Linear codes, Syndrome decoding of
linear codes, The Hamming codes, The binary Golay codes, The ternary Golay
codes, Perfect codes and data compression, MacWillimas identities, Pless
symmetry codes, Cyclic codes, Idempotent generators of cyclic codes
Author(s): Vladimir D.
Tonchev,Michigan Technological University
Logic programming is a paradigm where computation arises from proof search in a
logic according to a fixed, predictable strategy. It thereby unifies logical
specification and implementation in a way that is quite different from
functional or imperative programming. This course provides a thorough, modern
introduction to logic programming. It consists of a traditional lecture
component and a project component. The lecture component introduces the basic
concepts and techniques of logic programming followed by successive refinement
towards more efficient implementations or extensions to richer logical concepts.
We plan to cover a variety of logics and operational interpretations.
The project component will be one or several projects related to logic
Author(s): Frank Pfenning,Carnegie Mellon
This note explains the
following topics: Linear Codes, Bounds, Asymptotic Bounds and Shannon’s Theorem,
Constructing Codes from Other Codes, Generalized Reed-Solomon Codes,
Asymptotically Good Codes, Local Decodability, List Decoding, Hard Problems in
Coding Theory, The Nearest Codeword Problem and NP-Completeness.
This book has been written as
lecture notes for students who need a grasp of the basic principles of linear
codes. Topics covered includes: Shannon theory and coding, Coding theory,
Decoding of linear codes and MacWilliams identity, Coding theory - Constructing
New Codes, Coding theory - Bounds on Codes, Reed-Muller codes, Fast decoding of
RM codes and higher order RM codes.
This note covers the following
topics: Basic codes and constructions, Algebraic Geometry Codes, Limits on
Performance of Codes, Algebraic decoding, Algebraic decoding, Linear time
decoding, Decoding from random errors and erasures, Applications in complexity
theory and Complexity results in coding theory.
This book explains the following topics: Linear Codes, Probability
as Fancy Counting and the q-ary Entropy Function, Combinatorics, The Greatest
Code of Them All: Reed-Solomon Codes, What Happens When the Noise is Stochastic:
Shannon's Theorem, Bridging the Gap Between Shannon and Hamming: List Decoding,
Code Constructions, Code Concatenation, Algorithms, Decoding Concatenated Codes,
Efficiently Achieving the Capacity of the BSCp, Efficient Decoding of
Reed-Solomon Codes, Efficiently Achieving List Decoding Capacity, Applications.
Author(s): Venkatesan Guruswami, Atri Rudra and Madhu
This note covers
the following topics: Functions, Values and Side Effects, Control and
Higher-Order Functions, Environments and Lambda, Newton's Method and Recursion,
Data Abstraction, Sequences and Iterables, Objects, Lists, and Dictionaries,
Mutable Data Types, Object-Oriented Programming, Inheritance, Generic Functions,
Coercion and Recursive Data, Functional Programming, Declarative Programming,
Unification, MapReduce, Parallelism.
This note introduces the theory of
error-correcting codes to computer scientists. This theory, dating back to the
works of Shannon and Hamming from the late 40's, overflows with theorems,
techniques, and notions of interest to theoretical computer scientists. The
course will focus on results of asymptotic or algorithmic significance.
Principal topics include: Construction and existence results for
error-correcting codes, Limitations on the combinatorial performance of
error-correcting codes, Decoding algorithms, Applications in computer science.
main focus of this book is the design process that leads from problem statements
to well-organized solutions; it deemphasizes the study of programming language
details, algorithmic minutiae, and specific application domains. It covers the
following topics: Processing Simple Forms of Data, Processing Arbitrarily Large
Data, Abstracting Designs, Generative Recursion, Accumulating Knowledge,
Changing the State of Variables, Changing Compound Values.
Felleisen, Robert Bruce Findler, Matthew Flatt and Shriram Krishnamurthi
This book covers the following topics: Computer Architecture,
Functions, Files, Reading and Writing Simple Records, Developing Robust
Programs, Sharing Functions with Code Libraries, Intermediate Memory Topics,
High-Level Languages and Optimization.
This book provides a practitioner's guide for students, programmers,
engineers, and scientists who wish to design and build efficient and
cost-effective programs for parallel and distributed computer systems. It covers
the following topics: Parallel Computers and Computation, Designing Parallel
Algorithms, Quantitative Basis for Design, Putting Components Together, Tools,
Fortran M, High Performance Fortran, Message Passing Interface and Performance