Artificial Intelligence and Robotics Commons^™

Sparse Coding For Hyperspectral Images Using Random Dictionary And Soft Thresholding, Ender Oguslu, Khan Iftekharuddin, Jiang Li, Mark Allen Neifeld (Ed.), Amit Ashok (Ed.)

Electrical & Computer Engineering Faculty Publications

Many techniques have been recently developed for classification of hyperspectral images (HSI) including support vector machines (SVMs), neural networks and graph-based methods. To achieve good performances for the classification, a good feature representation of the HSI is essential. A great deal of feature extraction algorithms have been developed such as principal component analysis (PCA) and independent component analysis (ICA). Sparse coding has recently shown state-of-the-art performances in many applications including image classification. In this paper, we present a feature extraction method for HSI data motivated by a recently developed sparse coding based image representation technique. Sparse coding consists of a …

Mobile Phone Graph Evolution: Findings, Model And Interpretation, Siyuan Liu, Lei Li, Christos Faloutsos, Lionel M. Ni

LARC Research Publications

What are the features of mobile phone graph along the time? How to model these features? What are the interpretation for the evolutional graph generation process? To answer the above challenging problems, we analyze a massive who-call-whom networks as long as a year, gathered from records of two large mobile phone communication networks both with 2 million users and 2 billion of calls. We examine the calling behavior distribution at multiple time scales (e.g. day, week, month and quarter), and find that the distribution is not only skewed with a heavy tail, but also changing at different time scales. How …

Adaptive Decision Support For Structured Organizations: A Case For Orgpomdps, Pradeep Reddy Varakantham, Nathan Schurr, Alan Carlin, Christopher Amato

Research Collection School Of Computing and Information Systems

In today's world, organizations are faced with increasingly large and complex problems that require decision-making under uncertainty. Current methods for optimizing such decisions fall short of handling the problem scale and time constraints. We argue that this is due to existing methods not exploiting the inherent structure of the organizations which solve these problems. We propose a new model called the OrgPOMDP (Organizational POMDP), which is based on the partially observable Markov decision process (POMDP). This new model combines two powerful representations for modeling large scale problems: hierarchical modeling and factored representations. In this paper we make three key contributions: …

Recognition Situations Using Extended Dempster-Shafer Theory, Susan Mckeever

Other resources

Weiser’s [111] vision of pervasive computing describes a world where technology seamlessly integrates into the environment, automatically responding to peoples’ needs. Underpinning this vision is the ability of systems to automatically track the situation of a person. The task of situation recognition is critical and complex: noisy and unreliable sensor data, dynamic situations, unpredictable human behaviour and changes in the environment all contribute to the complexity. No single recognition technique is suitable in all environments. Factors such as availability of training data, ability to deal with uncertain information and transparency to the user will determine which technique to use in …

Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …

Marine Buoy Detection Using Circular Hough Transform, Loc Tran, Justin Selfridge, Gene Hou, Jiang Li

Electrical & Computer Engineering Faculty Publications

A low cost method for buoy detection in maritime settings is presented using inexpensive digital cameras. In this method, the circular Hough transform is applied to an edge image to circular objects in the image. The center of these circles will signify the locations of each buoy. The known color information of the buoys is also used to enhance the performance by removing false detections. The algorithm is compared to an approach that locates buoys purely on color information. In order to validate the method, we test the approach synthetically and also with real images captured from a small surface …

Fine-Tuning Algorithm Parameters Using The Design Of Experiments Approach, Aldy Gunawan, Hoong Chuin Lau, Linda Lindawati

Research Collection School Of Computing and Information Systems

Optimizing parameter settings is an important task in algorithm design. Several automated parameter tuning procedures/configurators have been proposed in the literature, most of which work effectively when given a good initial range for the parameter values. In the Design of Experiments (DOE), a good initial range is known to lead to an optimum parameter setting. In this paper, we present a framework based on DOE to find a good initial range of parameter values for automated tuning. We use a factorial experiment design to first screen and rank all the parameters thereby allowing us to then focus on the parameter …

Electroencephalogram Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen

Electrical & Computer Engineering Theses & Dissertations

A wavelet neural network (WNN) technique rs developed for electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural networks and the time/frequency property of wavelet, where the neural network was trained on a simulated dataset with known ground truths. The contribution of this thesis is two-fold. First, many EEG artifact removal algorithms, including regression based methods, require reference EOG signals, which are not always available. To remove EEG ai1ifacts, a WNN tries to learn the characteristics of the artifacts first and does not need reference EOG signals once trained. Second, WNNs are …

On Decision Support For Deliberating With Constraints In Constrained Optimization Models, Steven O. Kimbrough, Ann Kuo, Hoong Chuin Lau, David H. Wood

Research Collection School Of Computing and Information Systems

This paper introduces the Deliberation Decision Support System (DDSS). The DDSS obtains heuristically (using a genetic algorithm) solutions of interest for constrained optimization models. This is illustrated, without loss of generality, by generalized assignment problems. The DDSS also provides users with graphical tools that support post-solution deliberation for constrained optimization models. The DDSS and this paper, as befits practical concerns, are focused on deliberation with respect to the constraints of the models being used.

A First Practical Algorithm For High Levels Of Relational Consistency, Shant Karakashian, Robert J. Woodward, Christopher Reesons, Berthe Y. Choueiry, Christian Bessiere

School of Computing: Conference and Workshop Papers

Consistency properties and algorithms for achieving them are at the heart of the success of Constraint Programming. In this paper, we study the relational consistency property R(∗,m)C, which is equivalent to m-wise consistency proposed in relational databases. We also define wR(∗,m)C, a weaker variant of this property. We propose an algorithm for enforcing these properties on a Constraint Satisfaction Problem by tightening the existing relations and without introducing new ones. We empirically show that wR(∗,m)C solves in a backtrack-free manner all the instances of some CSP benchmark classes, thus hinting at the tractability of those classes.

Evolutionary Artificial Neural Network Weight Tuning To Optimize Decision Making For An Abstract Game, Corey M. Miller

Theses and Dissertations

Abstract strategy games present a deterministic perfect information environment with which to test the strategic capabilities of artificial intelligence systems. With no unknowns or random elements, only the competitors’ performances impact the results. This thesis takes one such game, Lines of Action, and attempts to develop a competitive heuristic. Due to the complexity of Lines of Action, artificial neural networks are utilized to model the relative values of board states. An application, pLoGANN (Parallel Lines of Action with Genetic Algorithm and Neural Networks), is developed to train the weights of this neural network by implementing a genetic algorithm over a …

Cbtv: Visualising Case Bases For Similarity Measure Design And Selection, Brian Mac Namee, Sarah Jane Delany

Conference papers

In CBR the design and selection of similarity measures is paramount. Selection can benefit from the use of exploratory visualisation- based techniques in parallel with techniques such as cross-validation ac- curacy comparison. In this paper we present the Case Base Topology Viewer (CBTV) which allows the application of different similarity mea- sures to a case base to be visualised so that system designers can explore the case base and the associated decision boundary space. We show, using a range of datasets and similarity measure types, how the idiosyncrasies of particular similarity measures can be illustrated and compared in CBTV allowing …

A Boosting Framework For Visuality-Preserving Distance Metric Learning And Its Application To Medical Image Retrieval, Yang Liu, Rong Jin, Lily Mummert, Rahul Sukthankar, Adam Goode, Bin Zheng, Steven C. H. Hoi, Mahadev Satyanarayanan

Research Collection School Of Computing and Information Systems

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one …

Motivated Learning As An Extension Of Reinforcement Learning, Janusz Starzyk, Pawel Raif, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

We have developed a unified framework to conduct computational experiments with both learning systems: Motivated learning based on Goal Creation System, and reinforcedment learning using RL Q-Learning Algorithm. Future work includes combining motivated learning to set abstract motivations and manage goals with reinforcement learning to learn proper actions. This will allow testing of motivated learning on typical reinforcement learning benchmarks with large dimensionality of the state/action spaces.

Dragon Age: Origins - Maps & Benchmark Problems, Nathan R. Sturtevant, Bioware Corp

Moving AI Lab: 2D Maps and Benchmark Problems

Maps extracted from Dragon Age: Origins with help and explicit permission from BioWare Corp. for use and distribution as benchmark problems.

Contains 156 maps and benchmark problem sets.

Analysis Of Partial Discharge Pulse Height Distribution Parameters, Vinay N. Nimbole

Electrical & Computer Engineering Theses & Dissertations

Partial Discharges (PD) have been traditionally used to assess the state of any insulation system and its remnant life. In earlier work, Perspex (PMMA) samples with a needle plane gap have been aged with AC voltage. Their tree growth was monitored simultaneously by collecting PD at regular intervals of time and taking microphotographs in real time without interrupting the aging voltage. The obtained partial discharge pulse amplitude records were clustered together into groups of class intervals. The sequence of PD pulse height records was quantified as a time series of shape (η), and scale (σ) parameters of a Weibull distribution. …

Maze Maps & Benchmark Problems, Nathan R. Sturtevant

Moving AI Lab: 2D Maps and Benchmark Problems

Contains 60 maps of size 512x512 and benchmark problem sets. These maps are algorithm-generated mazes with corridor widths of 1, 2, 4, 8, 16, or 32. There are 10 maps and problem sets for each corridor size.

Room Maps & Benchmark Problems, Nathan R. Sturtevant

Moving AI Lab: 2D Maps and Benchmark Problems

Contains 40 maps of size 512x512 and problem sets. Maps are divided into rooms of size 8x8, 16x16, 32x32, and 64x64. There are 10 maps and problem sets for each room size. Maps with differing room sizes are not scaled: thickness of walls and passages differs.

Random Obstacle Maps & Benchmark Problems, Nathan R. Sturtevant

Moving AI Lab: 2D Maps and Benchmark Problems

Contains 70 maps of size 512x512 and benchmark problem sets. These maps are algorithm-generated by blocking grid cells. Maps contain 10%, 15%, 20%, 25%, 30%, 35%, or 40% blocked cells. There are 10 maps and problem sets for each percentage.

Object Detection And Classification With Applications To Skin Cancer Screening, Jonathan Blackledge, Dmitryi Dubovitskiy

Articles

This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image. The classification method is based on the application of a set of features which include fractal parameters such as the Lacunarity and Fractal Dimension. Thus, the approach used, incorporates the characterisation of an object in terms of its texture.

The principal issues associated with object recognition are presented which includes two novel fast segmentation algorithms for which C++ code is provided. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and …

Cosign: A Parallel Algorithm For Coordinated Traffic Signal Control, Shih-Fen Cheng, Marina A. Epelman, Robert L. Smith

Research Collection School Of Computing and Information Systems

The problem of finding optimal coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, the game-theoretic paradigm of fictitious play to iteratively search for a coordinated signal timing plan is employed, which improves a system-wide performance criterion for a traffic network. The algorithm is robustly scalable to realistic-size networks modeled with high-fidelity simulations. Results of a case study for the city of Troy, MI, where there are 75 signalized …

Mobius: An Omnidirectional Robotic Platform And Software Architecture For Network Teleoperation, Samuel Aaron Miller

Electrical & Computer Engineering Theses & Dissertations

The following thesis presents the results of a project to develop and test an omnidirectional robotic system (hardware and software) at NASA Langley Research Center's Robotics and Intelligent Machines Lab. The impetus for the project was the unique capabilities of omnidirectional systems. Some of the many potential benefits these systems have include improved material-handling capabilities in constrained environments (such as might be found in extraterrestrial manned habitats), efficient camera-based vehicle teleoperation, and simplified route planning for autonomous robot operations.

The project's focus was to design, build, and test a system that used Mecanum wheels to achieve omnidirectional motion. In addition …

Hybrid Committee Classifier For A Computerized Colonic Polyp Detection System, Jiang Li, Jianhua Yao, Nicholas Petrick, Ronald M. Summers, Amy K. Hara, Joseph M. Reinhardt (Ed.), Josien P.W. Pluim (Ed.)

Electrical & Computer Engineering Faculty Publications

We present a hybrid committee classifier for computer-aided detection (CAD) of colonic polyps in CT colonography (CTC). The classifier involved an ensemble of support vector machines (SVM) and neural networks (NN) for classification, a progressive search algorithm for selecting a set of features used by the SVMs and a floating search algorithm for selecting features used by the NNs. A total of 102 quantitative features were calculated for each polyp candidate found by a prototype CAD system. 3 features were selected for each of 7 SVM classifiers which were then combined to form a committee of SVMs classifier. Similarly, features …

Automatic Speaker Identification Using Reusable And Retrainable Binary-Pair Partitioned Neural Networks, Ashutosh Mishra

Electrical & Computer Engineering Theses & Dissertations

This thesis presents an extension of the work previously done on speaker identification using Binary Pair Partitioned (BPP) neural networks. In the previous work, a separate network was used for each pair of speakers in the speaker population. Although the basic BPP approach did perform well and had a simple underlying algorithm, it had the obvious disadvantage of requiring an extremely large number of networks for speaker identification with large speaker populations. It also requires training of networks proportional to the square of the number of speakers under consideration, leading to a very large number of networks to be trained …

Warcraft Iii - Maps & Benchmark Problems, Nathan R. Sturtevant, Blizzard Corp.

Moving AI Lab: 2D Maps and Benchmark Problems

Maps extracted from Warcraft III from Blizzard Corp. for use and distribution as benchmark problems.

Contains 36 maps and benchmark problem sets, scaled to 512x512 and converted to a simple grid-based format.

Baldur's Gate Ii - Maps & Benchmark Problems, Nathan R. Sturtevant, Bioware Corp

Moving AI Lab: 2D Maps and Benchmark Problems

Maps extracted by Yngvi Björnsson from Baldur's Gate II with explicit permission from BioWare Corp. for use and distribution as benchmark problems.

Contains 75 maps and benchmark problem sets scaled to 512 x 512 and 120 original scale maps.

Applications Of Fuzzy Logic To Software Model, Yahaya Yuhanim Hani

Student Works (2000-2009)

This thesis investigates the use of fuzzy logic approach in software metrics application. Software metrics defines a standard way of measuring the properties of software products, development processes and resources. The most common application of software metrics is the software cost estimation where it predicts the effort required for completing certain stages of a software development life cycle. The ability to obtain an accurate effort prediction is essential for the cost estimation process, as it helps a project manager to specify the efforts needed for project development. In relation to this, cost estimation models such as COCOMO (Constructive Cost Model), …

Newton Parameter Update Algorithm For Recurrent Neural Networks Applied To Adaptive System Identification And Control, Donald Allen Gates

Electrical & Computer Engineering Theses & Dissertations

This paper shows that the combination of a second-order neural network parameter update algorithm and internal network feedback can be effectively used for adaptive, nonlinear, dynamical system identification and control. Adaptive neural identification and control algorithms are typically utilized for real-time applications where the rate of adaptation is often critical. A fast, adaptive network parameter update algorithm is presented.

Simulation results show that this algorithm is capable of quickly identifying and adapting to changes in system parameters, making it feasible to use for real-time control and fault accommodation applications.

Starcraft - Maps & Benchmark Problems, Nathan R. Sturtevant, Blizzard Corp.

Moving AI Lab: 2D Maps and Benchmark Problems

Maps extracted from Starcraft from Blizzard Corp. for use and distribution as benchmark problems.

Contains 75 maps and benchmark problem sets, converted to standard format by Dave Churchill and post-processed to remove all but the largest connected component.