I'm in need of figure out a way of changing the phase of a signal. Objective is to generate two signals with one phase changed and observe the patters when combined.
below is the program I'm using so far:
As in the above setting, I need to use the same signal to generate a phase changed signal and later combine the two signals and observe patters.
Can someone help me out on this?
Thanks.
Using the right inlet of the [osc~] object is a valid way to set the phase of an oscillator but it isn't the only or even the most correct way. The right inlet only permits a float at the control level.
A more comprehensive manipulation of phase can be done at the signal level using the [phasor~], [cos~], [wrap~], and [+~] objects. Essentially, you are performing the same function as [osc~] with a technique called a table lookup using [phasor~] and [cos~]. You could read another table with [tabread4~] instead of [cos~] as well.
This technique keeps your oscillators in sync. You can manipulate the phase of your oscillators with other oscillators, table lookups, and still of course floats (so long as the phase value is between 0 and 1, hence the [wrap~] object).
phase modulation at the signal level
Afterwards, like the other examples here, you can add the signals together and write them to corresponding tables or output the signal chain or both.
Here's how you might do the same for a custom table lookup. Of course, you'd replace sometable with your custom table name and num-samp-in-some-table with the number of samples in your table.
signal level phase modulation with custom tables
Hope it helps!
To change the phase of an oscillator, use the right-hand side inlet.
Quoting Johannes Kreidler's Programming Electronic Music in Pd:
3.1.2.1.3 Phase
In Pd, you can also set membrane position for a sound wave where it should begin (or where it should jump to). This is called the phase of a wave. You can set the phase in Pd in the right inlet of the "osc~" object with numbers between 0 and 1:
A wave's entire period is encompassed by the range from 0 to 1. However, it is often spoken of in terms of degrees, where the entire period has 360 degrees. One speaks, for example, of a "90 degree phase shift". In Pd, the input for the phase would be 0.25.
So for instance, if you want to observe how two signals can become mute due to destructive interference, you can try something like this:
Note that I connected a bang to adjust simultaneously the phases of both signals. This is important, because while you can reset the phase of a signal to any value between 0.0 and 1.0 at any moment, the other oscillator won't be reset and therefore the results will be quite random (you never know at which phase value the other signal will be at!). So resetting both does the trick.
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I am using opencv and openvino and am trying to figure out when I have a face detected, use the cv2.rectangle and have my coordinates sent but only on the first person bounded by the box so it can move the motors because when it sees multiple people it sends multiple coordinates and thus causing the servo and stepper motors to go crazy. Any help would be appreciated. Thank you
Generally, each code would run line by line. You'll need to create a proper function for each scenario so that the data could be handled and processed properly. In short, you'll need to implement error handling and data handling (probably more than these, depending on your software/hardware design). If you are trying to implement multiple threads of executions at the same time, it is better to use multithreading.
Besides, you are using 2 types of motors. Simply taking in all data is inefficient and prone to cause missing data. You'll need to be clear about what servo motor and stepper motor tasks are, the relations between coordinates, who will trigger what, if something fails or some sequence is missing then do task X, etc.
For example, the sequence of Data A should produce Result A but it is halted halfway because Data B went into the buffer and interfered with Result A and at the same time screwed Result B which was anticipated to happen. (This is what happened in your program)
It's good to review and design your whole process by creating a coding flowchart (a diagram that represents an algorithm). It will give you a clear idea of what should happen for each sequence of code. Then, design a proper handler for each situation.
Can you share more insights of your (pseudo-)code, please?
It sounds easy - you trigger a face-detection inference-request and you get a list/vector with all detected faces (the region-of-interest for each detected face) (including false-positive and false-positives, requiring some consistency-checks to filter those).
If you are interested in the first detected face only - then it could be to just process the first returned result from the list/vector.
However, you will see that sometimes the order of results might change, i.e. when 2 faces A and B were detected, in the next run it could still return faces, but B first and then A.
You could add object-tracking on top of face-detection to make sure you always process the same face.
(But even that could fail sometimes)
I have been trying to set up a custom manipulation station with Kuka IIWA hardware in drake. I got the hardware interface working. When running a joint teleoperation code (adapted from drake/examples/manipulation_station/joint_teleop.py), the robot jerks violently (all joints tries to move to 0 position) at first and then continues to operate normally. On digging deeper, I found that this is caused by the FirstOrderLowPassFilter system. While advancing the simulation a tiny bit (simulator.AdvanceTo(1e-6)) to evaluate the LCM messages to set the initial GUI sliders-filter_initial_output_value-plant joint positions etc., to match the hardware, the FirstOrderLowPassFilter outputs a momentary value of 0. This sets the IIWA_COMMAND position to zero for an instance and causes a jerk.
How can I avoid this behavior?.
As a workaround, I am subscribing separately to the raw LCM message from the hardware, before initializing the drake systems and sets the filter_initial_output_value before advancing the simulation. Is this the recommended way?.
I think what you're doing (manually reading the LCM message) is fine.
In the alternative, look how a DiscreteDerivative offers the suppress_initial_transient = true option. Perhaps we could add a similar option (via unrestricted update event) to FirstOrderLowPassFilter so that the initial output value was sampled from the input at t == 0. But the event sequencing of startup may still be difficult. We essentially need to initialize the systems in their dataflow order, including refreshing output ports as events fire, which is not natively supported.
In another alternative, perhaps we could configure the IIWA_COMMAND publisher to not publish at t == 0, instead publishing only t >= 0.005.
FirstOrderLowPassFilter has a method to set the initial value. https://drake.mit.edu/doxygen_cxx/classdrake_1_1systems_1_1_first_order_low_pass_filter.html#aaef7539cfbf1acfa0cf487c371bc5360
It is used in the example that you copied from:
https://github.com/RobotLocomotion/drake/blob/master/examples/manipulation_station/joint_teleop.py#L146
I am trying to understand Q-Learning,
My current algorithm operates as follows:
1. A lookup table is maintained that maps a state to information about its immediate reward and utility for each action available.
2. At each state, check to see if it is contained in the lookup table and initialise it if not (With a default utility of 0).
3. Choose an action to take with a probability of:
(*ϵ* = 0>ϵ>1 - probability of taking a random action)
1-ϵ = Choosing the state-action pair with the highest utility.
ϵ = Choosing a random move.
ϵ decreases over time.
4. Update the current state's utility based on:
Q(st, at) += a[rt+1, + d.max(Q(st+1, a)) - Q(st,at)]
I am currently playing my agent against a simple heuristic player, who always takes the move that will give it the best immediate reward.
The results - The results are very poor, even after a couple hundred games, the Q-Learning agent is losing a lot more than it is winning. Furthermore, the change in win-rate is almost non-existent, especially after reaching a couple hundred games.
Am I missing something? I have implemented a couple agents:
(Rote-Learning, TD(0), TD(Lambda), Q-Learning)
But they all seem to be yielding similar, disappointing, results.
There are on the order of 10²⁰ different states in checkers, and you need to play a whole game for every update, so it will be a very, very long time until you get meaningful action values this way. Generally, you'd want a simplified state representation, like a neural network, to solve this kind of problem using reinforcement learning.
Also, a couple of caveats:
Ideally, you should update 1 value per game, because the moves in a single game are highly correlated.
You should initialize action values to small random values to avoid large policy changes from small Q updates.
I'm making an iOS dice game and one beta tester said he liked the idea that the rolls were already predetermined, as I use arc4random_uniform(6). I'm not sure if they are. So leaving aside the possibility that the code may choose the same number consecutively, would I generate a different number if I tapped the dice in 5 or 10 seconds time?
Your tester was probably thinking of the idea that software random number generators are in fact pseudo-random. Their output is not truly random as a physical process like a die roll would be: it's determined by some state that the generators hold or are given.
One simple implementation of a PRNG is a "linear congruential generator": the function rand() in the standard library uses this technique. At its core, it is a straightforward mathematical function, and each output is generated by feeding in the previous one as input. It thus takes a "seed" value, and -- this is what your tester was thinking of -- the sequence of output values that you get is completely determined by the seed value.
If you create a simple C program using rand(), you can (must, in fact) use the companion function srand() (that's "seed rand") to give the LCG a starting value. If you use a constant as the seed value: srand(4), you will get the same values from rand(), in the same order, every time.
One common way to get an arbitrary -- note, not random -- seed for rand() is to use the current time: srand(time(NULL)). If you did that, and re-seeded and generated a number fast enough that the return of time() did not change, you would indeed see the same output from rand().
This doesn't apply to arc4random(): it does not use an LCG, and it does not share this trait with rand(). It was considered* "cryptographically secure"; that is, its output is indistinguishable from true, physical randomness.
This is partly due to the fact that arc4random() re-seeds itself as you use it, and the seeding is itself based on unpredictable data gathered by the OS. The state that determines the output is entirely internal to the algorithm; as a normal user (i.e., not an attacker) you don't view, set, or otherwise interact with that state.
So no, the output of arc4random() is not reliably repeatable by you. Pseudo-random algorithms which are repeatable do exist, however, and you can certainly use them for testing.
*Wikipedia notes that weaknesses have been found in the last few years, and that it may no longer be usable for cryptography. Should be fine for your game, though, as long as there's no money at stake!
Basically, it's random. No it is not based around time. Apple has documented how this is randomized here: https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man3/arc4random_uniform.3.html
I'm trying to get into machine learning, and decided to try things out for myself. I wrote a small tic-tac-toe game. So far, the computer plays against itself using random moves.
Now, I want to apply reinforcement learning by writing an agent that will explore or exploit based on the knowledge it has on the current state of the board.
The part I don't understand is this:
What does the agent use to train itself for the current state? Lets say a RNG bot (o) player does this:
[..][..][..]
[..][x][o]
[..][..][..]
Now the agent has to decide what the best move should be. A well trained one would pick 1st, 3rd, 7th or 9th. Does it look up a similar state in the DB that led him to a win? Because if so, I think I will need to save every single move into the DB up to eventually it's end state (win/lose/draw state), and that would be quite a lot of data for a single play?
If I'm thinking this through wrong, I would like to know how to this correctly.
Learning
1) Observe a current board state s;
2) Make a next move based on the distribution of all available V(s') of next moves. Strictly the choice is often based on Boltzman’s distribution of V(s'), but can be simplified to maximum-value move (greedy) or, with some probability epsilon, a random move as you are using;
3) Record s' in a sequence;
4) If the game finishes, it updates the values of the visited states in the sequence and starts over again; otherwise, go to 1).
Game Playing
1) Observe a current board state s;
2) Make a next move based on the distribution of all available V(s') of next moves;
3) Until the game is over and it starts over again; otherwise, go to 1).
Regarding your question, yes the look-up table in Game Playing phase is built up in the Learning phase. Every time the state is chosen from the all the V(s) with a maximum possible number of 3^9=19683. Here is a sample code written by Python that runs 10000 games in training.