the field of robotics, exploring novel ways to design optimal control for multi-robot systems has gained interest. In this respect, the concept of ergodicity has successfully been applied as an effective control technique for tracking and area coverage. The generation of flocking behaviour is a problem that involves both tracking and coverage, and as such is also suited for the use of ergodicity. The main contribution of this thesis is the application of ergodicity to emulate flocking behaviour. This approach is appealing because control and communication is assumed to be local, self-organized, and does not require separate algorithms in order to generate different behaviour. Simulation results show that the proposed approach is effective and a prototype provides evidence that flocking behaviour is possible using ergodicity in a real-life setting.
Discipline: Computer Science
Date added: 2019-10-03T17:01:53.318Z

Title: EMACS for ALDOR: An external learning system.
Contributors: Jeremy (Ya-Yu) Hsieh (author), David Casperson (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Date added: 2017-03-30T17:01:25.068Z

Title: A diverse user model in the context of an intelligent tutoring system.
Contributors: Nathan Kevin Keim (author), Charles Brown (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Date added: 2017-03-29T17:33:46.111Z

Title: A graph-based approach towards automatic text summarization
Contributors: Qandeel Fatima (author), Waqar Haque (thesis advisor), David Casperson (committee member), Reza Chowdhury (committee member)
Abstract: Due to an exponential increase in number of electronic documents and easy access to information on the Internet, the need for text summarization has become obvious. An ideal summary contains important parts of the original document, eliminates redundant information and can be generated from single or multiple documents. There are several online text summarizers but they have limited accessibility and generate somewhat incoherent summaries. We have proposed a Graph-based Automatic Summarizer (GAUTOSUMM), which consists of a pre-processing module, control features and a post-processing module. For evaluation, two datasets, Opinosis and DUC 2007 are used and generated summaries are evaluated using ROUGE metrics. The results show that GAUTOSUMM outperforms the online text summarizers in eight out of ten topics both in terms of the summary quality and time performance. A user interface has also been built to collect the original text and the desired number of sentences in the summary.
Discipline: Computer Science
Date added: 2019-09-04T21:14:08.453Z

Title: Connectionless routing protocols for mobile ad-hoc networks.
Contributors: Yong Sun (author), Kuppuchamy Alagarsamy (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Date added: 2017-03-30T17:00:40.608Z

Title: Local binary pattern network: a deep learning approach for face recognition
Contributors: Meng Xi (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: Deep learning is well known as a method to extract hierarchical representations of data. This method has been widely implemented in many fields, including image classification, speech recognition, natural language processing, etc. Over the past decade, deep learning has made a great progress in solving face recognition problems due to its effectiveness. In this thesis a novel deep learning multilayer hierarchy based methodology, named Local Binary Pattern Network (LBPNet), is proposed. Unlike the shallow LBP method, LBPNet performs multi-scale analysis and gains high-level representations from low-level overlapped features in a systematic manner. The LBPNet deep learning network is generated by retaining the topology of Convolutional Neural Network (CNN) and replacing its trainable kernel with the off-the-shelf computer vision descriptor, the LBP descriptor. This enables LBPNet to achieve a high recognition accuracy without requiring costly model learning approach on massive data. LBPNet progressively extracts features from input images from test and training data through multiple processing layers, pairwisely measures the similarity of extracted features in regional level, and then performs the classification based on the aggregated similarity values. Through extensive numerical experiments using the popular benchmarks (i.e., FERET, LFW and YTF), LBPNet has shown the promising results. Its results out-perform (on FERET) or are comparable (on LFW and FERET) to other methods in the same categories, which are single descriptor based unsupervised learning methods on FERET and LFW, and single descriptor based supervise learning methods with image-restricted no outside data settings on LFW and YTF, respectively. --Leaves i-ii.
Discipline: Computer Science
Date added: 2017-03-29T17:29:47.121Z

Title: A copula based method for fish species classification
Contributors: Raj Singh Dhawal (author), Liang Chen (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: The purpose of this thesis is to develop a method for classification of the species of a fish given in an image. This method uses the state of the art multi-dimensional image descriptor HOG (Histogram of Oriented Gradients), and colour histograms to create representative feature vectors. In this work copula theory have been used to summarize the multi-dimensional features. Copula theory has been used extensively for analysing the bivariate data however, not much exploration has been done to find its application for analysing multivariate data. This work is one of the few attempts where copulas have been used to analyse multivariate data. The classification accuracy of this method is comparable with other reported methods. --Leaf ii.
Discipline: Computer Science
Date added: 2017-03-29T17:28:19.984Z

Title: Parallelism in multiple sequence alignment.
Contributors: Qiong Bai (author), Siamak Rezaei (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: No abstract available.
Discipline: Computer Science
Date added: 2017-03-30T17:01:07.266Z

Title: Sentence encoders for semantic textual similarity
Contributors: Aarthi Babu (author), Ian Hartley (thesis advisor), David Casperson (committee member), University of Northern British Columbia (Degree granting institution)
Abstract: The last decade has witnessed many accomplishments in the field of Natural Language Processing, especially in understanding the language semantics. Well-established machine learning models for generating word representation are available and has been proven useful. However, the existing techniques proposed for learning sentence level representations do not adequately capture the complexity of compositional semantics. Finding semantic similarity between sentences is a fundamental language understanding problem. In this project, we compare various machine learning models on their ability to capture the semantics of a sentence using Semantic Textual Similarity (STS) Task. We focus on models that exhibit state-of-the-art performance in Sem-Eval(2017) STS shared task. Also, we analyse the impact of models’ internal architectures on STS task performance. Out of all the models the we compared, Bi-LSTM RNN with max-pooling layer achieves the best performance in extracting a generic semantic representation and aids in better transfer learning when compared to hierarchical CNN.
Discipline: Computer Science
Date added: 2019-03-18T19:10:20.258Z

Title: A mutual assistance protocol for agent teamwork.
Contributors: Narek Nalbandyan (author), Jernej Polajnar (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: This thesis proposes a novel protocol for incorporating helpful behavior into multiagent teamwork. In the proposed protocol, call the Mutual Assistance Protocol (MAP), an agent can use its own abilities and resources to advance a subtask assigned to another agent. The helpful act is performed only when the two agents jointly determine that it is in the interest of the team. The underlying design principle is that each agent assesses the team impact of changes in its own local plan. The distributed decision is reached through a bidding sequence similar to the Contract Net Protocol. The helpful act may consist in performing an action or in granting resources. The advantages of MAP over protocols that use unilateral help decisions are demonstrated through simulation experiments, using varying levels of mutual awareness in the team, dynamic disturbance in the environment, communication costs, and computation costs. --P. ii.
Discipline: Computer Science
Date added: 2017-03-30T17:11:26.485Z

Title: New watermarking methods for digital images.
Contributors: Md. Wahedul Islam (author), Saif Zahir (Thesis advisor), University of Northern British Columbia (Degree granting institution)
Abstract: The phenomenal spread of the Internet places an enormous demand on content-ownership-validation. In this thesis, four new image-watermarking methods are presented. One method is based on discrete-wavelet-transformation (DWT) only while the rest are based on DWT and singular-value-decomposition (SVD) ensemble. The main target for this thesis is to reach a new blind-watermarking-method. Method IV presents such watermark using QR-codes. The use of QR-codes in watermarking is novel. The choice of such application is based on the fact that QR-Codes have errors self-correction-capability of 5% or higher which satisfies the nature of digital-image-processing. Results show that the proposed-methods introduced minimal distortion to the watermarked images as compared to other methods and are robust against JPEG, resizing and other attacks. Moreover, watermarking-method-II provides a solution to the detection of false watermark in the literature. Finally, method IV presents a new QR-code guided watermarking-approach that can be used as a steganography as well. --Leaf ii.
Discipline: Computer Science
Date added: 2017-03-30T17:12:33.168Z

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