Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques,
2011
Old Dominion University
Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie
Electrical & Computer Engineering Faculty Publications
In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …
Enhancement Technique For Aerial Images,
2011
Old Dominion University
Enhancement Technique For Aerial Images, Sertan Erkanli, Ahmet Gungor Pakfiliz, Jiang Li
Electrical & Computer Engineering Faculty Publications
Recently, we proposed an enhancement technique for uniformly and non-uniformly illuminated dark images that provides high color accuracy and better balance between the luminance and the contrast in images to improve the visual representations of digital images. In this paper we define an improved version of the proposed algorithm to enhance aerial images in order to reduce the gap between direct observation of a scene and its recorded image.
Eeg Artifact Removal Using A Wavelet Neural Network,
2011
Old Dominion University
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.
Automatic Detection Of Aircraft Emergency Landing Sites,
2011
Old Dominion University
Automatic Detection Of Aircraft Emergency Landing Sites, Yu-Fei Shen, Zia-Ur Rahman, Dean Krusienski, Jiang Li, Zia-Ur Rahman (Ed.), Stephen E. Reichenbach (Ed.), Mark Allen Neifeld (Ed.)
Electrical & Computer Engineering Faculty Publications
An automatic landing site detection algorithm is proposed for aircraft emergency landing. Emergency landing is an unplanned event in response to emergency situations. If, as is unfortunately usually the case, there is no airstrip or airfield that can be reached by the un-powered aircraft, a crash landing or ditching has to be carried out. Identifying a safe landing site is critical to the survival of passengers and crew. Conventionally, the pilot chooses the landing site visually by looking at the terrain through the cockpit. The success of this vital decision greatly depends on the external environmental factors that can impair …
Checking The Feasibility Of Dial-A-Ride Instances Using Constraint Programming,
2010
Melbourne Business School
Checking The Feasibility Of Dial-A-Ride Instances Using Constraint Programming, Gerardo Berbeglia, Gilles Pesant, Louis-Martin Rousseau
Gerardo Berbeglia
No abstract provided.
K-Anonymity In The Presence Of External Databases,
2010
Singapore Management University
K-Anonymity In The Presence Of External Databases, Dimitris Sacharidis, Kyriakos Mouratidis, Dimitris Papadias
Kyriakos MOURATIDIS
The concept of k-anonymity has received considerable attention due to the need of several organizations to release microdata without revealing the identity of individuals. Although all previous k-anonymity techniques assume the existence of a public database (PD) that can be used to breach privacy, none utilizes PD during the anonymization process. Specifically, existing generalization algorithms create anonymous tables using only the microdata table (MT) to be published, independently of the external knowledge available. This omission leads to high information loss. Motivated by this observation we first introduce the concept of k-join-anonymity (KJA), which permits more effective generalization to reduce the …
Aggregate Nearest Neighbor Queries In Spatial Databases,
2010
Hong Kong University of Science and Technology
Aggregate Nearest Neighbor Queries In Spatial Databases, Dimitris Papadias, Yufei Tao, Kyriakos Mouratidis, Chun Kit Hui
Kyriakos MOURATIDIS
Given two spatial datasets P (e.g., facilities) and Q (queries), an aggregate nearest neighbor (ANN) query retrieves the point(s) of P with the smallest aggregate distance(s) to points in Q. Assuming, for example, n users at locations q1,...qn, an ANN query outputs the facility p belongs to P that minimizes the sum of distances |pqi| for 1 is less than or equal to i is less than or equal to n that the users have to travel in order to meet there. Similarly, another ANN query may report the point p belongs to P that minimizes the maximum distance that …
Group Nearest Neighbor Queries,
2010
Hong Kong University of Science and Technology
Group Nearest Neighbor Queries, Dimitris Papadias, Qiongmao Shen, Yufei Tao, Kyriakos Mouratidis
Kyriakos MOURATIDIS
Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q1 , q2 and q3 that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of Euclidean distances |pqi| for 1 ≤i ≤3. Assuming that Q fits in memory and P is indexed by an R-tree, we propose several algorithms for finding the group nearest neighbors efficiently. As a second step, …
Constrained Shortest Path Computation,
2010
Hong Kong University of Science and Technology
Constrained Shortest Path Computation, Manolis Terrovitis, Spiridon Bakiras, Dimitris Papadias, Kyriakos Mouratidis
Kyriakos MOURATIDIS
This paper proposes and solves a-autonomy and k-stops shortest path problems in large spatial databases. Given a source s and a destination d, an aautonomy query retrieves a sequence of data points connecting s and d, such that the distance between any two consecutive points in the path is not greater than a. A k-stops query retrieves a sequence that contains exactly k intermediate data points. In both cases our aim is to compute the shortest path subject to these constraints. Assuming that the dataset is indexed by a data-partitioning method, the proposed techniques initially compute a sub-optimal path by …
Advanced Topics On State Complexity Of Combined Operations,
2010
The University of Western Ontario
Advanced Topics On State Complexity Of Combined Operations, Yuan Gao
Electronic Thesis and Dissertation Repository
State complexity is a fundamental topic in formal languages and automata theory. The study of state complexity is also strongly motivated by applications of finite automata in software engineering, programming languages, natural language and speech processing and other practical areas. Since many of these applications use automata of large sizes, it is important to know the number of states of the automata. In this thesis, we firstly discuss the state complexities of individual operations on regular languages, including union, intersection, star, catenation, reversal and so on. The state complexity of an operation on unary languages is usually different from that …
Random Automata Networks: Why Playing Dice Is Not A Vice,
2010
Portland State University
Random Automata Networks: Why Playing Dice Is Not A Vice, Christof Teuscher
Systems Science Friday Noon Seminar Series
Random automata networks consist of a set of simple compute nodes interacting with each other. In this generic model, one or multiple model parameters, such as the the node interactions and/or the compute functions, are chosen at random. Random Boolean Networks (RBNs) are a particular case of discrete dynamical automata networks where both time and states are discrete. While traditional RBNs are generally credited to Stuart Kauffman (1969), who introduced them as simplified models of gene regulation, Alan Turing proposed unorganized machines as early as 1948. In this talk I will start with Alan Turing's early work on unorganized machines, …
Optimized Algorithms For Predictive Range And Knn Queries On Moving Objects,
2010
Singapore Management University
Optimized Algorithms For Predictive Range And Knn Queries On Moving Objects, Rui Zhang, H.V. Jagadish, Bing Tian Dai, Kotagiri Ramamohanarao
Research Collection School Of Computing and Information Systems
There have been many studies on management of moving objects recently. Most of them try to optimize the performance of predictive window queries. However, not much attention is paid to two other important query types: the predictive range query and the predictive k nearest neighbor query. In this article, we focus on these two types of queries. The novelty of our work mainly lies in the introduction of the Transformed Minkowski Sum, which can be used to determine whether a moving bounding rectangle intersects a moving circular query region. This enables us to use the traditional tree traversal algorithms to …
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment,
2010
Singapore Management University
Topical Summarization Of Web Videos By Visual-Text Time-Dependent Alignment, Song Tan, Hung-Khoon Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Search engines are used to return a long list of hundreds or even thousands of videos in response to a query topic. Efficient navigation of videos becomes difficult and users often need to painstakingly explore the search list for a gist of the search result. This paper addresses the challenge of topical summarization by providing a timeline-based visualization of videos through matching of heterogeneous sources. To overcome the so called sparse-text problem of web videos, auxiliary information from Google context is exploited. Google Trends is used to predict the milestone events of a topic. Meanwhile, the typical scenes of web …
Distributed Energy-Conserving Routing Protocols,
2010
Dartmouth College
Distributed Energy-Conserving Routing Protocols, Qun Li, Javed A. Aslam, Daniela Rus
Javed A. Aslam
This paper discusses several distributed power-aware routing protocols in wireless ad-hoc networks (especially sensor networks). We seek to optimize the lifetime of the network. We have developed three distributed power-aware algorithms and analyzed their efficiency in terms of the number of message broadcasts and the overall network lifetime modeled as the time to the first message that can not be sent. These are: (1) a distributed min Power algorithm (modeled on a distributed version of Dijkstra's algorithm), (2) a distributed max-min algorithm, and (3) the distributed version of our the centralized online max-min zPmin algorithm presented in [12]. The first …
Morphogrammatics Of Reflection,
2010
ThinkArt Lab Glasgow
Morphogrammatics Of Reflection, Rudolf Kaehr
Rudolf Kaehr
Turning back from the studies of morphogrammatics to some open questions of reflectional programming, the recountered problematics might be put into a different light and new methods of handling formal aspects of reflection and reflectionality shall be introduced. Albeit the use of light-metaphors, morphogrammatic reflection is not sketched along the paradigm of optical metaphors. Morphograms are presenting neither propositions nor perceptions able for mirroring (representation). Exercises in defining morphogrammatic retro-grade recursion and reflection schemata are continued from the paper “Sketches to Morphogrammatic Programming”.
Program Transformations For Information Personalization,
2010
University of Dayton
Program Transformations For Information Personalization, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Personalization constitutes the mechanisms necessary to automatically customize information content, structure, and presentation to the end user to reduce information overload. Unlike traditional approaches to personalization, the central theme of our approach is to model a website as a program and conduct website transformation for personalization by program transformation (e.g., partial evaluation, program slicing). The goal of this paper is study personalization through a program transformation lens and develop a formal model, based on program transformations, for personalized interaction with hierarchical hypermedia. The specific research issues addressed involve identifying and developing program representations and transformations suitable for classes of hierarchical …
A Hoare Calculus For Graph Programs,
2010
Singapore Management University
A Hoare Calculus For Graph Programs, Christopher M. Poskitt, Detlef Plump
Research Collection School Of Computing and Information Systems
We present Hoare-style axiom schemata and inference rules for verifying the partial correctness of programs in the graph programming language GP. The pre- and postconditions of this calculus are the nested conditions of Habel, Pennemann and Rensink, extended with expressions for labels in order to deal with GP’s conditional rule schemata and infinite label alphabet. We show that the proof rules are sound with respect to GP’s operational semantics.
The Maximum Clique Problem: Algorithms, Applications, And Implementations,
2010
University of Tennessee, Knoxville
The Maximum Clique Problem: Algorithms, Applications, And Implementations, John David Eblen
Doctoral Dissertations
Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily …
Cloud Storage And Online Bin Packing,
2010
University of Nevada, Las Vegas
Cloud Storage And Online Bin Packing, Swathi Venigella
UNLV Theses, Dissertations, Professional Papers, and Capstones
Cloud storage is the service provided by some corporations (such as Mozy and Carbonite) to store and backup computer files. We study the problem of allocating memory of servers in a data center based on online requests for storage. Over-the-net data backup has become increasingly easy and cheap due to cloud storage. Given an online sequence of storage requests and a cost associated with serving the request by allocating space on a certain server one seeks to select the minimum number of servers as to minimize total cost. We use two different algorithms and propose a third algorithm; we show …
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering,
2010
Singapore Management University
Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval And Clustering, Steven C. H. Hoi, Wei Liu, Shih-Fu Chang
Research Collection School Of Computing and Information Systems
Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of Semi-Supervised Distance Metric Learning for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are …
