ekf | If you are a student in ELEC6910P , you ca take my code | Math library

I realized i simply stands for the i'th columnn.

There is nothing "wrong" with what you've done.

This is actually a good use of references: your function parameter names are descriptive and self-documenting (presumably), but then you also clearly map these to existing terminology from another domain. That makes your expression easy to read (presumably), and shouldn't cost you anything in terms of performance or maintainability.

It's also very similar to how you'd write the algorithm out in "real life". You start by listing the one-character variables and explaining what they are; then you use them in a formula. So, if your intention is to stick to the original algorithmic form, this is a great way to put it in your code.

And, yes, it could be described as giving a function parameter a new name. That's generally what references do: introduce a new name for a pre-existing thing.

Do note, however, that how acceptable this pattern is will be completely subjective, and if you're employing it in many places in your project, you may raise a few eyebrows.

Also, if these single-letter names are completely conventional, and your target audience are all going to be domain experts forevermore, you may find that the original short names, despite being unclear in the programming sense, are actually easier for your readers to quickly understand at the callsite and in your interface documentation, than longer names that you've made up yourself.

I'm not going to read all the code to try and figure out what it is you're trying to do and thus where your mistake(s) are. Instead, I'll just examine what you've provided and you can go from there.

Clearly, both the signatures you've provided are valid signatures. This can be seen by manually going through the verification steps, stopping just after modular exponentiation using the encrypt exponent. The result is as expected for pkcs1 EMSA-PKCS1-v1_5 encoding. And just as clearly, we can see that different SHA-256 hashes are involved. In other words, the signatures are different because the data they are signing is different.

For the signature that starts with AR1X19... the data being signed is the UTF-8 encoding of the string ET8d1voUkzNcqud7M8W0WQcd3l2Ih1ZtiMxStPeubKg=. Note that this string a pretty obviously the base64-encoding of random looking 32 bytes. In this context one might guess that those 32-bytes are actually the sha256 hash of some other unknown quantity. And sure, enough the signature that starts with Zg8ftxJqR... is over data whose sha256 hash is the base64-decoding of ET8d....

So it looks like the first signature is correct, and the second one is the result of base64-encoding the sha256 hash during in the middle of the signing operation.

Note that for the last i, it is equal to len(data) - 1. Then i+1 = len(data), which is out of bounds.

Consider

As I've already mentioned in my comments you can express the reliability of your sensors through the input variance. The variance of Sensor A will be constant whereas the variance of Sensor B increases over the time.

The filter does not care how many sensors are in the system. It just gets measurements on its input as if there is only one sensor. The important thing is the communicated variance.

Here is a short matlab example of a filter with two acceleration sensors. The state space consists of the velocity and acceleration. Sensor A is active at even and Sensor B at odd calls.

According to this, ekf.x is of type double x[]. You can't just assign to an array like ekf->x = dptr;, as the error says. If you want to copy data from dptr to ekf->x, you can use memcpy:

ODE.jl is in maintenance mode so I would recommend using DifferentialEquations.jl instead. In the DiffEq FAQ there is an explanation about using ForwardDiff through the ODE solvers. It works, but as in the FAQ I would recommend using sensitivity analysis since that's a better way of calculating the derivatives (it will take a lot less compilation time). But yes, DiffEqParamEstim.jl is a whole repository for parameter estimation of ODEs/SDEs/DAEs/DDEs and it uses ForwardDiff.jl through the solvers.

(BTW, what you're looking to do sounds interesting. Feel free to get in touch with us in the JuliaDiffEq channel to talk about the development of parameter estimation tooling!)

There are a couple of problems that can cause dronekit to fail with a connection timeout:

• Ensure you have the pyserial module installed.

• Specify the baud rate for your connection explicitly, as in:

No, the second paper does not describe the end of the Kalman filter in Visual-Slam. The Kalman filter is a special case of the Maximum Likelihood Estimator for Gaussian noise. I want to direct your attention to Page 4, paragraph 3 of the second paper. There, the authors should clarify that the Kalman Filter and MAP are both extensions of Maximum Likelihood Estimation. As written, that insight is only implicit.