There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

http://bcpw.bg.pw.edu.pl/Content/1968/Rudiments.pdf

Rudiments of rough sets

Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted

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Gesture Recognition: A Survey

We estimated the world’s technological capacity to store, communicate, and compute information, tracking 60 analog and digital technologies during the period from 1986 to 2007. In 2007, humankind was able to store 2.9 × 10 20 optimally compressed bytes, communicate almost 2 × 10 21 bytes, and carry out 6.4 × 10 18 instructions per second on general-purpose computers. General-purpose computing capacity grew at an annual rate of 58%. The world’s capacity for bidirectional telecommunication grew at 28% per year, closely followed by the increase in globally stored information (23%). Humankind’s capacity for unidirectional information diffusion through broadcasting channels has experienced comparatively modest annual growth (6%). Telecommunication has been dominated by digital technologies since 1990 (99.9% in digital format in 2007), and the majority of our technological memory has been in digital format since the early 2000s (94% digital in 2007).

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The World’s Technological Capacity to Store, Communicate, and Compute Information

This PDF file contains the editorial “Image Processing: Principles and Applications” for JEI Vol. 15 Issue 03

Image Processing - Principles and Applications

Preface1 Introduction to Data Compression2 Source Coding Algorithms3 JPEG-Still Image Compression Standard4 Introduction to Discrete Wavelet Transform5 VLSI Architectures for Discrete Wavelet Transforms6 JPEG 2000 Standard7 Coding Algorithms in JPEG 20008 Code Stream Organization and File Format9 VLSI Architectures for JPEG 200010 Beyond Part 1 of JPEG 2000IndexAbout the Authors

JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures

Preface. 1. Introduction to Data Mining. 2. Soft Computing. 3. Multimedia Data Compression. 4. String Matching. 5. Classification in Data Mining. 6. Clustering in Data Mining. 7. Association Rules. 8. Rule Mining with Soft Computing. 9. Multimedia Data Mining. 10. Bioinformatics: An Application. Index. About the Authors.

Data mining : multimedia, soft computing, and bioinformatics

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Tinku Acharya

Papers

Image Processing - Principles and Applications

JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures

Data mining : multimedia, soft computing, and bioinformatics

JPEG: Still Image Compression Standard

JPEG2000 standard for image compression