Here are some tips that come to mind: From my experience, interactive scripting languages (I use Python) can be a great help. You can write a simple framework to deal with binary streams and some simple algorithms. Then you can write scripts that will take your binary and check various things. For example: Do some … Read more
Create a regular HTML img element like so: <img runat=”server” id=”image” /> And in code behind do this: image.src = “data:image/png;base64,” + Convert.ToBase64String(imageBytes); Where imageBytes is a byte[]. You are done. The image will be displayed.
There’s the intToBits function that converts any integer to a vector of 32 raws, so you can do this: decimals <- c(3,5,11,4) m <- sapply(decimals,function(x){ as.integer(intToBits(x))}) m > m [,1] [,2] [,3] [,4] [1,] 1 1 1 0 [2,] 1 0 1 0 [3,] 0 1 0 1 [4,] 0 0 1 0 [5,] 0 … Read more
Simply replace the whole thing with: return File.ReadAllBytes(fileName); However, if you are concerned about the memory consumption, you should not read the whole file into memory all at once at all. You should do that in chunks.
system(“stty erase ^H); system(“stty -F /dev/ttyS0 -icrnl -ixon -ixoff -opost -isig -icanon -echo”); // enter into non-canonical (raw) mode This would be insufficient code (and poor coding practice per POSIX conventions) to put the serial port into raw or non-canonical mode. The easiest method in C and Linux is to use the function cfmakeraw() which … Read more
You could encode the binary data using base64 and put it into a Base64 element; the below article is a pretty good one on the subject. Handling Binary Data in XML Documents
The advantage of storing data in the DB is taking advantage of DB security mechanisms and reducing maintanence cost (backups, …). The disadvantage of it is increasing DB load and consuming connections (which might be expensive for per-connection licensed database servers). If you are using SQL Server 2008, FILESTREAM might be a nice alternative. By … Read more
I’ve compared performance (space and time) for a number of ways to store numpy arrays. Few of them support multiple arrays per file, but perhaps it’s useful anyway. Npy and binary files are both really fast and small for dense data. If the data is sparse or very structured, you might want to use npz … Read more
The answer by phpguy is correct but I think there is a lot of confusion in the additional details there. The basic answer is in a BLOB data type / attribute domain. BLOB is short for Binary Large Object and that column data type is specific for handling binary data. See the relevant manual page … Read more
For unformated IO, Fortran compilers typically write the length of the record at the beginning and end of the record. Most but not all compilers use four bytes. This aids in reading records, e.g., length at the end assists with a backspace operation. You can suppress this with the new Stream IO mode of Fortran … Read more