Computer Engineering Commons^™

Generating Linear Temporal Logic Formulas For Pattern-Based Specifications, Salamah Salamah, Vladik Kreinovich, Ann Q. Gates

Departmental Technical Reports (CS)

Software property classifications and patterns, i.e., high-level abstractions that describe program behavior, have been used to assist practitioners in specifying properties. The Specification Pattern System (SPS) provides descriptions of a collection of patterns. Each pattern is associated with a scope that defines the extent of program execution over which a property pattern is considered. Based on a selected pattern, SPS provides a specification for each type of scope in multiple formal languages including Linear Temporal Logic (LTL). The Property Specification tool (Prospec) extends SPS by introducing the notion of Composite Propositions (CP), that classify sequential and concurrent behavior over pattern …

Fitting A Normal Distribution To Interval And Fuzzy Data, Gang Xiang, Vladik Kreinovich, Scott Ferson

Departmental Technical Reports (CS)

In traditional statistical analysis, if we know that the distribution is normal, then the most popular way to estimate its mean a and standard deviation s from the data sample x1,...,xn is to equate a and s to the arithmetic mean and sample standard deviation of this sample. After this equation, we get the cumulative distribution function F(x)=F0((x-a)/s) of the desired distribution.

In many practical situations, we only know intervals [xi] that contain the actual (unknown) values of xi or, more generally, a fuzzy number that describes xi. Different values of xi lead, in general, to different values of F(x). …

Under Interval And Fuzzy Uncertainty, Symmetric Markov Chains Are More Difficult To Predict, Roberto Araiza, Gang Xiang, Olga Kosheleva, Damjan Skulj

Departmental Technical Reports (CS)

Markov chains are an important tool for solving practical problems. In particular, Markov chains have been successfully applied in bioinformatics. Traditional statistical tools for processing Markov chains assume that we know the exact probabilities p(i,j) of a transition from the state i to the state j. In reality, we often only know these transition probabilities with interval (or fuzzy) uncertainty. We start the paper with a brief reminder of how the Markov chain formulas can be extended to the cases of such interval and fuzzy uncertainty.

In some practical situations, there is another restriction on the Markov chain--that this Markov …

Towards Combining Probabilistic, Interval, Fuzzy Uncertainty, And Constraints: On The Example Of Inverse Problem In Geophysics, George R. Keller, Scott A. Starks, Aaron Velasco, Matthew Averill, Roberto Araiza, Gang Xiang, Vladik Kreinovich

Departmental Technical Reports (CS)

In many real-life situations, we have several types of uncertainty: measurement uncertainty can lead to probabilistic and/or interval uncertainty, expert estimates come with interval and/or fuzzy uncertainty, etc. In many situations, in addition to measurement uncertainty, we have prior knowledge coming from prior data processing, prior knowledge coming from prior interval constraints. In this paper, on the example of the seismic inverse problem, we show how to combine these different types of uncertainty.

Adding Constraints To Situations When, In Addition To Intervals, We Also Have Partial Information About Probabilities, Martine Ceberio, Vladik Kreinovich, Gang Xiang, Scott Ferson, Cliff Joslyn

Departmental Technical Reports (CS)

In many practical situations, we need to combine probabilistic and interval uncertainty. For example, we need to compute statistics like population mean E=(x1+...+xn)/n or population variance V=(x1^2+...+xn^2)/n-E^2 in the situations when we only know intervals [xi] of possible values of xi. In this case, it is desirable to compute the range of the corresponding characteristic.

Some range computation problems are NP-hard; for these problems, in general, only an enclosure is possible. For other problems, there are efficient algorithms. In many practical situations, we have additional information that can be used as constraints on possible cumulative distribution functions (cdfs). For example, …

Decomposable Aggregability In Population Genetics And Evolutionary Computations: Algorithms And Computational Complexity, Vladik Kreinovich, Max Shpak

Departmental Technical Reports (CS)

Many dynamical systems are decomposably aggregable in the sense that one can divide their (micro)variables x1,...,xn into several (k) non-overlapping blocks and find combinations y1,...,yk of variables from these blocks (macrovariables) whose dynamics depend only on the initial values of the macrovariables. For example, the state of a biological population can be described by listing the frequencies xi of different genotypes i; in this example, the corresponding functions fi(x1,...,xn) describe the effects of mutation, recombination, and natural selection in each generation.

Another example of a system where detecting aggregability is important is a one that describes the dynamics of an …

Why Intervals? Why Fuzzy Numbers? Towards A New Justification, Vladik Kreinovich

Departmental Technical Reports (CS)

The purpose of this paper is to present a new characterization of the set of all intervals (and of the corresponding set of fuzzy numbers). This characterization is based on several natural properties useful in mathematical modeling; the main of these properties is the necessity to be able to combine (fuse) several pieces of knowledge.

Fern: An Updatable Authenticated Dictionary Suitable For Distributed Caching, Eric Freudenthal, David Herrera, Steve Gutstein, Ryan Spring, Luc Longpre

Departmental Technical Reports (CS)

Fern is an updatable cryptographically authenticated dictionary developed to propagate identification and authorization information within and among distributed systems. Conventional authenticated dictionaries permit authorization information to be disseminated by untrusted proxies, however these proxies must maintain full duplicates of the dictionary structure. In contrast, Fern incrementally distributes components of its dictionary as required to satisfy client requests and thus is suitable for deployments where clients are likely to require only a small fraction of a dictionary's contents and connectivity may be limited.

When dictionary components must be obtained remotely, the latency of lookup and validation operations is dominated by communication …

Towards Interval Techniques For Processing Educational Data, Olga Kosheleva, Vladik Kreinovich, Luc Longpre, Mourat Tchoshanov, Gang Xiang

Departmental Technical Reports (CS)

There are many papers that experimentally compare effectiveness of different teaching techniques. Most of these papers use traditional statistical approach to process the experimental results. The traditional statistical approach is well suited to numerical data but often, what we are processing is intervals (e.g., A means anything from 90 to 100). We show that the use of interval techniques leads to more adequate processing of educational data.

Architectural Assertions: Checking Architectural Constraints At Run-Time, Hyotaeg Jung, Carlos E. Rubio-Medrano, Eric Wong, Yoonsik Cheon

Departmental Technical Reports (CS)

The inability to express architectural concepts and constraints explicitly in implementation code invites the problem of architectural drift and corrosion. We propose runtime checks as a solution to mitigate this problem. The key idea of our approach is to express architectural constraints or properties in an assertion language and use the runtime assertion checker of the assertion language to detect any violations of the constraints. The architectural assertions are written in terms of architectural concepts such as components, connectors, and configurations, and thus they can be easily mapped to or traced back to the original high-level constraints written in an …

Towards A General Description Of Interval Multiplications: Algebraic Analysis And Its Relation To T-Norms, Olga Kosheleva, Guenter Mayer, Vladik Kreinovich

Departmental Technical Reports (CS)

It is well known that interval computations are very important, both by themselves (as a method for processing data known with interval uncertainty) and as a way to process fuzzy data. In general, the problem of computing the range of a given function under interval uncertainty is computationally difficult (NP-hard). As a result, there exist different methods for estimating such a range: some methods require a longer computation time and lead to more accurate results, other methods lead to somewhat less accurate results but are much faster than the more accurate techniques. In particular, different methods exist for interval multiplication, …

Throttling I/O Streams To Accelerate File-I/O Performance, Seetharami Seelam, Andre Kerstens, Patricia J. Teller

Departmental Technical Reports (CS)

To increase the scale and performance of scientific applications, scientists commonly distribute computation over multiple processors. Often without realizing it, file I/O is parallelized with the computation. An implication of this I/O parallelization is that multiple compute tasks are likely to concurrently access the I/O nodes of an HPC system. When a large number of I/O streams concurrently access an I/O node, I/O performance tends to degrade. In turn, this impacts application execution time.

This paper presents experimental results that show that controlling the number of synchronous file-I/O streams that concurrently access an I/O node can enhance performance. We call …

Von Mises Failure Criterion In Mechanics Of Materials: How To Efficiently Use It Under Interval And Fuzzy Uncertainty, Gang Xiang, Andrzej Pownuk, Olga Kosheleva, Scott A. Starks

Departmental Technical Reports (CS)

One of the main objective of mechanics of materials is to predict when the material experiences fracture (fails), and to prevent this failure. With this objective in mind, it is desirable to use {it ductile} materials, i.e., materials which can sustain large deformations without failure. Von Mises criterion enables us to predict the failure of such ductile materials. To apply this criterion, we need to know the exact stresses applied at different directions. In practice, we only know these stresses with interval or fuzzy uncertainty. In this paper, we describe how we can apply this criterion under such uncertainty, and …

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 …

Cs 480/680: Comparative Languages, Krishnaprasad Thirunarayan

Computer Science & Engineering Syllabi

This course will introduce fundamental concepts and paradigms underlying the design of modem programming languages. For concreteness, we study the details of an object-oriented language (e.g. Java l, and a functional language (e.g., Scheme). The overall goal is to enable comparison and evaluation of existing languages. The programming assignments will be coded in Java 5 and in Scheme.

Cs 141: Computer Programming - I, Michael Ondrasek

Computer Science & Engineering Syllabi

This course provides a general introduction to the fundamentals of computer programming. Examples from and applications to a broad range of problems are given. No prior knowledge of programming is assumed. The concepts covered will be applied to the Java programming language. Students must register for both lecture and one laboratory section. 4 credit hours. Prerequisite: MTH 127 (College Algebra) or equivalent.

Cs 208: Computer Programming For Business I, Dennis Kellermeier

Computer Science & Engineering Syllabi

CS 208 is the first of a two quarter sequence in programming for business students. It is required for Management Information Science majors. The courses are designed to help students achieve a high degree of facility in intermediate level programming. This course assumes students have never written a program before.

Cs 790-02: Optimizing Compliers For Modern Architectures, Meilin Liu

Computer Science & Engineering Syllabi

This course studies compiler optimization for modem architectures. Between parsing the input program and generating the target machine code, optimizing compilers perform a wide range of program transformations on a program to improve its performance. In this course we focus on data dependence analysis, loop transformations, loop scheduling, cache management, and a combination of these optimizing techniques.

Cs 801: Advanced Database Systems, Soon M. Chung

Computer Science & Engineering Syllabi

No abstract provided.

Cs 865: Advanced Topics In Soft Computing, Michael L. Raymer

Computer Science & Engineering Syllabi

No abstract provided.

Cs 205-04, 05, 06: Introduction To Computers And Office Productivity Software, Terri Bauer

Computer Science & Engineering Syllabi

Focus on learning MS Office software applications including word processing (intermediate), spreadsheets, database and presentation graphics using a case study approach where critical thinking and problem solving skills are required. Computer concepts are integrated throughout the course to provide an understanding of the basics of computing, the latest technological advances and how they are used in industry. Ethics and issues encountered in business are discussed to challenge students on societal impact of technology.

Cs 214: Visual Basic Programming, Michael Ondrasek

Computer Science & Engineering Syllabi

This course provides a general introduction to the fundamentals of object computer programming. Examples from and applications to a broad range of problems are given. No prior knowledge of programming is assumed. However, students should have a familiarity with programming concepts. The concepts covered will be applied in the Visual Basic programming language. 4 credit hours.

Cs 207: Advanced Office Productivity Ii, Amanda Hood

Computer Science & Engineering Syllabi

This course covers post-advanced microcomputer applications including Microsoft Office Word 2003, Excel 2003, Access 2003, and PowerPoint 2003. Topics include: creating an online form, working with macros and Visual Basic for Applications (VBA), working with a master document, and index and a table of contents, linking an Excel worksheet and charting its data in Word, formula auditing, data validation, and complex problem solving in Excel, importing data into Excel, working with PivotCharts, PivotTables, and trendlines in Excel, creating a PivotTable List, advanced Access report and form techniques, and creating a multi-page form, administering a database system, creating a self-running presentation …

Cs 400/600: Data Structures And Software Design, Natsuhiko Futamura

Computer Science & Engineering Syllabi

In this course, students will learn basic data structures and how to design and analyze and implement software. Course covers introduction to the fundamentals of complexity and analysis and study of common problems and solutions using various data structures. After taking this course, students are expected to be able to design reasonable software for problems and estimate (evaluate) the performance of them even without writing the software.

Cs 415: Social Implications Of Computing, Leo Finkelstein

Computer Science & Engineering Syllabi

CS 415 is a communication skills course using as its subject matter current salient issues associated with the social implications of computing. In addition to the course text, you will need to use certain reading materials in the library and elsewhere, and you will be responsible for using concepts and theories provided in class lectures and discussions.

Cs 701: Database Systems And Design I, Guozhu Dong

Computer Science & Engineering Syllabi

An introduction to database design, database system implementation issues and techniques, and advanced data models.

Cs 790-01: Multimedia Coding And Communication (Ii), Yong Pei

Computer Science & Engineering Syllabi

No abstract provided.

Cs 340: Programming Language Workshop In C#, Krishnaprasad Thirunarayan

Computer Science & Engineering Syllabi

This course is designed as a self-study in C#. You are expected to learn the language and solve a set of programming problems assigned to you using MS Visual Studio .NET. There are no exams. We officially meet only once in the quarter. However, I will be available in the posted office hours for clarifications and discussions about the programming problems.

Cs 242: Computer Programming Iii, Mateen M. Rizki

Computer Science & Engineering Syllabi

No abstract provided.

Cs 776: Functional Programming, Krishnaprasad Thirunarayan

Computer Science & Engineering Syllabi

This course will discuss important concepts of functional programming such as recursive definitions, higher-order functions, type inference, polymorphism, abstract data types, modules etc. The programming exercises will illustrate the utility of list-processing, pattern matching, abstraction of data/control, strong typing, and parameterized modules (functors). We also study the mathematical reasoning involved in the design of functional programs and techniques for proving properties about functions so defined.