Theory and Algorithms Commons^™

Neighborhood Defined Adaboost Based Mixture Of Color Components For Efficient Skin Segmentation, Ramya Reddy Maaram

Electrical & Computer Engineering Theses & Dissertations

A skin segmentation algorithm robust to illumination changes and skin-like backgrounds is developed in this thesis. So far skin pixel classification has been limited to only individual color spaces and there has not been a comprehensive evaluation of which color components or combination of color components would provide the best classification accuracy, Color components in a given color space form the feature set for the classification of skin pixels. The combination of the color components or the features present within a single color space may not be the best when it comes to skin pixel classification as the discriminatory power …

An Adaptive And Non-Linear Technique For Enhancement Of High Contrast Images, Saibabu Arigela

Electrical & Computer Engineering Theses & Dissertations

In night time surveillance, there is a possibility of having extremely bright and dark regions in some image frames of a video sequence. Neither the object details in the low intensity areas nor in the high intensity areas can be clearly interpreted. Several image processing techniques have been developed to retrieve meaningful information under low lighting conditions. The algorithm based on integrated neighborhood dependency of pixel characteristics, and that based on the illuminance reflectance model perform well for improving the visual quality of digital images captured under extremely low and nonuniform lighting conditions. But these techniques cannot perform well in …

Novelty Detection For Cross-Lingual News Stories With Visual Duplicates And Speech Transcripts, Xiao Wu, Alexander G. Hauptmann, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

An overwhelming volume of news videos from different channels and languages is available today, which demands automatic management of this abundant information. To effectively search, retrieve, browse and track cross-lingual news stories, a news story similarity measure plays a critical role in assessing the novelty and redundancy among them. In this paper, we explore the novelty and redundancy detection with visual duplicates and speech transcripts for cross-lingual news stories. News stories are represented by a sequence of keyframes in the visual track and a set of words extracted from speech transcript in the audio track. A major difference to pure …

Channel Management In Heterogeneous Cellular Networks, Mohammad Hadi Arbabi

Computer Science Theses & Dissertations

Motivated by the need to increase system capacity in the face of tight FCC regulations, modem cellular systems are under constant pressure to increase the sharing of the frequency spectrum among the users of the network.

Key to increasing system capacity is an efficient channel management strategy that provides higher capacity for the system while, at the same time, providing the users with Quality of Service guarantees. Not surprisingly, dynamic channel management has become a high profile topic in wireless communications. Consider a highly populated urban area, where mobile traffic loads are increased due to highway backups or sporting events. …

A Genetic Algorithm For Cellular Manufacturing Design And Layout, Xiaodan Wu, Chao-Hsien Chu, Yunfeng Wang, Weili Yan

Research Collection School Of Computing and Information Systems

Cellular manufacturing (CM) is an approach that can be used to enhance both flexibility and efficiency in today’s small-to-medium lot production environment. The design of a CM system (CMS) often involves three major decisions: cell formation, group layout, and group schedule. Ideally, these decisions should be addressed simultaneously in order to obtain the best results. However, due to the complexity and NP-complete nature of each decision and the limitations of traditional approaches, most researchers have only addressed these decisions sequentially or independently. In this study, a hierarchical genetic algorithm is developed to simultaneously form manufacturing cells and determine the group …

A Lateral Symmetry Approach To Percentage-Based Hybrid Pattern (Php) Training, Sheng-Uei Guan, Kiruthika Ramanathan

Research Collection School Of Computing and Information Systems

In this paper, we investigate the application of lateral symmetry to supervised learning using genetic algorithms. The hypothesis is motivated by the presence of symmetry in the animal brain and by research results showing approximately equal task division between the two hemispheres of the brain. In this paper, each training pattern is considered a task. By applying the concept of lateral symmetry, we use global training (a typically right brained activity) to learn half the tasks and local training (a left brained activity) to learn the rest of the tasks. We verified the use of this Percentage-based Pattern (PHP) training …

A Framework For Dynamizing Succinct Data Structures, Ankur Gupta, Wing K. Hon, Rahul Shah, Jeffery S. Vitter

Scholarship and Professional Work - LAS

We present a framework to dynamize succinct data structures, to encourage their use over non-succinct versions in a wide variety of important application areas. Our framework can dynamize most stateof-the-art succinct data structures for dictionaries, ordinal trees, labeled trees, and text collections.

Near-Duplicate Keyframe Retrieval With Visual Keywords And Semantic Context, Xiao Wu, Wan-Lei Zhao, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Near-duplicate keyframes (NDK) play a unique role in large-scale video search, news topic detection and tracking. In this paper, we propose a novel NDK retrieval approach by exploring both visual and textual cues from the visual vocabulary and semantic context respectively. The vocabulary, which provides entries for visual keywords, is formed by the clustering of local keypoints. The semantic context is inferred from the speech transcript surrounding a keyframe. We experiment the usefulness of visual keywords and semantic context, separately and jointly, using cosine similarity and language models. By linearly fusing both modalities, performance improvement is reported compared with the …

Efficient Gps Position Determination Algorithms, Thao Nguyen

Theses and Dissertations

This research is aimed at improving the state of the art of GPS algorithms, namely, the development of a closed-form positioning algorithm for a standalone user and the development of a novel differential GPS algorithm for a network of users. The stand-alone user GPS algorithm is a direct, closed-form, and efficient new position determination algorithm that exploits the closed-form solution of the GPS trilateration equations and works in the presence of pseudorange measurement noise for an arbitrary number of satellites in view. A two-step GPS position determination algorithm is derived which entails the solution of a linear regression and updates …

A Multi-Scale Tikhonov Regularization Scheme For Implicit Surface Modeling, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Kernel machines have recently been considered as a promising solution for implicit surface modelling. A key challenge of machine learning solutions is how to fit implicit shape models from large-scale sets of point cloud samples efficiently. In this paper, we propose a fast solution for approximating implicit surfaces based on a multi-scale Tikhonov regularization scheme. The optimization of our scheme is formulated into a sparse linear equation system, which can be efficiently solved by factorization methods. Different from traditional approaches, our scheme does not employ auxiliary off-surface points, which not only saves the computational cost but also avoids the problem …

Residual-Based Measurement Of Peer And Link Lifetimes In Gnutella Networks, Xiaoming Wang, Zhongmei Yao, Dmitri Loguinov

Computer Science Faculty Publications

Existing methods of measuring lifetimes in P2P systems usually rely on the so-called create-based method (CBM), which divides a given observation window into two halves and samples users "created" in the first half every Delta time units until they die or the observation period ends. Despite its frequent use, this approach has no rigorous accuracy or overhead analysis in the literature. To shed more light on its performance, we flrst derive a model for CBM and show that small window size or large Delta may lead to highly inaccurate lifetime distributions. We then show that create-based sampling exhibits an inherent …

On Node Isolation Under Churn In Unstructured P2p Networks With Heavy-Tailed Lifetimes, Zhongmei Yao, Xiaoming Wang, Dmitri Loguinov

Computer Science Faculty Publications

Previous analytical studies [12], [18] of unstructured P2P resilience have assumed exponential user lifetimes and only considered age-independent neighbor replacement. In this paper, we overcome these limitations by introducing a general node-isolation model for heavy-tailed user lifetimes and arbitrary neighbor-selection algorithms. Using this model, we analyze two age-biased neighbor-selection strategies and show that they significantly improve the residual lifetimes of chosen users, which dramatically reduces the probability of user isolation and graph partitioning compared to uniform selection of neighbors. In fact, the second strategy based on random walks on age-weighted graphs demonstrates that for lifetimes with infinite variance, the system …

Enhancing The Performance Of Semi-Supervised Classification Algorithms With Bridging, Jason Yuk Hin Chan, Josiah Poon, Irena Koprinska

Research Collection School Of Computing and Information Systems

Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in any supervised approach such as co-training or selflearning. We empirically show that classification performance increases by improving the semi-supervised algorithm’s ability to correctly assign labels to previouslyunlabelled …

Performance Analysis Of Ieee 802.11b Devices In The Presence Of Interference Aware Scheduling-Adaptive Frequency Hopping Enabled Bluetooth Devices, Deepthi Gopalpet

Electrical & Computer Engineering Theses & Dissertations

Wireless Local Area networks (WLAN) and Wireless Personal Area Networks (WPAN) provide complimentary services using the same unlicensed radio frequency band of operation. The 802.11b WLAN operates in the 2.4 GHz band and uses a Direct Sequence Spread Spectrum technique. It is designed to cover large areas ranging up to 100 meters in diameter, which may connect hundreds of computers. Bluetooth (BT) WPAN also operates in the same frequency band as the IEEE 802.lib and it uses a Frequency Hopping Spread Spectrum technique. BT is primarily used for communications between notebooks, palm units and other personal computing devices within relatively …

A Wavelet Based Complementary Approach For Image Enhancement, Ismail Kosum

Electrical & Computer Engineering Theses & Dissertations

Detail in an image means more meaningful information that is very important in many computer vision and pattern recognition applications. The object region visibility in an image plays an important role in obtaining accurate and desired information from the original image. In particular, image processing techniques developed for region segmentation and object classification have better results depending on the visibility in images. There are several enhancement techniques available which are capable of obtaining clear images with balanced lighting and contrast. In this thesis, a completely image dependent approach to enhance the luminance of images under extreme lighting conditions and a …

Long-Range Target Classification In A Cluttered Environment Using Multi-Sensor Image Sequences, Cenk Yaman

Electrical & Computer Engineering Theses & Dissertations

Accurate identification of unknown contacts is crucial in military intelligence. Automated systems which quickly and accurately determine the identity of a contact could be a benefit in backing up electronic signal identification methods such as Identification Friend and Foe (IFF) systems. Radio Detection and Ranging (RADAR) images are often undesirable in military applications since they reveal the location of the imaging system. So we explore the use of visible and infrared images of which are generally more consistent than RADAR images and for which it is easy to compensate for environmental effects. Recent advances in visible and IR imaging technology …

Use Of Tabu Search In A Solver To Map Complex Networks Onto Emulab Testbeds, Jason E. Macdonald

Theses and Dissertations

The University of Utah's solver for the testbed mapping problem uses a simulated annealing metaheuristic algorithm to map a researcher's experimental network topology onto available testbed resources. This research uses tabu search to find near-optimal physical topology solutions to user experiments consisting of scale-free complex networks. While simulated annealing arrives at solutions almost exclusively by chance, tabu search incorporates the use of memory and other techniques to guide the search towards good solutions. Both search algorithms are compared to determine whether tabu search can produce equal or higher quality solutions than simulated annealing in a shorter amount of time. It …

Percentage-Based Hybrid Pattern Training With Neural Network Specific Cross Over, Sheng-Uei Guan, Kiruthika Ramanathan

Research Collection School Of Computing and Information Systems

In this paper, a new weight-setting method is proposed to improve the training time and generalization accuracy of feed-forward neural networks. This method introduces a percentage-based hybrid pattern training (PHP) scheme and aims to provide a solution to the problem dependency of other Genetic Algorithm (GA)-based Neural Network weight-setting methods. A neural network is trained using a neural network specific GA until a certain percentage of the training patterns is learned. The weights thus obtained are used as the initial weights for backpropagation (BP) training, which is then applied to complete the network training. Further improvement to the method was …

Quality Of Service Routing Strategy Using Supervised Genetic Algorithm, Zhaoxia Wang, Yugeng Sun, Zhiyong Wang, Huayu Shen

Research Collection School Of Computing and Information Systems

A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates …

An Algorithm For Two-Dimensional Density Reconstruction In Proton Computed Tomography (Pct), Jihad Tafas

Theses Digitization Project

The purpose of this thesis is to develop an optimized and effective iterative reconstruction algorithm and hardware acceleration methods that work synonymously together through reconstruction in proton computed tomography, which accurately maps the electron density.

Parallelizing A Nondeterministic Optimization Algorithm, Sammy Raymond D'Souza

Theses Digitization Project

This research explores the idea that for certain optimization problems there is a way to parallelize the algorithm such that the parallel efficiency can exceed one hundred percent. Specifically, a parallel compiler, PC, is used to apply shortcutting techniquest to a metaheuristic Ant Colony Optimization (ACO), to solve the well-known Traveling Salesman Problem (TSP) on a cluster running Message Passing Interface (MPI). The results of both serial and parallel execution are compared using test datasets from the TSPLIB.

Modular Exponentiation Via The Explicit Chinese Remainder Theorem, Daniel J. Bernstein, Jonathan P. Sorenson

Scholarship and Professional Work - LAS

In this paper we consider the problem of computing xe mod m for large integers x, e, and m. This is the bottleneck in Rabin’s algorithm for testing primality, the Diffie-Hellman algorithm for exchanging cryptographic keys, and many other common algorithms.

All Minimal Prime Extensions Of Hereditary Classes Of Graphs, Vassilis Giakoumakis, Stephan Olariu

Computer Science Faculty Publications

The substitution composition of two disjoint graphs G₁ and G₂ is obtained by first removing a vertex x from G₂ and then making every vertex in G₁ adjacent to all neighbours of x in G₂. Let F be a family of graphs defined by a set Z* of forbidden configurations. Giakoumakis [V. Giakoumakis, On the closure of graphs under substitution, Discrete Mathematics 177 (1997) 83–97] proved that F∗, the closure under substitution of F, can be characterized by a set Z∗ of forbidden configurations — the minimal prime extensions of Z. He also …

A Study Of The Parallelisation Of Multiobjective Evolutionary Algorithms In A Cluster Environment, Sadeesha Gamhewa

Theses : Honours

The two main issues relating to the use of Multiobjective Evolutionary Algorithms (MOEAs) are the efficiency and effectiveness of the algorithms. As a result of the multiobjective and multi dimensional nature of MOEAs, the overall execution time that is taken to solve real world problems with MOEAs can be significant. Therefore, a few studies have recently been completed to address these performance issues by the use of parallelisation methods. The most widely known parallel Multiobjective Evolutionary Algorithm (pMOEA) models are the Master-slave, the Island, and the Diffusion models. The Master-slave and the Island models are generally implemented using message passing …

Validating Pareto Optimal Operation Parameters Of Polyp Detection Algorithms For Ct Colonography, Jiang Li, Adam Huang, Nicholas Petrick, Jianhua Yao, Ronald M. Summers, Maryellen L. Giger (Ed.), Nico Karssemeijer (Ed.)

Electrical & Computer Engineering Faculty Publications

We evaluated a Pareto front-based multi-objective evolutionary algorithm for optimizing our CT colonography (CTC) computer-aided detection (CAD) system. The system identifies colonic polyps based on curvature and volumetric based features, where a set of thresholds for these features was optimized by the evolutionary algorithm. We utilized a two-fold cross-validation (CV) method to test if the optimized thresholds can be generalized to new data sets. We performed the CV method on 133 patients; each patient had a prone and a supine scan. There were 103 colonoscopically confirmed polyps resulting in 188 positive detections in CTC reading from either the prone or …

Using Pareto Fronts To Evaluate Polyp Detection Algorithms For Ct Colonography, Adam Huang, Jiang Li, Ronald M. Summers, Nicholas Petrick, Amy K. Hara

Electrical & Computer Engineering Faculty Publications

We evaluate and improve an existing curvature-based region growing algorithm for colonic polyp detection for our CT colonography (CTC) computer-aided detection (CAD) system by using Pareto fronts. The performance of a polyp detection algorithm involves two conflicting objectives, minimizing both false negative (FN) and false positive (FP) detection rates. This problem does not produce a single optimal solution but a set of solutions known as a Pareto front. Any solution in a Pareto front can only outperform other solutions in one of the two competing objectives. Using evolutionary algorithms to find the Pareto fronts for multi-objective optimization problems has been …

Exploration Of Computational Methods For Classification Of Movement Intention During Human Voluntary Movement From Single Trial Eeg, Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

Electrical & Computer Engineering Faculty Publications

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG).

Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis …

Mining Frequent Patterns From Sequences: Theory, Algorithm, Implementation, And Performance, Markus Petteri Turkia

Theses and Dissertations

Mining frequent patterns from sequences is an important data mining problem which has direct applications in many areas. In this thesis, we make three contributions to the state-of-the-art of the sequential frequent pattern mining. First of all, we propose a fast pattern-growth mining algorithm using a novel sequence database representation called First-Occurrence Linked WAP-tree (FLWAP-tree). The pattern-growth mining algorithm using the Pre-Order Linked WAP-tree (PLWAP-tree) was reported in the literature to be faster than other algorithms. We show that our pattern-growth using our FLWAP-tree outperforms the PLWAP-tree mining significantly and consistently. Secondly, we extend the pattern-growth algorithm with partial enumeration …

Dual Constraint Problem Optimization Using A Natural Approach: Genetic Algorithm And Simulated Annealing, James P. Sweeney

UNF Graduate Theses and Dissertations

Constraint optimization problems with multiple constraints and a large solution domain are NP hard and span almost all industries in a variety of applications. One such application is the optimization of resource scheduling in a "pay per use" grid environment. Charging for these resources based on demand is often referred to as Utility Computing, where resource providers lease computing power with varying costs based on processing speed. Consumers using this resource have time and cost constraints associated with each job they submit. Determining the optimal way to divide the job among the available resources with regard to the time and …

Solving The Teacher Assignment-Course Scheduling Problem By A Hybrid Algorithm, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh

Research Collection School Of Computing and Information Systems

This paper presents a hybrid algorithm for solving atimetabling problem, which is commonly encountered in manyuniversities. The problem combines both teacher assignment andcourse scheduling problems simultaneously, and is presented as amathematical programming model. However, this problem becomesintractable and it is unlikely that a proven optimal solution can beobtained by an integer programming approach, especially for largeproblem instances. A hybrid algorithm that combines an integerprogramming approach, a greedy heuristic and a modified simulatedannealing algorithm collaboratively is proposed to solve the problem.Several randomly generated data sets of sizes comparable to that ofan institution in Indonesia are solved using the proposed algorithm.Computational results …