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I'm looking for a book similar to "Introduction to Algorithms" by Thomas Cormen geared towards DSP algorithms. Is there anything as thorough as Cormen on the DSP market?
EDIT
I should say I'm looking for a book that's analogous to The Joy of Cooking.
Disclaimer - I am not familiar with the Cormen book so I'm not quite sure what you're looking for.
I'm a huge fan of "A Digital Signal Processing Primer" by Ken Steiglitz. It introduces DSP concepts like sampling, as well as simple filtering implementations, without relying just on math for explanation. Cookbook equivalent: You know how to boil water on a stove, but you're nervous about the rest.
A more advanced book, more of a practitioner's handbook than a text, is "Theory and Application of Digital Signal Processing" by Lawrence Rabiner and Bernard Gold. Their explanation of the overlap-save FFT technique for convolution, in particular, is the best I've ever come across. Cookbook equivalent: Maybe Joy of Cooking, maybe the Cordon Bleu tome.
And "Telecommunications Breakdown" by Richard Johnson and William Sethares is great for taking some DSP concepts and bringing them to life by implementing a radio in software. Cookbook equivalent: A tour through a specific cuisine, and explains what "braising" is along the way.
Hope these are of use to you!
For theory, I like Understanding DSP by Rick Lyons, which also has some nice "recipe-type nuggets".
More practical, and much more "nuggetty" is Streamlining DSP, same author. There's some really interesting stuff in there (IMHO!). Some of it is of the "lost knowledge" variety - especially in these days of just running Matlab's filter design functions. Some of it relates to limited hardware machines (which is great for tiny microcontroller or FPGA implementations).
The articles are written by serious, practicing DSP engineers (many of whom hang out on news:comp.dsp) in a very accessible style.
(I'm afraid I'm no good with cooking analogies though :)
Just for the record and benefit to others, I would recommend The Scientist and Engineer's Guide to Digital Signal Processing.
This is a good book for beginners.
There are a few online books available as well at the great DSPRelated.com:
INTRODUCTION TO DIGITAL FILTERS WITH AUDIO APPLICATIONS by JULIUS O. SMITH
MATHEMATICS OF THE DISCRETE FOURIER TRANSFORM (DFT) WITH AUDIO APPLICATIONS by JULIUS O. SMITH
This is not a book but I'm sure it'll be a valuable resource: The Ecole Polythechnique de Lausanne is starting a free online course on digital signal processing on February 18th 2013: https://www.coursera.org/course/dsp.
Also, the guys teaching it co-authored a book on the topic: http://www.sp4comm.org/
A second vote for the Rick Lyons book. You might also want to get a couple of DSP "bibles", e.g. Oppenheim & Schafer and Proakis & Manolakis, which are more theoretical but cover more ground.
The DSP handbook: algorithms, applications and design techniques - Bateman, Andrew, Paterson-Stephens, Iain 2002
and
Introduction to digital signal processing - Meddins, Bob 2000
Have basically made my ADSP module a breeze (so far). They are also at the top of the suggested reading list. As such, both are fairly beginner friendly, and the latter includes Matlab examples.
The former is probably more Delia, while the latter is more 'my first cook book'.
I will also add Vetterli, Kovacevic, and Goya's Foundations of Signal Processing, which can be downloaded for free.
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I am doing a project at the university and I need to train an algorithm to rephrase sentences, what can you advise for implementation? Is it possible to use a translator to translate into another language in the end to get a paraphrased sentence? Also i want to use Word2Vec, or it's a bad idea?
This kind of broad-advice question – and about a very-tough problem, paraphrasing text, that is still a very active research problem – would be better answered by surveyin the research literature.
A great site for searching relevant papers – and then finding other related papers once you've set some positive examples – is http://www.arxiv-sanity.com/.
Searching for [paraphrasing] or [summarization] would give you a running start in seeing major techniques & their limitations. And, once you start bookmarking papers by the little 'disk' icon, it can autosuggest important related papers... so even if your 1st few finds are tangential or far-from-usefulness, it can lead you to the seminal papers, & prevailing cutting-edge algorithms/libraries, pretty quickly.
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Can somebody explain me the main pros and cons of the most known datamining open-source tools?
Everywhere I read that RapidMiner, Weka, Orange, KNIME are the best ones.
look at this blog post
Can somebody do a fast technical comparison in a small bullet list.
My needs are the following:
It should support classification algorithms (Naive Bayes, SVM, C4.5,
kNN).
It should be easy to implement in Java.
It should have understandable documentation.
It should have reference production projects or use cases working on in.
some additional benchmark comparison if possible.
Thanks!
I would like to say firstly there are pro's and cons for each of them on your list however I would suggest out of your list weka from my personal experience it is incredibly simple to implement in your own java application using the weka jar file and has its own self contained tools for data mining.
Rapid miner seems to be a commercial solution offering an end to end solution however the most notable number of examples of external implementations of solutions for rapid miner are usually in python and r script not java.
Orange offers tools that seem to be targeted primarily at people with possibly less need for custom implementations into their own software but a far easier time with user itneraction, its written in python and source is available, user addons are supported.
Knime is another commercial platform offering end to end solutions for data mining and analysis providing all the tools required, this one has various good reviews around the internet but i havent used it enough to advise you or anyone on the pro's or cons of it.
See here for knime vs weka
Best data mining tools
As i said weka is my personal favorite as a software developer but im sure other people have varying reasons and opinions on why to choose one over the other. Hope you find the right solution for you.
Also per your requirements weka supports the following:
Naivebayes
SVM
C4.5
KNN
I have tried Orange and Weka with a 15K records database and found problems with the memory management in Weka, it needed more than 16Gb of RAM while Orange could've managed the database without using that much. Once Weka reaches the maximum amount of memory, it crashes, even if you set more memory in the ini file telling Java virtual machine to use more.
I recently evaluated many open source projects, comparing and contrasted them with regards to the decision tree machine learning algorithm. Weka and KNIME were included in that evaluation. I covered the differences in algorithm, UX, accuracy, and model inspection. You might chose one or the other depending on what features you value most.
I have had positive experience with RapidMiner:
a large set of machine learning algorithms
machine learning tools - feature selection, parameter grid search, data partitioning, cross validation, metrics
a large set of data manipulation algorithms - input, transformation, output
applicable to many domains - finance, web crawling and scraping, nlp, images (very basic)
extensible - one can send and receive data other technologies: R, python, groovy, shell
portable - can be run as a java process
developer friendly (to some extent, could use some improvements) - logging, debugging, breakpoints, macros
I would have liked to see something like RapidMiner in terms of user experience, but with the underlying engine based on python technologies: pandas, scikit-learn, spacy etc. Preferably, something that would allow moving back and forth from GUI to code.
i have been given a project where i will need to write code such that it parts of images. For example the project will require me to extract a river part from the scenery or so. I have no experience in this context. Please tell me where do i start studying form. Which are good books? Which technologies will i need to learn. What are the tools that are helpful?
openCV is probably the most complete free image processing library.
There is also a book which describes both the library and some image processing techniques.
This is a reasonably complex problem, not exactly graduate research but challenging!
See this question for a list of other books.
ImageJ is an easy to use, scriptable, modular, open source software, which may be quite useful in learning image processing. As for books, Digital Image Processing by Gonzalez et al. is de facto standard.
Unsure if this Q is closed but I recommend NetPBM and similar packages. This allows you to define images as text and perform operations but in a manner (albeit slow processing) that makes it very easy to understand.
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What should someone study at university level if he/she wants to get into robotics and build robotics? So far 'Mechatronics' seems to be the field I'm looking for? I looked at a few plain 'robotics' courses but they seem to be only about the electrical and computer work, and don't include any details on building the mechanical components of robots?
I'm a professional robotics research consultant, with 30 years of experience working for organizations like SRI International and JPL.
Like computers, robotics has quite a strong divide between the software and the hardware. Hardware is further subdivided into actuators and sensors.
If you'd said "I want to get into computers", I would explain that only a few hardware engineers actually design and build physical computers--most researchers assume that the hardware and firmware has been built already, and then they worry about the software--how to make the system actually work.
Similarly with robots, building the hardware is a job for the mechanical engineers (to design the structure and heat dissipation), with little bits and pieces for power electrical engineers (to spec the motors) and computer engineers (to design the firmware silicon). Next-generation robots also use industrial designers (to make the outsides look pretty, and the insides fit well together).
Research areas for actuator design include fingered hands; tentacles; hummingbird and other bird and insect wings; springy wheels; legs; non-electronic designs for high radiation areas; and surgical instruments.
With cameras in every cell phone, vision sensors are mostly a solved problem at this point. Research areas for sensor design include smart flexible tactile skin, brain wave sensors, and other biomedical sensors. There's still some room for good force sensors as well. These fall in the realms of materials engineering, computer engineering, mechanical engineering, and biomedical engineering.
In order to drive the actuators properly so they don't shake themselves apart, you need a control-theory engineer. Start with Fourier transforms so that you can then understand z-transforms. The learning curve on this mathematics is extremely steep, and careers are quite few, so either you have to be born to be a controls engineer or you should let someone else handle these lower-level details for you.
Signal processing, for the medium- and low-level sensor drivers, has been under the domain of the EEs historically. This works its way up to image processing, which falls under computer science, and then image understanding, which is in the A.I. branch of CS.
However, as I mentioned, the hardware, firmware, and drivers are all manufacturing details that you solve once and then sell forever. Anybody can buy a Lego or a Bioloids kit off the shelf now, and start working with motors. It's not like 2006, when the Fujitsu HOAP humanoid robot we were working with at JPL was a $50,000 custom-ordered special.
Most of what I consider the really interesting work starts by assuming the hardware and drivers have already been accomplished--and then, what do you do with the system? This is completely in the realm of software.
Robotic software control starts with 3D simulators, which in turn are based on forward kinematics; eventually inverse kinematics; dynamics, if you feel like it; and physics-engine simulations. Math here centers around locations [position + orientation], which are best represented by using [4x4] homogeneous coordinate transformation matrices. These are not very hard, and you can get a good background in them from any computer graphics textbook. Make sure you follow the religion of post-multiplying by matrices ending in a column vector on the right; this allows you to chain base-to-waist-to-shoulder-to-elbow-to-hand kinematics in a way that you'll be able to understand. Early textbooks proposed premultiplying using row vectors, because they thought it wouldn't make a difference. It does.
Of course the physics engines require a decent knowledge of physics.
Higher-level processing is accomplished using artificial intelligence, usually rules-based systems. Natural-language processing also can tie in linguistics and phonetics. Speech recognition and speech generation are again mostly signal processing, taught in EE and CS.
Recent advances work on Big Data, which uses statistics, Bayesian reasoning, and bases vector spaces (from mathematics).
Robotics has not yet broken out. It is still at the level cell phones were at when Gordon Gecko was walking on the beach talking into a "portable phone" the size of a shoe. I don't see robots becoming ubiquitous before 2020. Around 2025, being a robot programmer will be in demand as much as being an app programmer is today. Study lots of A.I. Start early.
State-of-the-art humanoid robot system design as of 2006 [short movie]:
http://www.seqcon.com/caseJPL.html
Very high level block diagram of components [graphic]:
http://www.seqcon.com/images/SystemSchematic640.gif
I would highly recommend looking into Artificial Intelligence for Robotics on Udacity, it is very interesting course that covers the software and AI part. Also Coursera offers a free online robotics course, and other courses as well that are very relevant and useful to Robotics.
Mechanical and electrical engineering and computer science.
Mechanical engineering will inform choices about servos, linkages, gears, and all other mechanical components.
Control theory is the junction of mechanical and electrical engineering. You'll need that.
So much of control is digital these days, so EE and computer science will be a part of it.
It's a big field. Good luck.
Industrial robotics is usually handeled by Mechanical Engineers, and sport/team robotics by electical engr, electronics engr, or computer science majors. It all depends on what you mean by "robotics". Also, in case nobody else mentions it, a Masters degree is strongly encouranged.
As an added bonus the math used in industrial robotics, is directly linked to math for game development. There isn't really a clear cut line of who is supposed to be doing what in robotics.
Mechtronics is the current field of study for those interested in robotics. It combines mechanical, electrical, controls, and software as relates to robotics.
In the past we came from many different backgrounds, mechanical engineers, electrical, electronics, and software. I am an Application Engineer for robot manufacturer. I started out in Avionics, moved to automated test equipment, then to automated material delivery systems, I became a robotics service technician and manager then moved over to application programing and training.
One final note, be prepared to keep learning. This is a field that is constantly changing and evolving.
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I am looking for some entry level posts about machine learning. Can anyone suggest anything for somebody new to this subject?
By 'posts' i'll assume you mean any resource available online.
I recommend two groups of resources:
First, find Machine Learning blogs in which the blogger's preferred language is the same as yours. In my experience, reading a blog post on a single subject (e.g., SVM) while reading through the author's source code supplied along with blog post is about the best way for a programmer to learn ML. A couple of excellent examples are the blogs Smell the Data (Python), and Igvita (Ruby). Both contain (at least) several posts each describing, tutorial-style, specific ML techniques, which include close walk-throughs of their (posted) source code. Igvita, in particular, has excellent tutorials with working Ruby code on Support Vector Machines, Decision Trees, Singular Value Decomp, and Ensemble Methods--like, the other blog i mentioned, an upper-level undergraduate course could be taught based solely on the ML posts in either blog.
Second, I highly recommend VideoLectures.net.
This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on machine learning. The target audience for these video lectures ranges from beginner (some lectures are specifically tagged as "tutorials") to expert; most of them seem to be somewhere in the middle.
All of the lectures and tutorials are taught to highly experienced professionals and academics, and in many instances, the lecturer is the leading authority on the topic he/she is lecturing on. The site is also 100% free.
The one disadvantage is that you cannot download the lectures and store them in e.g., itunes; however, nearly every lectures has a set of slides which you can download (or, conveniently, you can view them online as you watch the presentation).
A few that i've watched and that i can recommend highly:
Semi-Supervised Learning Approaches
Introduction to Machine Learning
Gaussian Process Basics
Graphical Models
k-Nearest Neighbor Models
Introduction to Kernel Methods
Machine learning is such a broad topic. I would start with Wikipedia and focus in on areas that you find interesting.
Also, you could visit the Stack Exchange site for machine learning.
Stanford published videos and materials from a set of engineering courses at http://see.stanford.edu
One course by Andrew Ng focuses on Machine Learning techniques
http://see.stanford.edu/see/courseinfo.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1
The course is also available on iTunes U
Its a really good course from someone who obviously knows the field well, but he spends alot of the time deriving mathematical results - so if your rusty in linear algebra or prob/stats, you might need a refresher first.
I think the best that I know of are:
Stanford's Lectures on Machine Learning
Books: (In decreasing order of ease of understanding - IMHO)
Machine Learning: An Algorithmic Perspective by Stephen Marsland
Pattern Recognition and Machine Learning by Christopher Bishop
Introduction to Machine Learning - Ethem Alpaydin