Decoding The It Value Problem

Gain greater returns from your IT investments Revealing the secrets to proven, effective strategies that enable businesses to leverage the full value of highly expensive IT investments, Decoding the IT Value Problem is a no-nonsense guide for making smart IT investments and cutting through the noise of vendor marketing and media hype. Author Gregory Fell describes in rich detail the actual processes, frameworks, infrastructure and discipline required to develop and execute corporate IT strategies that are profitable and sustainable. Provides a proven framework for developing and successfully executing profitable IT strategies Plain English guidance for gaining the most return on investment from critical IT investments Explores developing and executing IT strategy; forecasting, calculating and managing IT costs; leveraging IT investments to drive business growth; IT and the evolving global economy; IT value management; communicating IT value across the enterprise; and leading change, transformation and innovation If you're a senior level manager or executive responsible for managing IT value in your business, Decoding the IT Value Problem is the practical and clearly written guide you'll turn to, with tools and tips for smart investment and management of IT costs.

Gain greater returns from your IT investments Revealing the secrets to proven, effective strategies that enable businesses to leverage the full value of highly expensive IT investments, Decoding the IT Value Problem is a no-nonsense guide for making smart IT investments and cutting through the noise of vendor marketing and media hype. Author Gregory Fell describes in rich detail the actual processes, frameworks, infrastructure and discipline required to develop and execute corporate IT strategies that are profitable and sustainable. Provides a proven framework for developing and successfully executing profitable IT strategies Plain English guidance for gaining the most return on investment from critical IT investments Explores developing and executing IT strategy; forecasting, calculating and managing IT costs; leveraging IT investments to drive business growth; IT and the evolving global economy; IT value management; communicating IT value across the enterprise; and leading change, transformation and innovation If you're a senior level manager or executive responsible for managing IT value in your business, Decoding the IT Value Problem is the practical and clearly written guide you'll turn to, with tools and tips for smart investment and management of IT costs.

Pooling designs have been widely used in various aspects of DNA sequencing. In biological applications, the well-studied mathematical problem called “group testing” shifts its focus to nonadaptive algorithms while the focus of traditional group testing is on sequential algorithms. Biological applications also bring forth new models not previously considered, such as the error-tolerant model, the complex model, and the inhibitor model. This book is the first attempt to collect all the significant research on pooling designs in one convenient place.The coverage includes many real biological applications such as clone library screening, contig sequencing, exon boundary finding and protein-protein interaction detecting and introduces the mathematics behind it.

Providing in-depth treatment of error correction Error Correction Coding: Mathematical Methods and Algorithms, 2nd Edition provides a comprehensive introduction to classical and modern methods of error correction. The presentation provides a clear, practical introduction to using a lab-oriented approach. Readers are encouraged to implement the encoding and decoding algorithms with explicit algorithm statements and the mathematics used in error correction, balanced with an algorithmic development on how to actually do the encoding and decoding. Both block and stream (convolutional) codes are discussed, and the mathematics required to understand them are introduced on a “just-in-time” basis as the reader progresses through the book. The second edition increases the impact and reach of the book, updating it to discuss recent important technological advances. New material includes: Extensive coverage of LDPC codes, including a variety of decoding algorithms. A comprehensive introduction to polar codes, including systematic encoding/decoding and list decoding. An introduction to fountain codes. Modern applications to systems such as HDTV, DVBT2, and cell phones Error Correction Coding includes extensive program files (for example, C++ code for all LDPC decoders and polar code decoders), laboratory materials for students to implement algorithms, and an updated solutions manual, all of which are perfect to help the reader understand and retain the content. The book covers classical BCH, Reed Solomon, Golay, Reed Muller, Hamming, and convolutional codes which are still component codes in virtually every modern communication system. There are also fulsome discussions of recently developed polar codes and fountain codes that serve to educate the reader on the newest developments in error correction.

This book discusses the recent advances in natural computation, fuzzy systems and knowledge discovery. Presenting selected, peer-reviewed papers from the 15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019), held in Kunming, China, from 20 to 22 July 2019, it is a useful resource for researchers, including professors and graduate students, as well as R&D staff in industry.

This book presents innovative techniques in recognition and classification of astrophysical and medical images. Coverage includes: image standardization and enhancement; region-based methods for pattern recognition in medical and astrophysical images; advanced information processing using statistical methods; and feature recognition and classification using spectral method.

This volume contains the proceedings of the AMS Special Session on Discrete Geometry and Algebraic Combinatorics held on January 11, 2013, in San Diego, California. The collection of articles in this volume is devoted to packings of metric spaces and related questions, and contains new results as well as surveys of some areas of discrete geometry. This volume consists of papers on combinatorics of transportation polytopes, including results on the diameter of graphs of such polytopes; the generalized Steiner problem and related topics of the minimal fillings theory; a survey of distance graphs and graphs of diameters, and a group of papers on applications of algebraic combinatorics to packings of metric spaces including sphere packings and topics in coding theory. In particular, this volume presents a new approach to duality in sphere packing based on the Poisson summation formula, applications of semidefinite programming to spherical codes and equiangular lines, new results in list decoding of a family of algebraic codes, and constructions of bent and semi-bent functions.

How can one exchange information e?ectively when the medium of com- nication introduces errors? This question has been investigated extensively starting with the seminal works of Shannon (1948) and Hamming (1950), and has led to the rich theory of “error-correcting codes”. This theory has traditionally gone hand in hand with the algorithmic theory of “decoding” that tackles the problem of recovering from the errors e?ciently. This thesis presents some spectacular new results in the area of decoding algorithms for error-correctingcodes. Speci?cally,itshowshowthenotionof“list-decoding” can be applied to recover from far more errors, for a wide variety of err- correcting codes, than achievable before. A brief bit of background: error-correcting codes are combinatorial str- tures that show how to represent (or “encode”) information so that it is - silient to a moderate number of errors. Speci?cally, an error-correcting code takes a short binary string, called the message, and shows how to transform it into a longer binary string, called the codeword, so that if a small number of bits of the codewordare ?ipped, the resulting string does not look like any other codeword. The maximum number of errorsthat the code is guaranteed to detect, denoted d, is a central parameter in its design. A basic property of such a code is that if the number of errors that occur is known to be smaller than d/2, the message is determined uniquely. This poses a computational problem,calledthedecodingproblem:computethemessagefromacorrupted codeword, when the number of errors is less than d/2.