This book was published by the 'publishing arm' of Computer Concepts (now Xara) around 1988 or so. Acorn was selling the ARM2 (?) development workstations and had just launched the A300 Archimedes computer. My copy was handed to me when I joined Computer Concepts. Incorporating an assembler was par for the course for Acorn, and much of Computer Concepts software was written as assembly code wrapped by a two-pass BASIC for-loop. The book is a slim book, and covers the ARM3 instruction set including the load/store instructions, arithmetic, barrel shifter. It's a decent read and covers the material adequately, but as always, it probably better to actually learn mainly by getting your hands dirty and writing some code. Note that there is no thumb coverage, since this was not introduced to the ARM until much later. This book is more of a historical artifact now, being almost 20 years old, but still of interest.

This book is intended as a hands-on manual for learning how to design systems using the STM32 F1 family of micro-controllers. It was written to support a junior-level computer science course at Indiana University.

The focus of this book is on developing code to utilize the various peripherals available in STM32 F1 micro-controllers and in particular the STM32VL Discovery board. Because there are other fine sources of information on the Cortex-M3, which is the core processor for the STM32 F1 micro-controllers, we do not examine this core in detail; an excellent reference is "The Definitive Guide to the ARM CORTEX-M3."

This book is not exhaustive, but rather provides a single "trail" to learning about programming STM32 micro controller built around a series of laboratory exercises. A key design decision was to utilize readily available off-the-shelf hardware models for all the experiments discussed.

Geoffrey Brown is a Professor of Computer Science, School of Informatics and Computing, Indiana University.

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If the command was write.csv(x,"mytable.csv",quote=F) then there won't be any quotes around the linenumber.

Giving the command to get metadata for all quality A recordings from all over the world makes the server work for 30 minutes

Comparing two of my cameras for pixel density. I want to know what is the focal length multiplier if I crop the sensor image 1:1 at Full HD (1920 x 1080). I use Full HD as the benchmark as I often crop to this size for making desktop pictures.

Camera	D7100	V2
Max res	6000 x 4000	4608 x 3072
Eff pixels	24 M	14 M
Sensor size	23.5 x 15.6 mm	13.2 x 8.8 mm
Focal length multiplier	36 / 23.5 = 1.53x	36 / 13.2 = 2.73x
Linear density	255 pix/mm	349 pix/mm
Sensor width at Full HD	7.52 mm	5.5 mm
Focal length multiplier at Full HD	4.78x	6.55x
Focal length multiplier at Full HD with TC1.4	6.69x	9.16x

Not exactly a breeze to get running.

First, try installing devtools. It's a package that allows other packages to be installed from GitHub.

It didn't work for me. After Googling around, I found this:

> install.packages('devtools',dependencies=TRUE, repos='https://stat.ethz.ch/CRAN/')

If that is ok, install keras R package from GitHub:

Then install both the core Keras library as well as the TensorFlow backend.

Next, run some test as recommended by https://keras.rstudio.com/

Here's my output after going all the steps.

The R programming language is an open source scripting language for analytics and data visualization. R acts as free alternative to traditional statistical packages such as SPSS, SAS, and Stata. Such software allows for the user to freely distribute, study, change, and improve the software under the Free Software Foundation's GNU General Public License. These advantages over other statistical software encourage the growing use of R in cutting edge social science research.

Why R?

The "R" name is derived from the first letter of the names of its two developers, Ross Ihaka and Robert Gentleman, who were associated with the University of Auckland at the time. R is a free implementation of the S programming language, which was originally created and distributed by Bell Labs. Most code written in S will run successfully in the R environment.

They are plenty of tools available in the market to perform data analysis.  The picture below depicts the learning curve compared to the business capability a language offers. Excel and PowerBI are simple to learn but don't offer outstanding business capability, especially in term of modeling. In the middle, you can see Python and SAS. SAS is a click and run software tool to run a statistical analysis for business, but it is not free. Python, however, is a language with a monotonous learning curve. Python is a fantastic tool to deploy Machine Learning and AI but lacks communication features. With an identical learning curve, R is a good trade-off between implementation and data analysis.

Who uses R?

Stack Overflow (https://stackoverflow.com) is the largest, most trusted online community for developers to learn, share​ ​their programming ​knowledge. Lately, the percentage of question-views has increased sharply for R compared to the other languages. This trend is  highly correlated with the booming age of data science and reflects the demand of R language for data science.

warbleR is an R package for analysis of animal acoustic signature.

Installing warbleR is easy on macOS and Windows. In R command prompt:

More steps in Ubuntu because warbleR has many unresolved dependencies.

1) Install mvtnorm

2) Install soundgen

3) Install RCurl

4) Install FFTW3 (don't skip this step)

Our R package will require a shared library so we will be installing fftw3 from source:

5) Install fftw. It's a wrapper around the fastest fourier transform in the west (FFTW) library.

6) Finally, install warbleR from inside R:

I'm deep indebted to Jonathan Callahan: