Fundamental-Information

You want to study computer hardware. You need to know how Windows 2000 Professional works and most importantly you need to learn how Office applications work with both Windows 2000 and the hardware of the system. You need to buy 8 or 9 books, or do you?

Marc Herniter has taken the trouble to put together a 900 plus page book that covers each of the areas and will give you a better understanding how each integrates with the other. Herniter's book are not written to any exam certification specifically, however there is enough information to supplement your studies.

From hardware topics like memory, disk storage and video, to Windows 2000 topics like the file system, explorer, desktop, management and system utilities to Word, Excel and PowerPoint in Office 2000, Herniter gives you a very good book to work with.

Also Herniter includes networking and the internet through Windows 2000, which is certainly a great bonus. The omitting of Applications like Access, Front Page, Outlook and Project does mean you will need other books, hopefully in the 3rd edition these topics will be covered.

There is an abundance of screen shoots to help make the author's points in every topic. Also the additional questions at the end of each section are a great study bonus. The Cd-rom that comes with the book includes several tutorials from DOS to Windows 2000 to Office 2000. For those that teach this is certainly a book to take serious look at.

New interesting findings regarding intellectual functioning - especially interesting is the chapter on prenatal cognitive development; a discussion of the first thoughts a child forms.

This book is in overall close to very good. Some confusingthings are the unclear differences of USE-relationship, and otherrelationships. I probably include more comments in the future. END

I bought my first copy of this text in 1992 and it has been my constant companion and mentor ever since. From my early Pascal days in college to J2EE development in present times, I have always found the authors' treatment of the discipline of software engineering to be concise, accurate and relevant to the issues at hand. It is one of those books that code shovellers hate...an uncompromising publication that addresses serious process issues such as requirements specification, rigour, interface design and modularity, and robustness. These matters just refuse to go away, and the authors of this book know it. This book is timeless.

The book is beginner to intermediate level. It does a good job of descibing how the processor process information, decribes memory mapping vs I/O mapping with some good hardware examples to cover both. I use it a lot for reference. Covers the no-man land between soft and hardware well. All software is in assembly (off course).

wan system

The beginner in Automaitc Speech Recognition should read this book. It introduces all the basics of signal processing and vocal tract modeling needed and provides good descriptions of modern algorithms for statistical speech recognition (such as dynamic programmation, Hidden Markov Models, Viterbi Algorithm ...).

This ia a classical book on speech recognition. It covers the basic concepts and practical speech recognition Techniques. The first tutorial on HMM by Rabiner,appeared in IEEE, is included in this book with much more practical examples. This book helped me a lot during my post graduation and work in the area of speech recognition. Thanks to Rabiner and Juang !!!

This book is a comprehensive and excellent introduction to the ever-expanding
field of Automatic Speech Recognition. Starting with models of speech
production, speech characterization, methods of analysis (transforms etc),
the authors go onto discuss pattern comparison, hidden Markov models (HMMs),
and design and implementation of speech recognition systems, right from
isolated word recognition to large vocabulary continuous speech recognition
systems. Neural networks and their use in speech recognition is also presented,
though somewhat briefly.

Rabiner was the author of the first widely-read tutorial on HMMs, so
naturally the presentation of HMMs is one of the strong points of this
textbook. The theory is developed in detail, but in an easy to follow
fashion, starting with the very basics and with plenty of helpful examples.
The implementation is discussed at great length as well, starting with
the simplest of tasks and progressing to the state-of-the-art (circa 1993).

That isn't to say that HMMs are the only good part of this book - indeed,
practically every topic, whether it be perception, transforms, vector quantization
or dynamic programming, is presented with great clarity. This book really is easy to
learn from, with numerous examples and illustrations.

The field of speech recognition is inherently multi-disciplinary in nature,
drawing upon various areas of study, including Physics, Physiology, Acoustics,
Signal Processing and Computer Science, to name but a few. The authors do a
great job of explaining all these facets, as well as the mathematics that
is an essential tool.

The only caveat is that it's now a little old (published 1993), since the
field has been growing by leaps and bounds - so while the basics remain
the same, things have changed and hence what's said here should not be
taken as the last word on the subject.

Perhaps a new edition is due, and would certainly be most welcome.

However, for an excellent, accessible introduction to this exciting field,
this is still a great choice.

I bought this book after reading the first few chapters of Shu Lin's "An Introduction to Error Correcting Codes" pub in 1970. Since that was very understandable (but unavailable), I opted for this one. Alas, I got bogged down in the additional math almost immediately. This is a bit heavy duty stuff for learning on your own. Maybe I can find a class some day and get through the wall of fire.

a very detailed book for getting into Galois field arithmetics, cyclic codes, convolutional codes, ... As a very beginner I had no big problems understanding the content. I am not the type of guy who could understand just by reading the theory - this book gives a lot of very useful examples, so you could call it fun reading it!

Lin and Costello produced an excellent text which is targeted towards engineers as opposed to mathematicians. The mathematics behind error correction can be extremely intensive and, with other texts, I quickly become lost in complex proofs. Lin and Costello present error correction in method, with plenty of good examples, which those who need to know how to apply it can understand and the gory details of the theory are not as important. I used this book as my introduction to error correction and it continues to be a great reference book. The only drawback in it is since it was published in '82, it stops at convolutional coding and does not cover trellis-coded modulation or turbo codes.

This is a bad book for several reasons
1.)It lacks good examples. The examples are too short for the most part, and the author makes little effort to explain how did he get into the final answer.
2.)It spends too much time on Boolean algebra. Nobody use boolean algebra for complex digital circuits. The author should have given more time for the K-Map.
3.)There is nothing in the book that tell you the application of digital design in the real world

I suggest you read Digital Fundamentals by Thomas Floyd. It is a much better book!

(Note -- this review applies to the 4th edition of the text. I haven't seen the 5th edition, but it seems that it will fix most of my complaints, hopefully without subtracting from the good features!)

This is a learn-by-example style of text. Not only are examples given in the body text, but the first end-of-chapter problems are worked out in detail, and solutions are given for many more. I consider this a fine book for enriching the material I'm presenting in class.

Major down-side is that the book is decades out of date in terms of design style (some of which might cost one's job if applied) and extensive use of obsolete components (type T flip-flops anyone?). Roth's VHDL based book is probably better, but you gotta learn the fundamentals first, and this book covers them. Any good instructor will point out the flaws.

i have been using both roth and mano for my digital design course at college and my experience is that roth is 'the' book for digital design.it is clear ,concise and to the point.mano seems to be a little too descriptive and sometimes drives you early to bed.however it is always good to refer to mano as for PLDs are concerned..

Douglas Hofstadter is best known for his seminal work 'Godel, Escher, Bach' (1981), but not much was known about the work he carried out at the University of Indiana. This work collects a number of research papers from the 80s, thus offering a glimpse into the continuation of the work that was carried out with the help of the 'fluid concepts'-group. Hofstadter writes well, which means that the accounts of the projects that were undertaken are exciting, thought-provoking, and intruiging. I'm not entirely happy about the theoretical background to some of the work, maybe Hofstadter tries too deliberately to maintain things at a simple level. Still, if you're at all interested in the state of the art in AI research, this is a book you may not want to miss.

This book has received some poor reviews and been unfairly compared to Hofstader's previous book, Goedel, Escher, Bach. While both are books about cognitive science, the former is a book of philosophy -- it's written for the layperson and discusses the topic in relatively abstract terms. This book is no less interesting for the fact that it deals in concretes: it discusses the actual architecture, the design of the programs which simulate the intelligent processes described so well in GEB. Those with a background in computer programming will especially appreciate the novelty of Hofstadter's architecture, and will perhaps be inspired to implement their own. Those without a background probably won't have any trouble visualizing the processes for themselves. The book is written as a collection of essays, so my recommendation is: skip around. Read whatever interests you, and think about it for a while. This book is neither a narrative nor an exhaustive reference, and you won't enjoy it if you try to read it as either.

Where does meaning enter the picture in artificial intelligence? How can we say that a machine possesses understanding? Where, and how, does such understanding happen? These are among the deepest and hardest questions faced by the field, which, as many skeptics claim, has not yielded much about them so far. Consider, for instance, that most current research in AI can be roughly classified over two distinct classes:

(1) Low-level perception. The best example of this type of work comes obviously from computer vision systems. These systems, given a set of input images, usually extract some important information from this input, generating, well, other images (i.e. depth image, edge contours etc.). But this extracted information is usually on a still very low, meaningless, level, to be used by, for instance, a theorem-proving system. To make it clear to all readers what is meant by "meaning", consider the information-processing that must occur whenever an animal, given its massive sensorial information, perceives danger. Going from a set of images and sounds to a feeling of danger involves extracting meaning from the original input, and this is not what is done by current low-level perception projects. It is almost as if these perceptual processes "delegate" the extraction of meaning to another upcoming process. To get into the meaning of a situation, low-level perceptual processes are not enough; there is a clear need for further perceptual processing.

(2) GOFAI symbolic manipulation. This is the other side of the AI coin, dubbed by philosopher John Haugeland as GOFAI, for "good-old-fashioned artificial intelligence", where programs usually handle (syntactically) a representation that supposedly should have been formed by a perceptual process. These systems, such as theorem-proving systems, chess playing, and others, do perform some impressive feats, but they do not have a clue about the semantics of their symbol manipulation. As an example, consider the following predicate-calculus statement: (philosopher (Socrates)). We all fully understand what that means, but what about the machine that executes it? Does it have any meaning to the machine? It is obvious that the answer is no, for that is just a syntactic symbol, as meaningful to the computer as (XzE (GgGggGG)), which doesn't mean anything. But how can a system that only manipulates meaningless syntactic symbols posses any meaning on those symbols? This seems to be an intrinsic problem to GOFAI projects.

Both of these avenues of AI research seem to be based on an unspoken hypothesis of a "center of meaning" arising in the brain (maybe the mind's eye?). The low-level perceptual processes should operate on information that has yet to reach such place, and GOFAI systems in turn handle information that seems to have long reached it. The problem is, what happens at the point of crossing the line? Nobody really knows.

Maybe, then, there is no such line after all - as Hofstadter clearly considers as true, by presenting us with an original alternative. His main thesis is based on the idea that meaning comes from an emergent process that combines perception with analogy-making. He argues, following philosopher Immanuel Kant, that perceptual processes are inseparable from high-level cognitive processes, and, moreover, that (1) perception is guided by analogy-making, and (2) this analogy-making process is itself derived from perception. This thesis has profounds implications for AI.

In his systems, perceptual observations activate concepts, and these activated concepts in turn guide (probabilistically) further perceptual observations. Hofstadter and his group ressurect the HEARSAY II architecture and extend it to other pattern-analysis domains. There is a mixture of bottom-up and top-down processing that eventually leads to the understanding of a situation arising as a combination of "platonic" concepts. This iterative (perception/analogy mapping) process gradually develops a coherent view of the context of the problem it is working on, and that view constitutes, in a sense, on the extracted meaning of the problem. We can say that "understanding p" is, in a sense, "to know what p is like", and this "what p is like" information comes from such analogy-mapping.

Not surprisingly, his projects cannot be found on the symbolic versus connectionist menu. Hofstadter points out that GOFAI (symbolic) systems are too optimal, too rational to be psychologically realistic (he calls them "the Boolean dream"), and that, on the other hand, connectionist systems operate on a level "too low" to be relevant, at present, to a greater understanding of the cognitive issues. Obviously, all mental phenomena may be reducible to a connectionist-system level, but, then again, these same phenomena will be reducible to a quantum physics level. What we should strive for at the moment, he argues, is the right level on which to conduct research. And that level may just be the level of the HEARSAY II speech-understanding system.

Probably the most ambitious AI project under development today is the Letter Spirit project, described on the last chapter. Striving to develop a system that deserves credit for its own creations, with a sense for esthetics, with true creativity and true style - almost taboo issues in AI -, this project messes with many important topics that lack serious study. And, just in case a skeptical reader is wondering, "but, doesn't the project X mess with these exact issues?", then, well, I would recommend Hofstadter's own criticism of "related" projects, given on the epilogue "On Computers, Creativity, Credit, Brain Mechanisms, and the Turing Test".

In summary, this is not your average AI book. This is a full redefinition of artificial intelligence, on a class of its own, an excellent book that deals with deep issues largely ignored by the AI community. Like all the great AI books, this one shuffles between philosophy, methodology, and architecture. Some, maybe even most, highly established AI researchers will not comprehend it completely -- they'll never realize its full scope. However, it is highly recommended to Graduate Students on AI (though not as an introduction to the field). It also seems to be making its mark among philosophers, and I think that neural network researchers will appreciate it as well, for, by extending the HEARSAY II architecture to other domains, it presents an alternative (emergent) architecture that brings us much closer to understanding what understanding is all about.

The author has done great justice to the subject of database in the modern settings. I recommend this book as the first serious academic (yet practical) database book to read.It is not a book on a specific tool however. It is mainly a conceptual development book as it claims to be. As a faculty member in Computer Science, I have read many database books at all levels of difficulties. It is one of the best books I have read that I think has provided the clearest possible explanation on the Entity Relationship model.

Having said all these positive comments, I would like to suggest to the authors to put more in-depth, concrete, as well as practical examples in the topics on Relational Calculus and Relational Algebra in order to the students true appreciations why efforts on learning such abstracts tools are justified. Much stronger emphasis on Object-Oriented databases would be a very timely addtions to the next edition, which I sincerely look forward to purchasing a copy of, if and when it materializes.

I strongly recommend this book for a 3rd year level univeristy level database course for all faculties in computer science and computer information systems as a formal text. It is well worth every penny of your money spent.

Lawrence Lee
Vancouver BC, Canada.

Some people claim this book is too difficult for novices to understand, but that's just not true -- I was a complete novice! The book's not for *skimming* and I think that's the problem. The authors don't include extraneous paragraphs or even sentences, which means it's all necessary. Start at the beginning, maybe even take a couple notes and you'll be fine -- you'll be great in fact, there is wealth of knowledge and experience infused in every chapter. It is definitely one of the most intelligently written, clear, and complete Computer Science books I have yet read.

This is a most comprehensive book for database at the same time having enough depth and I have used this book for 2 courses and read it in entirely.It covers every area of the db from realtional models,database design,system impelmentation techniques to advanced concepts.If you want to know about databases in entierty, this is the book.
I have read some of the negative reviews, guys this is a not book for learning sql or for that matter is not titled database for dummies.I agree that you need some background in computer science but I donot think there is advanced math in it.
Please READ THE TITLE and the abstract and probably then make your views about the book.
I give it 5 stars because it achieves what it desires brilliantly

This is not a book you can use to rigourously learn the fundamentals of image processing. It's a good reference book for the techniques, giving formulas and some hand waving to accompany them - little is motivated, ir doesn't flow, is more a compendium of techniques used in the field accompanied by some text just to make the formulas less dry : the format formula/paragraph is repeated throughout; everything is treated at the same level hence the disjointed structure that doesn't flow. Low on rigourous analysis; may be good as a collection of magazine articles but fails as a textbook on fundamentals - reading this book you'll know the how but it won't help you grasp the why.

A good book for a variety of people, novice or advanced. Mathematically very well organized. References and bibliography well structured, for each section, giving freedom to explore the ideas.

This is an excellent and comprehensive book about image processing. Its name is misleading - this is not an introductory book, although it covers all the needed mathematical background. However, this is one of the most valuable books in this field to be handy on your book shelf.