Artificial Intelligence and Robotics Commons^™

Scale Invariant Object Recognition Using Cortical Computational Models And A Robotic Platform, Danny Voils

Dissertations and Theses

This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX …

Object Retrieval From Secure Unknown Interior Spaces Using Autonomous Unmanned Aerial Vehicles, John Levous, Julie Hoven, Victor Habgood, Abdulrahman Alotaibi, Brandon Ordway, Garibe Mohammed-Jones, Haole Guo, Filip Cuckov, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

This paper describes an autonomous unmanned aerial vehicle (UAV) designed to participate in the 23rd annual International Aerial Robotics Competition. The UAV is equipped with onboard sensors and a Harvard architecture 8-bit RISC microcontroller to monitor and locally control its flight telemetry. Additional sensors (and an additional microcontroller) are used for detecting and mapping of structural and environmental objects while the UAV is in flight. The microcontrollers are interfaced with wireless communication modules for transmitting flight telemetry and structural/environmental data to a ground control station that sends the UAV command and control signals required for the mission objectives. The UAV …

Model Individualization For Real-Time Operator Functional State Assessment, Guangfan Zhang, Roger Xu, Wei Wang, Aaron A. Pepe, Feng Li, Jiang Li, Frederick Mckenzie, Tom Schnell, Nick Anderson, Dean Heitkamp

Electrical & Computer Engineering Faculty Publications

Proper assessment of Operator Functional State (OFS) and appropriate workload modulation offer the potential to improve mission effectiveness and aviation safety in both overload and under-load conditions. Although a wide range of research has been devoted to building OFS assessment models, most of the models are based on group statistics and little or no research has been directed towards model individualization, i.e., tuning the group statistics based model for individual pilots. Moreover, little emphasis has been placed on monitoring whether the pilot is disengaged during low workload conditions. The primary focus of this research is to provide a real-time engagement …

Simulation, Application, And Resilience Of An Organic Neuromorphic Architecture, Made With Organic Bistable Devices And Organic Field Effect Transistors, Robert A. Nawrocki

Electronic Theses and Dissertations

This thesis presents work done simulating a type of organic neuromorphic architecture, modeled after Artificial Neural Network, and termed Synthetic Neural Network, or SNN. The first major contribution of this thesis is development of a single-transistor-single-organic-bistable-device-per-input circuit that approximates behavior of an artificial neuron. The efficacy of this design is validated by comparing the behavior of a single synthetic neuron to that of an artificial neuron as well as two examples involving a network of synthetic neurons. The analysis utilizes electrical characteristics of polymer electronic elements, namely Organic Bistable Device and Organic Field Effect Transistor, created in the laboratory at …

An Integrated Computer-Aided Robotic System For Dental Implantation, Xiaoyan Sun, Yongki Yoon, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

This paper describes an integrated system for dental implantation including both preoperative planning utilizing computer-aided technology and automatic robot operation during the intra-operative stage. A novel two-step registration procedure was applied for transforming the preoperative plan to the operation of the robot, with the help of a Coordinate Measurement Machine (CMM). Experiments with a patient-specific phantom were carried out to evaluate the registration error for both position and orientation. After adopting several improvements, registration accuracy of the system was significantly improved. Sub-millimeter accuracy with the Target Registration Errors (TREs) of 0.38±0.16 mm (N=5) was achieved. The target orientation errors after …

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.

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 …

Prediction Of Brain Tumor Progression Using A Machine Learning Technique, Yuzhong Shen, Debrup Banerjee, Jiang Li, Adam Chandler, Yufei Shen, Frederic D. Mckenzie, Jihong Wang, Nico Karssemeijer (Ed.), Ronald M. Summers (Ed.)

Electrical & Computer Engineering Faculty Publications

A machine learning technique is presented for assessing brain tumor progression by exploring six patients' complete MRI records scanned during their visits in the past two years. There are ten MRI series, including diffusion tensor image (DTI), for each visit. After registering all series to the corresponding DTI scan at the first visit, annotated normal and tumor regions were overlaid. Intensity value of each pixel inside the annotated regions were then extracted across all of the ten MRI series to compose a 10 dimensional vector. Each feature vector falls into one of three categories:normal, tumor, and normal but progressed to …

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. …

Brain Tumor Progression Assessment Using Multiple Mri Volumes, Yufei Shen

Electrical & Computer Engineering Theses & Dissertations

Predicting and assessing tumor progression is important in brain tumor treatment. We attempt to use machine learning techniques to achieve consistency in assessing brain tumor progression. This thesis presents a prediction method of brain tumor progression by exploring a large MR database, which contains two patients ' complete records covering all their visits in the past two years. All ten MRI series, namely, apparent diffusion coefficient (ADC) , diffusion tensor imaging (DTI) , fractional anisotropy (FA), fluid attenuated inversion recovery (FLAIR), max eigenvalue (MAX), mid eigenvalue (MID), min eigenvalue (MIN) , post-contrast T₁-weighted, T₁- weighted, and …

Tree-D-Seek: A Framework For Retrieving Three-Dimensional Scenes, Saurav Mazumdar

Electrical & Computer Engineering Theses & Dissertations

In this dissertation, a strategy and framework for retrieving 3D scenes is proposed. The strategy is to retrieve 3D scenes based on a unified approach for indexing content from disparate information sources and information levels. The TREE-D-SEEK framework implements the proposed strategy for retrieving 3D scenes and is capable of indexing content from a variety of corpora at distinct information levels. A semantic annotation model for indexing 3D scenes in the TREE-D-SEEK framework is also proposed. The semantic annotation model is based on an ontology for rapid prototyping of 3D virtual worlds.

With ongoing improvements in computer hardware and 3D …

Grey Measurement Based On Rough Set Granule Calculations, Jian Liu, Zhili Huang, Shunxiang Wu, Ruiyi Chen

Electrical and Computer Engineering Faculty Research & Creative Works

This paper put the Rough set methodology based on the information table disposal to expand the dual (binary) relations described by the neighborhood system. To make full use of logic operations including AND operation by-bit, XOR operation by-bit and NOR operation by-bit, gains the certain and uncertain information among the various decision-making factors. The use of knowledge related with Grey system theory, establishes their mathematical model for decision making and data mining, according to the principle of the priority of certain information in decision-making and the ambiguity degree from small to big. At last, a new method for data mining …

Wireless Sensor Network Modeling Using Modified Recurrent Neural Network: Application To Fault Detection, Azzam Issam Moustapha

Doctoral Dissertations

Wireless Sensor Networks (WSNs) consist of a large number of sensors, which in turn have their own dynamics. They interact with each other and the base station, which controls the network. In multi-hop wireless sensor networks, information hops from one node to another and finally to the network gateway or base station. Dynamic Recurrent Neural Networks (RNNs) consist of a set of dynamic nodes that provide internal feedback to their own inputs. They can be used to simulate and model dynamic systems such as a network of sensors.

In this dissertation, a dynamic model of wireless sensor networks and its …

Lightweight Objective Quality Of Voice Estimation Through Machine Learning, Daniel Riordan

Theses

Communication systems are undergoing constant and rapid innovation, both at the design stage and in the field. This in turn has led to an inereasing need for fast, efficient, portable and economic methods for the testing of these systems. For voice carrying communication systems the quality of the transmitted voice that the system produces is a large factor in the overall performance rating of the system. This measure is known as the ‘Quality of Voice’ (QoV) and can be evaluated either subjectively or objectively.

Speech quality is a complex subjective phenomenon that can be best quantified by subjective testing. A …

New Method For Approximating Vague Sets To Fuzzy Sets Based On Voting Model, Jian Liu, Zhizhan Liu, Shunxiang Wu, Yongjian Zhang

Electrical and Computer Engineering Faculty Research & Creative Works

By analyzing Vague Sets voting model, we bring forth a new method for approximating Vague Sets to Fuzzy Sets, and its general process is presented in the article. In a voting model, firstly, we suppose that the abstainers must vote for once more, and the results are close studied. Then the randomicity, uncertainty, and conformity of voting are found. As we know, an abstainer may favor somebody, oppose somebody, or just abstain. In this article, we suppose the distribution of results is consistent with a normal distribution. So, we advance the new approximation method based on Gauss Distribution. © 2008 …

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 …

A Fuzzy Logic Controller For Autonomous Wheeled Vehicles, Mohamed Trabia, Linda Z. Shi, Neil Eugene Hodge

Mechanical Engineering Faculty Research

Autonomous vehicles have potential applications in many fields, such as replacing humans in hazardous environments, conducting military missions, and performing routine tasks for industry. Driving ground vehicles is an area where human performance has proven to be reliable. Drivers typically respond quickly to sudden changes in their environment. While other control techniques may be used to control a vehicle, fuzzy logic has certain advantages in this area; one of them is its ability to incorporate human knowledge and experience, via language, into relationships among the given quantities. Fuzzy logic controllers for autonomous vehicles have been successfully applied to address various …

Learning As A Nonlinear Line Of Attraction For Pattern Association, Classification And Recognition, Ming-Jung Seow

Electrical & Computer Engineering Theses & Dissertations

Development of a mathematical model for learning a nonlinear line of attraction is presented in this dissertation, in contrast to the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete location in state space. A nonlinear line of attraction is the encapsulation of attractive fixed points scattered in state space as an attractive nonlinear line, describing patterns with similar characteristics as a family of patterns.

It is usually of prime imperative to guarantee the convergence of the dynamics of the recurrent network for associative learning and recall. We propose to alter …

Socio-Economic Impacts Of Computer Viruses In Tanzania, M Victor

Tanzania Journal of Engineering and Technology (TJET)

This paper reports on a research project conducted with an objective of identifying and assessing various approaches used by different computer users (Management, System Administrators and end users) in Tanzania to combat computer viruses (CVs), and to assess users' awareness level on CVs. Specifically, the study aimed at assessing the awareness level on CVs to the Tanzanian business community; analyze the socio -economic impact caused by CVs in Tanzania and; assess existing methods, capacity and limitations on controlling CVs in Tanzania. Data was collected using both questionnaires and interview from financial institutions such as NBC and BOT, and telecommunications sector …

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 …

Review Of The Product Development Process And Information Flow In The Manufacturing Industry: Problems And A Possible Wav Forward, E Opiyo

Tanzania Journal of Engineering and Technology (TJET)

No abstract provided.

A Computer-Based Articulation Training Aid For Short Words (Cata), Mukund Devarajan

Electrical & Computer Engineering Theses & Dissertations

Several improvements in the vowel articulation training aid (VATA) are described, as well as the efforts to extend the visual feedback system to operate with short words in the form of consonant, vowel and consonant (CVC). The extended version of the visual feedback system is referred to as CATA (Computer-based Articulation Training Aid); the vowel version of the aid (VATA) only operates with ten American English monopthong vowels. Improvements in VATA include the use of a neural network (NN) recognizer method to prune a large database of vowel recordings to eliminate noisy and/or mispronounced tokens. The spectral jitter problem, previously …

Aurora Working Group: Dsr Front End Lvcsr Evaluation — Baseline Recognition System Description, Naveen Parihar, Joseph Picone

Publications

In this document we describe the features of the baseline system to be used in the Distributed Speech Recognition (DSR) front end large vocabulary continuous speech recognition (LVCSR) evaluations being conducted by the Aurora Working Group of the European Telecommunications Standards Institute (ETSI). The objective of these evaluations is to determine the robustness of different front ends for use in client/server type telecommunications applications. As such, our experiments are designed to test the following focus conditions on the DARPA Wall Street Journal (WSJ0) corpus using a 5000-word closed-loop vocabulary and a bigram language model:

1. Additive Noise: six noise conditions …

Comparison Of Two Distributed Fuzzy Logic Controllers For Flexible-Link Manipulators, Linda Z. Shi, Mohamed Trabia

Mechanical Engineering Faculty Presentations

The paper suggests that fuzzy logic controllers present a computationally efficient and robust alternative to conventional controllers. The paper presents two possible structures for the distributed fuzzy logic controller of a single-link flexible manipulator. A linear quadratic regulator method is used to prove the effectiveness of fuzzy logic controllers.

Fingerprint Recognition Using Neural Networks, Eng Hoe Kennie Yeoh

Student Works (2000-2009)

Traditional methods of fingerprint verification uses either complicated feature detection algorithms that are not specific to each fingerprint, or compare two fingerprint images directly using image processing toots. The former involves very complicated calculations and tedious algorithms, and the latter tend to work poorly. In this paper it is described a new method which takes the middle ground. This paper studies the implementation of the Fast Fourier Transform and Artificial Neural Networks into the recognition of fingerprints. With tests conducted on the implementation of the Fourier Transform as a method of fingerprint feature extraction, the use of the Fourier Transform …

Dynamic Bandwidth Allocation Using Neural-Fuzzy In Atm Network, Yan Sing Chua

Student Works (2000-2009)

Dynamic bandwidth allocation is becoming one of the crucial issues in the design and research in the computer network. This is due to the continuous increasing demand of intensive applications that require more bandwidth while retaining higher quality. Dynamic bandwidth allocation utilises the current network state information to optimise the bandwidth distribution. The state information can be gathered through prediction using past data and measurement on current state. Agility and flexibility of dynamic bandwidth allocation using Neural-Fuzzy has the advantage that it can adapt to the state changes of the network. ATM network carries heterogeneous traffic and this causes the …

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …

Application Of Vibrational Techniques In Determination Of Dynamic Properties Of Agricultural Products-State Of The Arton Of Vibrational Techniques In Determination Of Dynamic Properties Of Agricultural Products-State Of The Art, Silas Kajuna

Tanzania Journal of Engineering and Technology (TJET)

Vibration is one of the techniques employed in the determination of dynamic properties of fruits and vegetables. It entails generation of a mechanical or acoustic vibrational signal which is propagated through the flesh of the agricultural material. A transducer is either attached or held close to the specimen to monitor the propagation of the signal through the specimen. The manner in which the signal is transmitted through the material is analyzed, and the dynamic properties of the specimen which relate to its firmness or its internal being are derived. The technique has been around for the past 30 years or …

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.