Computer Sciences Commons^™

Maximizing Resource Sharing In Wdm Mesh Networks With Path-Based Protection And Sparse Oeo Regeneration, Xi Yang, Byrav Ramamurthy, Lu Shen

School of Computing: Conference and Workshop Papers

We propose a resource-sharing scheme that supports three kinds of sharing scenarios in a WDM mesh network with path-based protection and sparse OEO regeneration. Several approaches are used to maximize the sharing of wavelength-links and OEO regenerators.

Building Desktop Applications With Web Service In A Message-Based Mvc Paradigm”, To Appear, Xiaohong Qiu

Electrical Engineering and Computer Science - All Scholarship

Over the past decade, classic client side applications with Model-View-Controller (MVC) architecture haven’t changed much but become more complex. In this paper, we present an approach of building desktop applications with Web Services in an explicit message-based MVC paradigm. By integrating with our publish/subscribe messaging middleware, it makes SVG browser (a Microsoft PowerPoint like client application) with Web Service style interfaces universally accessible from different client platforms ─ Windows, Linux, MacOS, PalmOS and other customized ones. Performance data suggests that this scheme of building application around messages is a practical architecture for the next generation Web application client.

A Key Management Scheme For Wireless Sensor Networks Using Deployment Knowledge, Wenliang Du, Jing Deng, Yunghsiang S. Han, Shigang Chen

Electrical Engineering and Computer Science - All Scholarship

To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is non-trivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key predistribution scheme and …

Uncheatable Grid Computing, Wenliang Du, Jing Jia, Manish Mangal, Mummoorthy Murugesan

Electrical Engineering and Computer Science - All Scholarship

Grid computing is a type of distributed computing that has shown promising applications in many fields. A great concern in grid computing is the cheating problem described in the following: a participant is given D = {x1,...,xn}, it needs to compute f(x) for all x ∈ D and return the results of interest to the supervisor. How does the supervisor efficiently ensure that the participant has computed f(x) for all the inputs in D, rather than a subset of it? If participants get paid for conducting the task, there are incentives for cheating. In this paper, we propose a novel …

Reconstructability Analysis With Fourier Transforms, Martin Zwick

Complex Systems Faculty Publications and Presentations

Fourier methods used in two‐ and three‐dimensional image reconstruction can be used also in reconstructability analysis (RA). These methods maximize a variance‐type measure instead of information‐theoretic uncertainty, but the two measures are roughly collinear and the Fourier approach yields results close to that of standard RA. The Fourier method, however, does not require iterative calculations for models with loops. Moreover, the error in Fourier RA models can be assessed without actually generating the full probability distributions of the models; calculations scale with the size of the data rather than the state space. State‐based modeling using the Fourier approach is also …

A Generic Oo Architecture Language For Semantics Analysis Of Oo Specification, Xiaoqing Frank Liu

Computer Science Faculty Research & Creative Works

Formal specification enables a rigorous analysis and model checking for ensuring the correctness of specification. Formal OO specification methods are of mathematical nature and the semantics of specification is purposely defined such that it is not related to the semantics of code. We propose a new language, which will lay a common semantics ground for both specification and code.

Distributed Hybrid Agent Based Intrusion Detection And Real Time Response System, Vaidehi Kasarekar, Byrav Ramamurthy

School of Computing: Conference and Workshop Papers

Wireless LANs are growing rapidly and security has always been a concern. We have implemented a hybrid system, which will not only detect active attacks like identity theft causing denial of service attacks, but will also detect the usage of access point discovery tools. The system responds in real time by sending out an alert to the network administrator.

A Load-Balancing Shared-Protection-Path Reconfiguration Approach In Wdm Wavelength-Routed Networks, Lu Shen, Xi Yang, Byrav Ramamurthy

School of Computing: Conference and Workshop Papers

Wavelength-routed networks (WRN) are very promising candidates for next-generation Internet and telecommunication backbones. In such a network, optical-layer protection is of paramount importance due to the risk of losing large amounts of data under a failure. To protect the network against this risk, service providers usually provide a pair of risk-independent working and protection paths for each optical connection. However, the investment made for the optical-layer protection increases network cost. To reduce the capital expenditure, service providers need to efficiently utilize their network resources. Among all the existing approaches, shared-path protection has proven to be practical and cost-efficient [1]. In …

Imsh: An Iterative Heuristic For Srlg Diverse Routing In Wdm Mesh Networks, Ajay Todimala, Byrav Ramamurthy

School of Computing: Conference and Workshop Papers

Survivable routing of a connection involves computation of a pair of diverse routes such that at most one mute fails when failures occur in the network topology. A subset of links in the network that share the risk of failure at the same time are said to belong to a Shared Risk Link Group (SRLG) [3]. A network with shared risk link groups defined over its links is an SRLG network. A failure of an SRLG is equivalent to the failure of all the links in the SRLG. For a connection to he survivable in an SRLG network its working …

Survivable Traffic Grooming With Differentiated End-To-End Availability Guarantees In Wdm Mesh Networks, Wang Yao, Byrav Ramamurthy

School of Computing: Conference and Workshop Papers

Traffic grooming is critical in WDM optical metropolitan area networks (MANS), where low-rate connections are packed onto high-rate wavelength paths (lightpaths). Various applications in the MAN demand different levels of reliability. Therefore, it is necessary to provision connections with differentiated reliability guarantees in the MAN. In this paper, we first present an analytical model to calculate the availability of connections using different protection schemes in WDM optical MANs with general mesh topologies. Then we propose and simulate two grooming algorithms which can provision availability guaranteed connections based on per-connection requirements.

A Gcd Attack Resistant Crthacs For Secure Group Communications, Xukai Zou, Byrav Ramamurthy, Spyros S. Magliveras

School of Computing: Conference and Workshop Papers

In this paper, we propose an improved CRTHACS scheme for secure group communications. The scheme resists several GCD attacks which exist in the original CRTHACS scheme [2] and were recently reported in [1].

Infrastructure Support For Controlled Experimentation With Software Testing And Regression Testing Techniques, Hyunsook Do, Sebastian Elbaum, Gregg Rothermel

School of Computing: Conference and Workshop Papers

Where the creation, understanding, and assessment of software testing and regression testing techniques are concerned, controlled experimentation is an indispensable research methodology. Obtaining the infrastructure necessary to support such experimentation, however, is difficult and expensive. As a result, progress in experimentation with testing techniques has been slow, and empirical data on the costs and effectiveness of techniques remains relatively scarce. To help address this problem, we have been designing and constructing infrastructure to support controlled experimentation with testing and regression testing techniques. This paper reports on the challenges faced by researchers experimenting with testing techniques, including those that inform the …

An Extended Kernel For Generalized Multiple-Instance Learning, Qingping Tao, Stephen Scott, N. V. Vinodchandran, Thomas Takeo Osugi, Brandon Mueller

School of Computing: Conference and Workshop Papers

The multiple-instance learning (MIL) model has been successful in areas such as drug discovery and content-based image-retrieval. Recently, this model was generalized and a corresponding kernel was introduced to learn generalized MIL concepts with a support vector machine. While this kernel enjoyed empirical success, it has limitations in its representation. We extend this kernel by enriching its representation and empirically evaluate our new kernel on data from content-based image retrieval, biological sequence analysis, and drug discovery. We found that our new kernel generalized noticeably better than the old one in content-based image retrieval and biological sequence analysis and was slightly …

Maintaining Stability With Distributed Generation In A Restructured Industry, Judith Cardell, Marija Ilic

Engineering: Faculty Publications

A set of reduced order, linearized, dynamic models for distributed generators is developed along with a framework for modeling the generators in a power distribution system. Analysis of this distributed system structure raises two issues. The first is that the simulations demonstrate, unexpectedly, that a small load disturbance is capable of causing frequency instability in the primary dynamics of the distributed generators. Eigenanalysis of the instability suggests that it is a system phenomenon. The second issue is that the system matrix is found to not have a block diagonal dominant structure raising questions over the possible implementation of decentralized control …

Statistical Procedures For Evaluating Daily And Monthly Hydrologic Model Predictions, Marilyn E. Coffey, Stephen R. Workman, Joseph L. Taraba, Alex W. Fogle

Biosystems and Agricultural Engineering Faculty Publications

The overall study objective was to evaluate the applicability of different qualitative and quantitative methods for comparing daily and monthly SWAT computer model hydrologic streamflow predictions to observed data, and to recommend statistical methods for use in future model evaluations. Statistical methods were tested using daily streamflows and monthly equivalent runoff depths. The statistical techniques included linear regression, Nash-Sutcliffe efficiency, nonparametric tests, t-test, objective functions, autocorrelation, and cross-correlation. None of the methods specifically applied to the non-normal distribution and dependence between data points for the daily predicted and observed data. Of the tested methods, median objective functions, sign test, autocorrelation, …

Approximating First-Order Logic Programs By Feedforward Networks, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We want to apply Funahashi's theorem in order to approximate the T_P operator for first-order (normal) logic programs P via 3-layer feedforward networks. I.e. we need to understand T_P as a continuous function on the reals.

We will need to study some preliminaries from set-theoretic topology first result from [HS00, HHS0_x], which extends results from [HKS99]. We close with some further considerations about the methods and results.

Semantic Integration Of Glycomics Data And Information, William S. York, Amit P. Sheth, Krzysztof J. Kochut, John A. Miller, Christopher Thomas, Karthik Gomadam, X. Yi, Meenakshi Nagarajan

Kno.e.sis Publications

No abstract provided.

Discovering And Ranking Semantic Associations Over A Large Rdf Metabase, Christian Halaschek-Wiener, Boanerges Aleman-Meza, I. Budak Arpinar, Amit P. Sheth

Kno.e.sis Publications

Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this data is becoming an active research topic. Just as ranking of documents is a critical component of today's search engines, the ranking of relationships will be essential in tomorrow's semantic analytics engines. Building upon our recent work on specifying these semantic relationships, which we refer to as Semantic Associations, we demonstrate a system where these associations are discovered among a large semantic metabase represented in RDF. Additionally we employ ranking techniques to provide …

Art Network Discussion, Paul Thomas

Creative Connections Symposium @ BEAP2004 September 4, 2004

No abstract provided.

A Generalised Feedforward Neural Network Architecture And Its Applications To Classification And Regression, Ganesh Arulampalam

Theses: Doctorates and Masters

Shunting inhibition is a powerful computational mechanism that plays an important role in sensory neural information processing systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired networks where the basic synaptic computations are based on shunting inhibition. SIANNs were designed to solve difficult machine learning problems by exploiting the inherent non-linearity mediated by shunting inhibition. The aim was to develop powerful, trainable networks, with non-linear decision surfaces, for classification …

Contrast Enhancement Of Ultrasound Images Using Shunting Inhibitory Cellular Neural Networks, Murali M. Gogineni

Theses: Doctorates and Masters

Evolving from neuro-biological insights, neural network technology gives a computer system an amazing capacity to actually generate decisions dynamically. However, as the amount of data to be processed increases, there is a demand for developing new types of networks such as Cellular Neural Networks (CNN), to ease the computational burden without compromising the outcomes. The objective of this thesis is to research the capability of Shunting Inhibitory Cellular Neural Networks (SICNN) to solve the clarity problems in ultrasound imaging. In this thesis, we begin by reviewing a number of traditional enhancement techniques and measures. Since the entire work of this …

A Toolkit For Xml-Based And Process-Oriented Application Integration, Volker Münch

Theses

Systems integration is hard work; different architectures with different interfaces and an innumerable amount of Legacy applications with non-standard interfaces exist. However the needs to meet changing business requirements and to implement faster and optimised procedures within an enterprise and in cooperation with other external enterprises mean that there is a continuing demand for such integration strategies and products. Integration was often an activity separate from system development and could not be accomplished without re-involving system designers and developers. Such a process is therefore inefficient and costly. This thesis proposes a process-oriented integration strategy that combines the tasks of integration …

Logistics Outsourcing And 3pl Challenges, Michelle L. F. Cheong

Research Collection School Of Computing and Information Systems

Logistics has been an important part of every economy and every business entity. The worldwide trend in globalization has led to many companies outsourcing their logistics function to Third-Party Logistics (3PL) companies, so as to focus on their core competencies. This paper attempts to broadly identify and categorize the challenges faced by 3PL companies and discover potential gaps for future research. Some of the challenges will be related with the experience and information collected from interviews with two 3PL companies.

Investigation Of Implementation Techniques For Backpropagation Neural Networks., John Culloty

Theses

Artificial neural networks are biologically inspired computational methods that have the ability to approximate discrete, real and vector valued target functions. For some problem domains this ability makes neural networks a more appealing solution than traditional computational techniques. Such problem domains typically contain a large number of parameters that are interrelated in a complex and often unknown manner. This makes rule based solutions all but impossible. However another feature of such problem domains is the presence of large volumes of real world data that can be used to tram a neural network until it learns the solution.

One of the …

Speech Enabled E-Learning Technology For Adult Literacy Tutoring, Jason Meade

Theses

This research presents the work involved in developing a speech-enabled e-Learning prototype for use in literacy tutoring. As the main objective was to develop an interface for literacy learning, initial research concentrated on establishing a framework for literacy e-Leaming through the use of speech technology. Requirements for best practice e-Learning and the relevance of learning theories to an e-Learning application were also investigated. The technologies to facilitate this, such as text to speech technologies and mark-up languages, were addressed during the implementation of speech-enabled prototypes. Both server-side and client-side prototypes were implemented to investigate speech technology. Testing found the server-side …

Investigation Of Implementation Techniques For Backpropagation Neural Networks, John Culloty

Theses

Artificial neural networks are biologically inspired computational methods that have the ability to approximate discrete, real and vector valued target functions. For some problem domains this ability makes neural networks a more appealing solution than traditional computational techniques. Such problem domains typically contain a large number of parameters that are interrelated in a complex and often unknown manner. This makes rule based solutions all but impossible. However another feature of such problem domains is the presence of large volumes of real world data that can be used to tram a neural network until it learns the solution.

One of the …

Discrete-Time Neural Network Output Feedback Control Of Nonlinear Systems In Non-Strict Feedback Form, P. He, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

An adaptive neural network (NN) -Based output feedback controller is proposed to deliver a desired tracking performance for a class of discrete-time nonlinear systems, which is represented in non-strict feedback form. the NN backstepping approach is utilized to design the adaptive output feedback controller consisting of 1) a NN observer to estimate the system states with the input-output data, and 2) two NNs to generate the virtual and actual control inputs, respectively. the non-causal problem in the discrete time backstepping design is avoided by using the universal NN approximator. the persistence excitation (PE) condition is relaxed both in the NN …

Reinforcement Learning-Based Output Feedback Control Of Nonlinear Systems With Input Constraints, P. He, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

A novel neural network (NN) -Based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NNs: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. the magnitude constraints are manifested as saturation nonlinearities in the output feedback …

Distributed Power Control Of Cellular Networks In The Presence Of Channel Uncertainties, Maciej Jan Zawodniok, Q. Shang, Jagannathan Sarangapani

Electrical and Computer Engineering Faculty Research & Creative Works

A novel distributed power control (DPC) scheme for cellular networks in the presence of radio channel uncertainties such as path loss shadowing, and Rayleigh fading is presented. Since these uncertainties can attenuate the received signal strength and can cause variations in the received Signal-to-lnterference ratio (SIR), the proposed DPC scheme maintains a target SIR at the receiver provided the uncertainty is slowly varying with time. The DPC estimates the time varying nature of the channel quickly and uses the information to arrive at a suitable transmitter power value . Further, the standard assumption of a constant interference during a link's …

Interactive Dna Sequence And Structure Design For Dna Nanoapplications, Mingjun Zhang, Chaman Sabharwal, Weimin Tao, Tzyh-Jong Tarn, Ning Xi, Guangyong Li

Computer Science Faculty Research & Creative Works

DNA sequence and structure design is very important for DNA nanoapplications. A computer-aided design tool is needed for exploring DNA sequence and structure of interests before experimental synthesis, which is a time- and labor-consuming process. In this paper, an interactive DNA sequence and structure design software tool called DNA shop is proposed and implemented. The visualization tool can generate DNA structures by specifying, selecting, and moving DNA sequences around and display corresponding structures. Using the tool, DNA sequence and structure can be visually inspected in three-dimensional space before experimental studies.